ResNet18_trainable_swish_CIFAR10_torch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06279483-490c-4aaf-a791-4ae81c28ca35",
   "metadata": {},
   "source": [
    "# ResNet-18: CIFAR-10 + PyTorch\n",
    "\n",
    "Trained with:\n",
    "- linear lr warmup, followed by step-decays.\n",
    "- __Trainable $\\beta$ hyper-parameter__ in Swish activation function.\n",
    "\n",
    "[SEARCHING FOR ACTIVATION FUNCTIONS](https://arxiv.org/pdf/1710.05941v2.pdf) by Prajit Ramachandran, Barret Zoph and Quoc V. Le; ICLR 2018 paper.\n",
    "\n",
    "Swish is an activation function: $f(x) = \\frac{1}{1 + e^{-\\beta \\cdot x}}$ where $\\beta$ is a learnable parameter. Nearly all implementations do not use the learnable parameter $\\beta$, in which case the activation function is $f(x) = \\frac{1}{1 + e^{-x}}$ (\"Swish-1\") - [paperswithcode, swish](https://paperswithcode.com/method/swish)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecab793c-af51-4da0-bfda-80b4c1abe0b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "# Specify GPU to be used-\n",
    "%env CUDA_DEVICE_ORDER = PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0244bcb4-c207-4bdb-911b-71d637e0f746",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm import trange\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os, pickle\n",
    "\n",
    "from cifar10_dataloader import get_cifar10_data\n",
    "from ResNet18_trainable_swish_torch import ResNet_Block, ResNet18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb00f4e-9793-409e-9ddb-a429ced233be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae4da8f-ee7a-4f89-83ec-8cf3413998a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37ebf097-fdd1-45ef-9e45-19433805535f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 2.0.0\n",
      "Number of GPU(s) available = 1\n",
      "Current GPU: 0\n",
      "Current GPU name: NVIDIA GeForce RTX 3080 Laptop GPU\n",
      "Available device is cuda\n"
     ]
    }
   ],
   "source": [
    "print(f\"torch version: {torch.__version__}\")\n",
    "\n",
    "\n",
    "# Check if there are multiple devices (i.e., GPU cards)-\n",
    "print(f\"Number of GPU(s) available = {torch.cuda.device_count()}\")\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    print(f\"Current GPU: {torch.cuda.current_device()}\")\n",
    "    print(f\"Current GPU name: {torch.cuda.get_device_name(torch.cuda.current_device())}\")\n",
    "else:\n",
    "    print(\"PyTorch does not have access to GPU\")\n",
    "\n",
    "# Device configuration-\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Available device is {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead145c1-18e5-4577-ae6b-e73647a084e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00477b3d-3ee2-456b-a29a-83d1ab8cb663",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify hyper-parameters\n",
    "batch_size = 256\n",
    "num_classes = 10\n",
    "num_epochs = 85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bc6fd94-d736-4477-9d4d-711e4c336533",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd037808-e83c-4fdf-9e69-2e0cd3bdcc8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "train_loader, val_loader, train_dataset, val_dataset = get_cifar10_data(\n",
    "    path_to_files = 'C:/Users/arjun/Downloads/data/',\n",
    "    batch_size = batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79fde03c-42b0-4081-b38d-e799bf8f92b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecd0ec21-f6a6-442f-8b0d-2ecc6afb4d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize ResNet-50 CNN architecture-\n",
    "model = ResNet18(beta = 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef891fa7-1858-438c-9132-cb28bbe60fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Move to device-\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5709cf5-40fc-49d6-8b72-2720dd6bde70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f4ae74b-ee16-4f13-b698-682d7c3e77a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save randomly initialize parameters-\n",
    "torch.save(model.state_dict(), \"ResNet18_random_params.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f60a9b-e739-437d-a10a-158f6f5c3ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.load_state_dict(torch.load(\"ResNet50_best_model.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90376e58-2799-408d-83cc-a5c99c486f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'beta' in model.state_dict().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efcc0aff-d2e8-45a9-8b16-3c0983b28ff7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f851aea-3267-4c03-9b73-0feacdbdce66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_trainable_params(model):\n",
    "    # Count number of layer-wise parameters and total parameters-\n",
    "    tot_params = 0\n",
    "    for param in model.parameters():\n",
    "        # print(f\"layer.shape = {param.shape} has {param.nelement()} parameters\")\n",
    "        tot_params += param.nelement()\n",
    "\n",
    "    return tot_params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06c028e2-8577-4840-a52f-a5d8abdb6684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet-18 has 11168833 params\n"
     ]
    }
   ],
   "source": [
    "print(f\"ResNet-18 has {count_trainable_params(model = model)} params\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0c4679a-24e2-4d53-b6b9-d50ba7762454",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b625ab-850a-44e8-9936-f42f21eba1db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "438eaaca-70b0-44cb-8166-32ee819625b0",
   "metadata": {},
   "source": [
    "### Learning Rate Scheduler\n",
    "\n",
    "Training dataset size = 50K, batch-size = 256, number of training steps per epoch = 50K / 256 = 195.3125\n",
    "\n",
    "After an initial LR warmup of 13 epochs or, 2539.0625 = 2540 training iterations:\n",
    "\n",
    "- For the next 32 epochs, or, until $45^{th}$ epoch, use lr = 01.\n",
    "\n",
    "- For the next 25 epochs, or, until $70^{th}$ epoch, use lr = 0.01.\n",
    "\n",
    "- For remaninig epochs, use lr = 0.001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c6c491b-9716-4596-b8d0-e1961411354c",
   "metadata": {},
   "outputs": [],
   "source": [
    "boundaries = [8790, 13672]\n",
    "values = [0.1, 0.01, 0.001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8dcb973-9f9d-46b6-a478-e5c9fdb6c5ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8a036f1-c038-4391-b9a1-76cbf088f312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define loss function and optimizer-\n",
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = torch.optim.SGD(\n",
    "    params = model.parameters(), lr = 0.0,\n",
    "    momentum = 0.9, weight_decay = 5e-4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8c08209-5847-44d0-883f-9828c9034817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "optimizer.param_groups[0]['lr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f081c2-f5e4-45fc-b16d-709f76fe0e99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4afd3495-b397-4121-b9ac-9daf213e3027",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decay_function(step, boundaries = [8790, 13672], values = [0.1, 0.01, 0.001]):\n",
    "    \n",
    "    for idx, bound in enumerate(boundaries):\n",
    "        if step < bound:\n",
    "            return values[idx]\n",
    "\n",
    "    return values[-1]\n",
    "\n",
    "\n",
    "class schedule():\n",
    "    def __init__(self, initial_learning_rate = 0.1, warmup_steps = 1000, decay_func = None):\n",
    "        self.initial_learning_rate = initial_learning_rate\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.decay_func = decay_func\n",
    "        self.warmup_step_size = initial_learning_rate/warmup_steps\n",
    "        self.current_lr = 0\n",
    "\n",
    "    def get_lr(self, step):\n",
    "        if step == 0:\n",
    "            return self.current_lr\n",
    "        elif step <= self.warmup_steps:\n",
    "            self.current_lr+=self.warmup_step_size\n",
    "            return self.current_lr\n",
    "        elif step > self.warmup_steps:\n",
    "            if self.decay_func:\n",
    "                return self.decay_func(step)\n",
    "        else:\n",
    "            return self.current_lr\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13e55cc2-b632-4e11-8eea-bbaa571fb476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial linear LR warmup: 195.3125 x 13 = 2540 steps in 13 epochs.\n",
    "custom_lr_scheduler = schedule(\n",
    "    initial_learning_rate = 0.1, warmup_steps = 2540,\n",
    "    decay_func = decay_function\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ce220f-696c-492d-a740-fd34d836fbcc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d784855-5bd6-4c9a-a22d-790a37979e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a37f4f47-57b3-4c0d-aa6d-9b19211cd00a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model_progress(model, train_loader, train_dataset):\n",
    "    '''\n",
    "    Function to perform one epoch of training by using 'train_loader'.\n",
    "    Returns loss and number of correct predictions for this epoch.\n",
    "    '''\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0.0\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    with tqdm(train_loader, unit = 'batch') as tepoch:\n",
    "        for images, labels in tepoch:\n",
    "            tepoch.set_description(f\"Training: \")\n",
    "            \n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            \n",
    "            # Get model predictions-\n",
    "            outputs = model(images)\n",
    "            \n",
    "            # Compute loss-\n",
    "            J = loss(outputs, labels)\n",
    "            \n",
    "            # Empty accumulated gradients-\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # Perform backprop-\n",
    "            J.backward()\n",
    "            \n",
    "            # Update parameters-\n",
    "            optimizer.step()\n",
    "            \n",
    "            global step\n",
    "            optimizer.param_groups[0]['lr'] = custom_lr_scheduler.get_lr(step)\n",
    "            step += 1\n",
    "            \n",
    "            # Compute model's performance statistics-\n",
    "            running_loss += J.item() * images.size(0)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            running_corrects += torch.sum(predicted == labels.data)\n",
    "            \n",
    "            tepoch.set_postfix(\n",
    "                loss = running_loss / len(train_dataset),\n",
    "                accuracy = (running_corrects.double().cpu().numpy() / len(train_dataset)) * 100\n",
    "            )\n",
    "            \n",
    "    \n",
    "    train_loss = running_loss / len(train_dataset)\n",
    "    train_acc = (running_corrects.double() / len(train_dataset)) * 100\n",
    "\n",
    "    # return running_loss, running_corrects\n",
    "    return train_loss, train_acc.cpu().numpy()\n",
    "\n",
    "\n",
    "def test_model_progress(model, test_loader, test_dataset):\n",
    "    total = 0.0\n",
    "    correct = 0.0\n",
    "    running_loss_val = 0.0\n",
    "    \n",
    "    # Set model to evaluation mode-\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        with tqdm(test_loader, unit = 'batch') as tepoch:\n",
    "            for images, labels in tepoch:\n",
    "                tepoch.set_description(f\"Validation: \")\n",
    "                \n",
    "                images = images.to(device)\n",
    "                labels = labels.to(device)\n",
    "            \n",
    "                # Predict using trained model-\n",
    "                outputs = model(images)\n",
    "                _, y_pred = torch.max(outputs, 1)\n",
    "                \n",
    "                # Compute validation loss-\n",
    "                J_val = loss(outputs, labels)\n",
    "                \n",
    "                running_loss_val += J_val.item() * labels.size(0)\n",
    "    \n",
    "                # Total number of labels-\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total number of correct predictions-\n",
    "                correct += (y_pred == labels).sum()\n",
    "                \n",
    "                tepoch.set_postfix(\n",
    "                    val_loss = running_loss_val / len(test_dataset),\n",
    "                    val_acc = 100 * (correct.cpu().numpy() / total)\n",
    "                )\n",
    "            \n",
    "        \n",
    "    # return (running_loss_val, correct, total)\n",
    "    val_loss = running_loss_val / len(test_dataset)\n",
    "    val_acc = (correct / total) * 100\n",
    "\n",
    "    return val_loss, val_acc.cpu().numpy()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af6cdbf-dbbe-465c-a45a-25994f0d92cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "437f68df-3034-4a9d-8412-1893cd0e1873",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python3 dict to contain training metrics-\n",
    "training_history_lr_scheduler = {}\n",
    "\n",
    "# Initialize parameters saving 'best' models-\n",
    "best_val_acc = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82d22978-f139-40bf-893a-7f1a166353e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06e5139f-a10f-4f6d-b424-40dd80d8678b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████| 196/196 [00:52<00:00,  3.76batch/s, accuracy=22.3, loss=4.25]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.16batch/s, val_acc=37.5, val_loss=2.07]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 1 training loss = 4.2471, training accuracy = 22.26%, val_loss = 2.0669, val_accuracy = 37.53% &  beta = 1.886302 & LR = 0.00768\n",
      "\n",
      "Saving model with highest val_acc = 37.5300%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.10batch/s, accuracy=40.7, loss=1.8]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.41batch/s, val_acc=47.5, val_loss=1.47]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 2 training loss = 1.7980, training accuracy = 40.74%, val_loss = 1.4717, val_accuracy = 47.50% &  beta = 2.044027 & LR = 0.01539\n",
      "\n",
      "Saving model with highest val_acc = 47.5000%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.10batch/s, accuracy=47.9, loss=1.5]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 18.81batch/s, val_acc=51.8, val_loss=1.38]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 3 training loss = 1.5028, training accuracy = 47.87%, val_loss = 1.3760, val_accuracy = 51.82% &  beta = 2.081630 & LR = 0.02311\n",
      "\n",
      "Saving model with highest val_acc = 51.8200%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████| 196/196 [00:48<00:00,  4.04batch/s, accuracy=53.1, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.55batch/s, val_acc=57.7, val_loss=1.23]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 4 training loss = 1.3438, training accuracy = 53.10%, val_loss = 1.2282, val_accuracy = 57.66% &  beta = 2.095978 & LR = 0.03083\n",
      "\n",
      "Saving model with highest val_acc = 57.6600%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 196/196 [00:47<00:00,  4.10batch/s, accuracy=58, loss=1.2]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.67batch/s, val_acc=62.5, val_loss=1.08]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 5 training loss = 1.1993, training accuracy = 58.02%, val_loss = 1.0775, val_accuracy = 62.52% &  beta = 2.055020 & LR = 0.03854\n",
      "\n",
      "Saving model with highest val_acc = 62.5200%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=62.2, loss=1.09]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.73batch/s, val_acc=68.1, val_loss=0.936]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 6 training loss = 1.0850, training accuracy = 62.24%, val_loss = 0.9363, val_accuracy = 68.11% &  beta = 2.147521 & LR = 0.04626\n",
      "\n",
      "Saving model with highest val_acc = 68.1100%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=65.4, loss=0.998]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.71batch/s, val_acc=72, val_loss=0.818]\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 7 training loss = 0.9985, training accuracy = 65.37%, val_loss = 0.8183, val_accuracy = 72.03% &  beta = 2.235134 & LR = 0.05398\n",
      "\n",
      "Saving model with highest val_acc = 72.0300%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=67.3, loss=0.942]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.77batch/s, val_acc=71.2, val_loss=0.834]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 8 training loss = 0.9420, training accuracy = 67.26%, val_loss = 0.8344, val_accuracy = 71.24% &  beta = 2.299520 & LR = 0.06169\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=69.4, loss=0.88]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.76batch/s, val_acc=62.1, val_loss=1.21]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 9 training loss = 0.8796, training accuracy = 69.42%, val_loss = 1.2104, val_accuracy = 62.12% &  beta = 2.523668 & LR = 0.06941\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=71.2, loss=0.836]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.71batch/s, val_acc=69.7, val_loss=0.882]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 10 training loss = 0.8359, training accuracy = 71.25%, val_loss = 0.8819, val_accuracy = 69.69% &  beta = 2.675123 & LR = 0.07713\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=72.4, loss=0.802]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.75batch/s, val_acc=75.1, val_loss=0.728]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 11 training loss = 0.8021, training accuracy = 72.39%, val_loss = 0.7279, val_accuracy = 75.14% &  beta = 2.873386 & LR = 0.08484\n",
      "\n",
      "Saving model with highest val_acc = 75.1400%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=73.2, loss=0.777]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.78batch/s, val_acc=75.1, val_loss=0.741]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 12 training loss = 0.7767, training accuracy = 73.19%, val_loss = 0.7412, val_accuracy = 75.12% &  beta = 3.043522 & LR = 0.09256\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=74.2, loss=0.748]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.74batch/s, val_acc=71.7, val_loss=0.835]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 13 training loss = 0.7476, training accuracy = 74.16%, val_loss = 0.8354, val_accuracy = 71.69% &  beta = 3.369721 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=75.2, loss=0.717]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.71batch/s, val_acc=79.7, val_loss=0.602]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 14 training loss = 0.7168, training accuracy = 75.20%, val_loss = 0.6021, val_accuracy = 79.70% &  beta = 3.658306 & LR = 0.10000\n",
      "\n",
      "Saving model with highest val_acc = 79.7000%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=76.1, loss=0.697]\n",
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    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 15 training loss = 0.6966, training accuracy = 76.05%, val_loss = 0.6406, val_accuracy = 78.35% &  beta = 4.019728 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=77.1, loss=0.668]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.74batch/s, val_acc=74.4, val_loss=0.767]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 16 training loss = 0.6680, training accuracy = 77.11%, val_loss = 0.7670, val_accuracy = 74.36% &  beta = 4.294059 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=77.4, loss=0.648]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.78batch/s, val_acc=82.2, val_loss=0.541]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 17 training loss = 0.6475, training accuracy = 77.43%, val_loss = 0.5414, val_accuracy = 82.16% &  beta = 4.610133 & LR = 0.10000\n",
      "\n",
      "Saving model with highest val_acc = 82.1600%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:49<00:00,  3.99batch/s, accuracy=78.4, loss=0.63]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 18 training loss = 0.6296, training accuracy = 78.38%, val_loss = 0.5649, val_accuracy = 80.27% &  beta = 4.911165 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=78.8, loss=0.618]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 19 training loss = 0.6183, training accuracy = 78.76%, val_loss = 0.5663, val_accuracy = 81.02% &  beta = 5.193012 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=79.2, loss=0.606]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 20 training loss = 0.6061, training accuracy = 79.24%, val_loss = 0.8319, val_accuracy = 72.21% &  beta = 5.481262 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=79.4, loss=0.601]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.78batch/s, val_acc=80.1, val_loss=0.594]\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 21 training loss = 0.6007, training accuracy = 79.44%, val_loss = 0.5944, val_accuracy = 80.08% &  beta = 5.733926 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=79.7, loss=0.588]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.77batch/s, val_acc=83.4, val_loss=0.511]\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 22 training loss = 0.5879, training accuracy = 79.72%, val_loss = 0.5110, val_accuracy = 83.39% &  beta = 5.951094 & LR = 0.10000\n",
      "\n",
      "Saving model with highest val_acc = 83.3900%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=80.1, loss=0.583]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 23 training loss = 0.5832, training accuracy = 80.10%, val_loss = 0.4927, val_accuracy = 83.37% &  beta = 6.076081 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=80.4, loss=0.572]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 24 training loss = 0.5715, training accuracy = 80.39%, val_loss = 0.5475, val_accuracy = 81.98% &  beta = 6.217688 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=80.5, loss=0.565]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.57batch/s, val_acc=73.5, val_loss=0.859]\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 25 training loss = 0.5653, training accuracy = 80.52%, val_loss = 0.8594, val_accuracy = 73.48% &  beta = 6.308719 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=81.1, loss=0.553]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.58batch/s, val_acc=84.2, val_loss=0.47]\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 26 training loss = 0.5534, training accuracy = 81.12%, val_loss = 0.4703, val_accuracy = 84.25% &  beta = 6.451999 & LR = 0.10000\n",
      "\n",
      "Saving model with highest val_acc = 84.2500%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=81.4, loss=0.542]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 27 training loss = 0.5422, training accuracy = 81.40%, val_loss = 0.5786, val_accuracy = 81.13% &  beta = 6.526549 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=81.3, loss=0.547]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 28 training loss = 0.5467, training accuracy = 81.31%, val_loss = 0.5115, val_accuracy = 82.73% &  beta = 6.561112 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=81.6, loss=0.541]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 29 training loss = 0.5406, training accuracy = 81.64%, val_loss = 0.5274, val_accuracy = 82.32% &  beta = 6.590678 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=81.5, loss=0.536]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 30 training loss = 0.5360, training accuracy = 81.49%, val_loss = 0.5270, val_accuracy = 82.85% &  beta = 6.627245 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 31 training loss = 0.5307, training accuracy = 81.70%, val_loss = 0.5679, val_accuracy = 81.62% &  beta = 6.661466 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 32 training loss = 0.5281, training accuracy = 81.88%, val_loss = 0.5737, val_accuracy = 81.88% &  beta = 6.647497 & LR = 0.10000\n",
      "\n"
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    },
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=82.3, loss=0.52]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.64batch/s, val_acc=82.2, val_loss=0.543]\n"
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     "text": [
      "\n",
      "epoch: 33 training loss = 0.5197, training accuracy = 82.26%, val_loss = 0.5431, val_accuracy = 82.23% &  beta = 6.642533 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 34 training loss = 0.5185, training accuracy = 82.38%, val_loss = 0.7081, val_accuracy = 77.33% &  beta = 6.626249 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.70batch/s, val_acc=82.2, val_loss=0.537]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 35 training loss = 0.5190, training accuracy = 82.20%, val_loss = 0.5372, val_accuracy = 82.15% &  beta = 6.683588 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=82.9, loss=0.506]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.65batch/s, val_acc=85.8, val_loss=0.439]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 36 training loss = 0.5062, training accuracy = 82.90%, val_loss = 0.4387, val_accuracy = 85.78% &  beta = 6.662245 & LR = 0.10000\n",
      "\n",
      "Saving model with highest val_acc = 85.7800%\n",
      "\n"
     ]
    },
    {
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=82.9, loss=0.503]\n",
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     "text": [
      "\n",
      "epoch: 37 training loss = 0.5030, training accuracy = 82.94%, val_loss = 0.5069, val_accuracy = 83.36% &  beta = 6.667841 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 38 training loss = 0.4992, training accuracy = 82.89%, val_loss = 0.4388, val_accuracy = 85.49% &  beta = 6.660444 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 39 training loss = 0.4964, training accuracy = 83.09%, val_loss = 0.5148, val_accuracy = 82.88% &  beta = 6.648847 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 40 training loss = 0.4951, training accuracy = 83.11%, val_loss = 0.4369, val_accuracy = 85.31% &  beta = 6.615694 & LR = 0.10000\n",
      "\n"
     ]
    },
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=83.2, loss=0.49]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 41 training loss = 0.4897, training accuracy = 83.23%, val_loss = 0.4999, val_accuracy = 83.84% &  beta = 6.564793 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 42 training loss = 0.4903, training accuracy = 83.17%, val_loss = 0.4494, val_accuracy = 85.12% &  beta = 6.569628 & LR = 0.10000\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 43 training loss = 0.4813, training accuracy = 83.41%, val_loss = 0.5486, val_accuracy = 81.68% &  beta = 6.586953 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=83.4, loss=0.485]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.66batch/s, val_acc=82.2, val_loss=0.527]\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 44 training loss = 0.4847, training accuracy = 83.41%, val_loss = 0.5274, val_accuracy = 82.18% &  beta = 6.581092 & LR = 0.10000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=84, loss=0.467]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.44batch/s, val_acc=89.3, val_loss=0.314]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 45 training loss = 0.4670, training accuracy = 83.96%, val_loss = 0.3142, val_accuracy = 89.27% &  beta = 6.555890 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 89.2700%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=89.1, loss=0.322]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 46 training loss = 0.3217, training accuracy = 89.11%, val_loss = 0.2646, val_accuracy = 91.10% &  beta = 6.553314 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 91.1000%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=90.7, loss=0.278]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 47 training loss = 0.2777, training accuracy = 90.72%, val_loss = 0.2506, val_accuracy = 91.48% &  beta = 6.543442 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 91.4800%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=91.2, loss=0.26]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 48 training loss = 0.2599, training accuracy = 91.21%, val_loss = 0.2499, val_accuracy = 91.48% &  beta = 6.531926 & LR = 0.01000\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=91.8, loss=0.242]\n",
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     "text": [
      "\n",
      "epoch: 49 training loss = 0.2416, training accuracy = 91.77%, val_loss = 0.2406, val_accuracy = 91.89% &  beta = 6.521212 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 91.8900%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=92.3, loss=0.23]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 50 training loss = 0.2301, training accuracy = 92.25%, val_loss = 0.2408, val_accuracy = 91.74% &  beta = 6.521156 & LR = 0.01000\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=92.3, loss=0.223]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.60batch/s, val_acc=92.1, val_loss=0.237]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 51 training loss = 0.2230, training accuracy = 92.34%, val_loss = 0.2372, val_accuracy = 92.07% &  beta = 6.512432 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 92.0700%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=93, loss=0.211]\n",
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     "text": [
      "\n",
      "epoch: 52 training loss = 0.2113, training accuracy = 93.02%, val_loss = 0.2396, val_accuracy = 91.93% &  beta = 6.505198 & LR = 0.01000\n",
      "\n"
     ]
    },
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=92.8, loss=0.211]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 53 training loss = 0.2109, training accuracy = 92.85%, val_loss = 0.2431, val_accuracy = 92.07% &  beta = 6.501472 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=93.4, loss=0.197]\n",
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     "text": [
      "\n",
      "epoch: 54 training loss = 0.1967, training accuracy = 93.42%, val_loss = 0.2404, val_accuracy = 92.15% &  beta = 6.496717 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 92.1500%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=93.5, loss=0.194]\n",
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     "text": [
      "\n",
      "epoch: 55 training loss = 0.1943, training accuracy = 93.51%, val_loss = 0.2390, val_accuracy = 92.17% &  beta = 6.494266 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 92.1700%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "\n",
      "epoch: 56 training loss = 0.1913, training accuracy = 93.71%, val_loss = 0.2409, val_accuracy = 92.24% &  beta = 6.489770 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 92.2400%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "\n",
      "epoch: 57 training loss = 0.1797, training accuracy = 94.10%, val_loss = 0.2536, val_accuracy = 92.03% &  beta = 6.491140 & LR = 0.01000\n",
      "\n"
     ]
    },
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=94.2, loss=0.175]\n",
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     "text": [
      "\n",
      "epoch: 58 training loss = 0.1746, training accuracy = 94.18%, val_loss = 0.2369, val_accuracy = 92.45% &  beta = 6.496712 & LR = 0.01000\n",
      "\n",
      "Saving model with highest val_acc = 92.4500%\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 59 training loss = 0.1728, training accuracy = 94.24%, val_loss = 0.2481, val_accuracy = 91.86% &  beta = 6.505071 & LR = 0.01000\n",
      "\n"
     ]
    },
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      "\n",
      "epoch: 60 training loss = 0.1704, training accuracy = 94.20%, val_loss = 0.2484, val_accuracy = 92.12% &  beta = 6.517155 & LR = 0.01000\n",
      "\n"
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    },
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=94.5, loss=0.167]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.51batch/s, val_acc=92.2, val_loss=0.241]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 61 training loss = 0.1674, training accuracy = 94.48%, val_loss = 0.2409, val_accuracy = 92.18% &  beta = 6.516994 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=94.6, loss=0.164]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.62batch/s, val_acc=92.2, val_loss=0.245]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 62 training loss = 0.1641, training accuracy = 94.60%, val_loss = 0.2454, val_accuracy = 92.21% &  beta = 6.520722 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=94.8, loss=0.155]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.57batch/s, val_acc=92.1, val_loss=0.252]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 63 training loss = 0.1551, training accuracy = 94.84%, val_loss = 0.2520, val_accuracy = 92.10% &  beta = 6.528920 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=95, loss=0.154]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.61batch/s, val_acc=92.3, val_loss=0.246]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 64 training loss = 0.1542, training accuracy = 94.98%, val_loss = 0.2459, val_accuracy = 92.27% &  beta = 6.540968 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=94.9, loss=0.157]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.65batch/s, val_acc=92, val_loss=0.254]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 65 training loss = 0.1566, training accuracy = 94.87%, val_loss = 0.2536, val_accuracy = 92.04% &  beta = 6.548135 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=95.3, loss=0.148]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.57batch/s, val_acc=92.3, val_loss=0.258]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 66 training loss = 0.1485, training accuracy = 95.27%, val_loss = 0.2578, val_accuracy = 92.26% &  beta = 6.559021 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=95.2, loss=0.145]\n",
      "Validation: : 100%|████████████████████████████████████| 40/40 [00:02<00:00, 19.64batch/s, val_acc=91.5, val_loss=0.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 67 training loss = 0.1455, training accuracy = 95.20%, val_loss = 0.2701, val_accuracy = 91.52% &  beta = 6.566317 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=95.4, loss=0.144]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.60batch/s, val_acc=92, val_loss=0.258]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 68 training loss = 0.1437, training accuracy = 95.38%, val_loss = 0.2575, val_accuracy = 91.95% &  beta = 6.574825 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=95.4, loss=0.139]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.60batch/s, val_acc=92.2, val_loss=0.249]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 69 training loss = 0.1389, training accuracy = 95.42%, val_loss = 0.2493, val_accuracy = 92.22% &  beta = 6.583127 & LR = 0.01000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=95.4, loss=0.14]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.60batch/s, val_acc=92.5, val_loss=0.241]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 70 training loss = 0.1396, training accuracy = 95.44%, val_loss = 0.2413, val_accuracy = 92.48% &  beta = 6.593835 & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 92.4800%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=96.1, loss=0.121]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.61batch/s, val_acc=93, val_loss=0.233]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 71 training loss = 0.1205, training accuracy = 96.08%, val_loss = 0.2326, val_accuracy = 93.05% &  beta = 6.594110 & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.0500%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=96.4, loss=0.112]\n",
      "Validation: : 100%|██████████████████████████████████████| 40/40 [00:02<00:00, 19.54batch/s, val_acc=93, val_loss=0.23]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 72 training loss = 0.1120, training accuracy = 96.44%, val_loss = 0.2301, val_accuracy = 92.98% &  beta = 6.595648 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=96.6, loss=0.111]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.63batch/s, val_acc=93.1, val_loss=0.232]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 73 training loss = 0.1109, training accuracy = 96.61%, val_loss = 0.2324, val_accuracy = 93.07% &  beta = 6.595726 & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.0700%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=96.8, loss=0.105]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.56batch/s, val_acc=93, val_loss=0.231]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 74 training loss = 0.1054, training accuracy = 96.79%, val_loss = 0.2308, val_accuracy = 93.04% &  beta = 6.594266 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=96.7, loss=0.104]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.61batch/s, val_acc=93, val_loss=0.233]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 75 training loss = 0.1041, training accuracy = 96.74%, val_loss = 0.2328, val_accuracy = 93.05% &  beta = 6.592986 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.13batch/s, accuracy=96.8, loss=0.103]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.66batch/s, val_acc=93, val_loss=0.231]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 76 training loss = 0.1027, training accuracy = 96.78%, val_loss = 0.2309, val_accuracy = 93.04% &  beta = 6.592631 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████| 196/196 [00:47<00:00,  4.12batch/s, accuracy=96.9, loss=0.102]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.60batch/s, val_acc=93, val_loss=0.231]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 77 training loss = 0.1023, training accuracy = 96.90%, val_loss = 0.2314, val_accuracy = 93.03% &  beta = 6.591558 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=96.9, loss=0.0988]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.68batch/s, val_acc=93, val_loss=0.233]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 78 training loss = 0.0988, training accuracy = 96.92%, val_loss = 0.2329, val_accuracy = 92.96% &  beta = 6.590920 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=96.9, loss=0.1]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.63batch/s, val_acc=93, val_loss=0.232]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 79 training loss = 0.1003, training accuracy = 96.93%, val_loss = 0.2322, val_accuracy = 93.04% &  beta = 6.589186 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=96.9, loss=0.0983]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.69batch/s, val_acc=93, val_loss=0.234]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 80 training loss = 0.0983, training accuracy = 96.94%, val_loss = 0.2341, val_accuracy = 93.00% &  beta = 6.588081 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=97.1, loss=0.0947]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.62batch/s, val_acc=93.2, val_loss=0.231]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 81 training loss = 0.0947, training accuracy = 97.10%, val_loss = 0.2306, val_accuracy = 93.19% &  beta = 6.587454 & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.1900%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=97.1, loss=0.0944]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.65batch/s, val_acc=93.1, val_loss=0.231]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 82 training loss = 0.0944, training accuracy = 97.13%, val_loss = 0.2311, val_accuracy = 93.08% &  beta = 6.586453 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.15batch/s, accuracy=97.1, loss=0.0927]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.62batch/s, val_acc=93, val_loss=0.232]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 83 training loss = 0.0927, training accuracy = 97.10%, val_loss = 0.2318, val_accuracy = 93.01% &  beta = 6.585247 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.14batch/s, accuracy=97.1, loss=0.0924]\n",
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 19.45batch/s, val_acc=93.2, val_loss=0.234]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 84 training loss = 0.0924, training accuracy = 97.11%, val_loss = 0.2336, val_accuracy = 93.18% &  beta = 6.584311 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 196/196 [00:47<00:00,  4.12batch/s, accuracy=97.2, loss=0.0931]\n",
      "Validation: : 100%|█████████████████████████████████████| 40/40 [00:02<00:00, 19.40batch/s, val_acc=93, val_loss=0.235]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 85 training loss = 0.0931, training accuracy = 97.19%, val_loss = 0.2350, val_accuracy = 92.99% &  beta = 6.581717 & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(num_epochs):\n",
    "\n",
    "    # Train and validate model for 1 epoch-\n",
    "    train_loss, train_acc = train_model_progress(\n",
    "        model, train_loader,\n",
    "        train_dataset\n",
    "    )\n",
    "    val_loss, val_acc = test_model_progress(\n",
    "        model, val_loader,\n",
    "        val_dataset\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nepoch: {epoch + 1} training loss = {train_loss:.4f}, \"\n",
    "          f\"training accuracy = {train_acc:.2f}%, val_loss = {val_loss:.4f}\"\n",
    "          f\", val_accuracy = {val_acc:.2f}% & \"\n",
    "          f\" beta = {model.beta.cpu().detach().numpy():.6f} & LR = {optimizer.param_groups[0]['lr']:.5f}\\n\"\n",
    "         )\n",
    "    \n",
    "    training_history_lr_scheduler[epoch + 1] = {\n",
    "        'loss': train_loss, 'acc': train_acc,\n",
    "        'val_loss': val_loss, 'val_acc': val_acc,\n",
    "        'lr': optimizer.param_groups[0]['lr'],\n",
    "        'beta': model.beta.cpu().detach().numpy()\n",
    "    }\n",
    "\n",
    "    \n",
    "    # Save best weights achieved until now-\n",
    "    if (val_acc > best_val_acc):\n",
    "        # update 'best_val_loss' variable to lowest loss encountered so far-\n",
    "        best_val_acc = val_acc\n",
    "\n",
    "        print(f\"Saving model with highest val_acc = {val_acc:.4f}%\\n\")\n",
    "        \n",
    "        # Save trained model with 'best' validation accuracy-\n",
    "        torch.save(model.state_dict(), \"ResNet18_swish_best_model.pth\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f63f3626-1571-4b1b-826e-61bcb6f6b3f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52ff8b3f-3dc1-4207-9da0-311f0e3d7bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training metrics as Python3 history for later analysis-\n",
    "with open(\"ResNet18_trainable_Swish_training_history.pkl\", \"wb\") as file:\n",
    "    pickle.dump(training_history_lr_scheduler, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d29a6ac-e953-4d20-8947-e217db0da002",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9239d8a-0e80-4e17-978a-b7d7400fab31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LSkrirbfe4u2336ZXr174+PiQlJRU7/fdsmULW7ZsAcyH6qKiIt577z0ABg0axKBBgwDzbfWFF17IhAkTuPXWW4mIiGDVqlXMnz+fQYMGcd5557mv+corr3Ddddfx4osvtlhdwdFcQ2HOmjWLSy+9lAMHDvDPf/6TmJiYVp2Ruinuv/9+Jk+ezLnnnsstt9yCw+HgX//6FwEBAWRlZdV7rquXYM6cOYwaNarG/vz8fL755htee+01brnlFp555hmmTp3KySefzK233kpCQgL79+9n8eLFvP766wA89thjnHLKKYwePZq77rqLPn36kJaWxieffMLzzz9PYGAgkyZNIiwsjOuvv577778fT09PXn75ZQ4cONDo7z1hwgS8vb258sormTNnDiUlJfz73/8mOzu72nEBAQE8+uij/P73v+fss8/mD3/4A1FRUezatYsNGzawcOHCasffcsstXH755VgsFnfqnoh0Qu1c6CwiUo1rNJU1a9bU2FdcXGwkJCQYffv2NcrLyw3DMIwNGzYY06ZNM7p162Z4eXkZ0dHRxplnnmk899xz7vPuuusuY+TIkUZoaKhhs9mMXr16Gbfeeqtx5MgR9zFVRx862tixYw2g2uhDhmEYZWVlxiOPPGIMGzbM8PHxMQICAowBAwYYN954o7Fz5073cXv37jXOOeccIzAw0ADqHUno6PbU9vnHP/5R7dhvv/3WOOecc4zo6GjD19fX6Nevn3HbbbdV+35Vf7cvvfRSg/evijpGH/rXv/5V6/EPPfSQ0aNHD8NmsxkDBw40/vvf/9Y6ek5dow+9++671Y5z3a9qu+safahqO+u6j2EYxocffmgkJSUZ3t7eRkJCgvHQQw8ZN998sxEaGlrn78FutxvdunUzTjjhhDqPKS8vN7p3724kJSW5t61cudI477zzjODgYMNmsxm9e/c2br311mrnbdmyxbjsssuM8PBwd5tmzpxplJSUuI/5+eefjbFjxxr+/v5GXFyc8Y9//MP43//+V+voQ0f/rbp8+umn7r/XuLg444477jC+/PJLAzCWLVtW7dgvvvjCGD9+vOHv72/4+fkZgwYNMh5++OEa1ywtLTVsNpsxceLEOn8vItLxWQyjgb5BERGR41xZWRknnHACcXFx1eYQkIZ9+umnnH/++Xz++edMmjSpvZsjIs2koEBERLqc66+/ngkTJhATE0NqairPPfccy5cvZ8mSJZx99tnt3bxOYcuWLezbt49bbrkFf39/fv311ybNCC0iHYtqCkREpMvJz8/n9ttvJyMjAy8vL0aMGMEXX3yhgKAJZs2axY8//siIESNYtGiRAgKRTk49BSIiIiIiXZyGJBURERER6eIUFIiIiIiIdHEKCkREREREujgVGgNOp5PDhw8TGBioQikREREROS4YhkF+fj6xsbF4eNTfF6CgADh8+DDx8fHt3QwRERERkRZ34MABunfvXu8xCgqAwMBAwPyFBQUFtXNrRERERESOXV5eHvHx8e5n3fq0a1Dw/fff869//YtffvmFlJQUPvzwQy688EL3fsMwuO+++/jPf/5DdnY2o0eP5plnnmHw4MHuY0pLS7n99tt58803KS4u5qyzzuLZZ59tMBqqypUyFBQUpKBARERERI4rjUmPb9dC48LCQoYNG8bChQtr3b9gwQIee+wxFi5cyJo1a4iOjmbChAnk5+e7j5k9ezYffvghb731Fj/88AMFBQVMmTIFh8PRVl9DRERERKRT6zCTl1kslmo9BYZhEBsby+zZs7nzzjsBs1cgKiqKhx9+mBtvvJHc3FwiIyN59dVXufzyy4HK+oAvvviCc889t1H3zsvLIzg4mNzcXPUUiIiIiMhxoSnPuB12SNLk5GRSU1M555xz3NtsNhvjx4/np59+AuCXX36hrKys2jGxsbEMGTLEfUxtSktLycvLq/YREREREemqOmyhcWpqKgBRUVHVtkdFRbFv3z73Md7e3oSGhtY4xnV+bebPn899993XpPYYhkF5ebnSkrowq9WKp6enhq0VERGR406HDQpcjn4AMwyjwYeyho65++67+ctf/uL+2VWZXRe73U5KSgpFRUWNbLUcr/z8/IiJicHb27u9myIiIiLSYjpsUBAdHQ2YvQExMTHu7enp6e7eg+joaOx2O9nZ2dV6C9LT0xk7dmyd17bZbNhstka1w+l0kpycjNVqJTY2Fm9vb70p7oIMw8But5ORkUFycjJ9+/ZtcBIQERERkc6iwwYFPXv2JDo6mqVLlzJ8+HDAfGO/fPlyHn74YQBOPPFEvLy8WLp0KdOmTQMgJSWFTZs2sWDBghZph91ux+l0Eh8fj5+fX4tcUzonX19fvLy82LdvH3a7HR8fn/ZukoiIiEiLaNegoKCggF27drl/Tk5OZv369YSFhZGQkMDs2bOZN28effv2pW/fvsybNw8/Pz+mT58OQHBwMNdffz233XYb4eHhhIWFcfvtt5OUlMTZZ5/dom3VW2EB/R2IiIjI8aldg4K1a9dyxhlnuH925fnPmDGDl19+mTlz5lBcXMysWbPck5ctWbKk2qxsjz/+OJ6enkybNs09ednLL7+M1Wpt8+8jIiIiItIZdZh5CtpTfWO4lpSUkJycTM+ePZUuIvp7EBERkU7juJinQDqWHj168MQTTzT6+O+++w6LxUJOTk6rtUlEREREWkaHLTSWY3P66adzwgknNOlBvj5r1qzB39+/0cePHTuWlJQUgoODW+T+IiIiItJ6FBR0YYZh4HA48PRs+M8gMjKySdf29vZ2DysrIiIiIh2b0oeayDAMiuzl7fJpbPnHzJkzWb58OU8++SQWiwWLxcLevXvdKT2LFy9m5MiR2Gw2VqxYwe7du7nggguIiooiICCAk046ia+//rraNY9OH7JYLPzvf//joosuws/Pj759+/LJJ5+49x+dPvTyyy8TEhLC4sWLGThwIAEBAUycOJGUlBT3OeXl5dx8882EhIQQHh7OnXfeyYwZM7jwwgvr/K6ZmZlceeWVdO/eHT8/P5KSknjzzTerHeN0Onn44Yfp06cPNpuNhIQEHnzwQff+gwcPcsUVVxAWFoa/vz8jR45k9erVjfpdi4iIiBwP1FPQRMVlDgb9fXG73HvL/efi593wP7Inn3ySHTt2MGTIEO6//37AfNO/d+9eAObMmcMjjzxCr169CAkJ4eDBg0yaNIkHHngAHx8fFi1axNSpU9m+fTsJCQl13ue+++5jwYIF/Otf/+Lpp5/mqquuYt++fYSFhdV6fFFREY888givvvoqHh4eXH311dx+++28/vrrADz88MO8/vrrvPTSSwwcOJAnn3ySjz76qNoIVUcrKSnhxBNP5M477yQoKIjPP/+c3/3ud/Tq1YvRo0cD5gzW//3vf3n88cc55ZRTSElJYdu2bYA5LO748eOJi4vjk08+ITo6ml9//RWn09ng71lERETkeKGg4DgUHByMt7c3fn5+tabw3H///UyYMMH9c3h4OMOGDXP//MADD/Dhhx/yySef8Kc//anO+8ycOZMrr7wSgHnz5vH000/z888/M3HixFqPLysr47nnnqN3794A/OlPf3IHLQBPP/00d999NxdddBEACxcu5Isvvqj3u8bFxXH77be7f/7zn//MV199xbvvvsvo0aPJz8/nySefZOHChcyYMQOA3r17c8oppwDwxhtvkJGRwZo1a9zBTJ8+feq9p4iIiMjxRkFBE/l6Wdly/7ntdu+WMHLkyGo/FxYWct999/HZZ59x+PBhysvLKS4uZv/+/fVeZ+jQoe51f39/AgMDSU9Pr/N4Pz8/d0AAEBMT4z4+NzeXtLQ0Ro0a5d5vtVo58cQT631r73A4eOihh3j77bc5dOgQpaWllJaWuouit27dSmlpKWeddVat569fv57hw4fX2bshIiIi0hUoKGgii8XSqBSejuzoUYTuuOMOFi9ezCOPPEKfPn3w9fXl0ksvxW6313sdLy+vaj9bLJZ6H+BrO/7oOgmLxVLt54bqKB599FEef/xxnnjiCZKSkvD392f27Nnutvv6+tZ7fkP7RURERLoCFRofp7y9vXE4HI06dsWKFcycOZOLLrqIpKQkoqOj3fUHbSU4OJioqCh+/vln9zaHw8G6devqPW/FihVccMEFXH311QwbNoxevXqxc+dO9/6+ffvi6+vLN998U+v5Q4cOZf369WRlZbXMFxERERHphBQUHKd69OjB6tWr2bt3L0eOHKn3DX6fPn344IMPWL9+PRs2bGD69OntUmj75z//mfnz5/Pxxx+zfft2brnlFrKzs2v0HlTVp08fli5dyk8//cTWrVu58cYbSU1Nde/38fHhzjvvZM6cObzyyivs3r2bVatW8cILLwBw5ZVXEh0dzYUXXsiPP/7Inj17eP/991m5cmWrf18RERGRjqJz58FInW6//XZmzJjBoEGDKC4uJjk5uc5jH3/8ca677jrGjh1LREQEd955J3l5eW3YWtOdd95Jamoq11xzDVarlRtuuIFzzz0Xq7XuWop7772X5ORkzj33XPz8/Ljhhhu48MILyc3NrXaMp6cnf//73zl8+DAxMTH88Y9/BMwelSVLlnDbbbcxadIkysvLGTRoEM8880yrf18RERFpfYZhkFFQyuGcEors5ZSWOSktd1By1NJpgLfVA5uXB95WD7w9PbB5mkvvimcRAwOnYV7TqLi2YUBpuZPc4jJyisrIKbaTW1RW5ecyhnUP5qFLhtbf0HZmMRo7+P1xLC8vj+DgYHJzcwkKCqq2r6SkhOTkZHr27ImPj087tbBrcjqdDBw4kGnTpvHPf/6zvZsD6O9BRESkpRiGQbnToNxhUO50ViwNnBUP2k6jcr3qz+YHHE4Dh9Pc7zDM9axCO/uzijhQ8dmfVcSB7CJKytp3qPFRPcJ4549j2vy+9T3jHk09BdJh7Nu3jyVLljB+/HhKS0tZuHAhycnJTJ8+vb2bJiIi0mXkFNlJPlKIh8WCv82Kv80TP29P/L2teFprZp4bhkFpuZNiu4OScgfFdgdHCuyk5BaTlldCSm5J5TK3hMxCO+UVD/RtxcMC0UE++Ns8sXl54ONprbHEAvZyJ/ZyJ6UVS7vD6d7myma2WCx4WMBiAQ+LBQvg7elBsK83wb5ehPh5EVKxDPL1IsTPm6ggW5t91+ZSUCAdhoeHBy+//DK33347hmEwZMgQvv76awYOHNjeTRMRETmuGIbBkQI7O9Pz2ZVewM60AnOZXsCRgtI6z7N5euBv88TLaqGkzElJmYPS8pZ9C2/1cD10Vywxlx4WC1jM/VaLBYvFgtWDKusWgn29SAjzIz7Mj/gwXxLC/EgI8yM2xBevWgIaqaSgQDqM+Ph4fvzxx/ZuhoiIyHHBMAwyC+3syywk+UgR+zIL2ZtZxN4jhezNLCS/pLzOc6ODfLB6WCgoLaewtJzyirf6peVOSsvrHrLcy2rBx9NKWIA30UE+RAdXfIJ8iAn2ISrIh4gAG96eHnh6WPD08MDTaj7Qe1k9sHrUPbiItC4FBSIiIiLHibySMpZtS2fJljRW7Mggr54Hf4sFEsP86NMtgD7dAunbLYC+UQH0jgzA31b9EbG03EFRqYOC0nKK7A7KHE58vDzw8bJWfjw9ak0vks5BQYGIiIhIJ5aWV8LSLWks3pzKqj2ZlDkqc/UtFogN9qVHhB+J4f70CPejR7g/PSL8SQjzw8er7hH+qrJ5WrF5Wgn1926tryHtTEGBiIiISCdjL3fy1pr9fPDrIdYfyKm2r3ekP+cOjubsQVEMiglq9IO/dG0KCkREREQ6CcMw+HJTKg9/tY19mUXu7SfEh3Du4GgmDIqiT7eAdmyhdFYKCkREREQ6gV/3Z/Pg51v5ZV82AJGBNmad3ptJSTFEBWnuHDk2CgpEREREOrADWUU89NU2Pt+YAoCvl5UbTuvFDaf1qlEQLNJc+kuSOvXo0YPZs2cze/bs9m6KiIhIp1XucFJS7qS0zOFelpZXjvFf5nCak3lVzOjrcJoz/DqcBpsP5/Hqyn3YHebkWZed2J2/TOhPdLB6BqRlKSgQERERqUd6Xgnb0/Lx8/YkyMeTQB8vAn088fO2YqmY5tbpNDicW8yejEJ2ZxSwJ6OQPUcK2J1eSGpeyTG34ZQ+EdwzaSCDYoOO+VoitVFQICIiInKU7EI7X21O5ZP1h1mVnIlh1DzG6mEhwOZJgM2TzMJSSsoantnX29MDm6c5vr+tYt3b04qnhzmBl3tptWD18MDPy8rlo+I5vV+kOwARaQ0KCprKMKCsqOHjWoOXnzngcAOef/557r//fg4cOICHR+UkIueffz6hoaEsWrSI3bt385e//IVVq1ZRWFjIwIEDmT9/PmeffXajm7NmzRruuece1q1bR1lZGSeccAKPP/44I0aMcB+Tk5PDnDlz+Pjjj8nNzaVPnz489NBDTJkyBYAff/yRe+65hzVr1mCz2Rg1ahRvvfUWoaGhTfjFiIiI1K6wtJzDOcUczCmm2O4gzN+byEAbEQE2gnw8qz1oF5aWs3RLGp9sOMz3OzLcs/gC9Ir0p8zhJL+knPySchwVaT65xWXkFpcB5my+ieH+9Irwp3e3APcyMcwPf5sn3lYPPDRjr3RQCgqaqqwI5sW2z73vOQze/g0edtlll3HzzTezbNkyzjrrLACys7NZvHgxn376KQAFBQVMmjSJBx54AB8fHxYtWsTUqVPZvn07CQkJjWpOfn4+M2bM4KmnngLg0UcfZdKkSezcuZPAwECcTifnnXce+fn5vPbaa/Tu3ZstW7ZgtZrjJa9fv56zzjqL6667jqeeegpPT0+WLVuGw+Fozm9HRES6qILScjYdymXToVwOZhdzKKeYQ9nFHM4tJqeorM7zvK0eRAR4ExFoI8Dmya/7s6u97R8YE8TUYTFMHRpLfJife7thGBSXOSoChDLySsoJ9fMmPtRXM/pKp6Wg4DgUFhbGxIkTeeONN9xBwbvvvktYWJj752HDhjFs2DD3OQ888AAffvghn3zyCX/6058adZ8zzzyz2s/PP/88oaGhLF++nClTpvD111/z888/s3XrVvr16wdAr1693McvWLCAkSNH8uyzz7q3DR48uHlfWkREuoSSMgdbU/LYeDCXDQdz2Hgwl90ZBbWm97gE+XgSG+JbkeZj50h+Kfml5dgdTg7nlnA4tzLnv0e4H+cPi2XqsFj6RgXWej2LxYKftyd+3p4aClSOGwoKmsrLz3xj3173bqSrrrqKG264gWeffRabzcbrr7/OFVdc4X5LX1hYyH333cdnn33G4cOHKS8vp7i4mP379zf6Hunp6fz973/n22+/JS0tDYfDQVFRkfsa69evp3v37u6A4Gjr16/nsssua/T9RETk+GYYBgeyiknJLSYtv5T0vBLS8kpIzy81l3ml7M8qqpbW4xIb7ENS92B6RgQQF+JDXKgvsSG+xIX4EujjVeP4kjIHGfmlHCko5UiBnexCOwNjghgSF6TcfemSFBQ0lcXSqBSe9jZ16lScTieff/45J510EitWrOCxxx5z77/jjjtYvHgxjzzyCH369MHX15dLL70Uu93e6HvMnDmTjIwMnnjiCRITE7HZbIwZM8Z9DV9f33rPb2i/iIgc3wzDYFd6Aav2ZLJqTxar9mSSWdjw/4fC/L0Z2j2Yod1DGFaxjAy0NenePl5W4sP8qqUFiXRlCgqOU76+vlx88cW8/vrr7Nq1i379+nHiiSe6969YsYKZM2dy0UUXAWaNwd69e5t0jxUrVvDss88yadIkAA4cOMCRI0fc+4cOHcrBgwfZsWNHrb0FQ4cO5ZtvvuG+++5rxjcUEZG2Vu5wkltcRk5xGQUl5RTayykqdZhLu4PCUnNZUubA29MDXy8rPl5WfL2s2LzMEXd8vKzsyyxk1Z5MVu/JqhEEeHt6EBfiS2SgjaggH6ICbXQLMte7BfoQH2a+/dfbfJGWpaDgOHbVVVcxdepUNm/ezNVXX11tX58+ffjggw+YOnUqFouFe++9F6ez4aHUjr7Gq6++ysiRI8nLy+OOO+6o9vZ//PjxnHbaaVxyySU89thj9OnTh23btmGxWJg4cSJ33303SUlJzJo1iz/+8Y94e3uzbNkyLrvsMiIiIlrkdyAiIk2TllfCF7+lsHZvNtlFdjMIKDJH2CkoLW/x+/l4eTAyMYyTe4Vxcq9whnYPwdtTxboibU1BwXHszDPPJCwsjO3btzN9+vRq+x5//HGuu+46xo4dS0REBHfeeSd5eXlNuv6LL77IDTfcwPDhw0lISGDevHncfvvt1Y55//33uf3227nyyispLCx0D0kK0K9fP5YsWcI999zDqFGj8PX1ZfTo0Vx55ZXH9sVFRKRJjhSU8uVvKXy6MYU1e7PqLdoFCPTxJMjHCz9vK342T/y9rfh5e+JvM5c2Tw/sjooZe8ucFJeZvQclZQ6Ky5xEBHgzuqeCAJGOxGIYDf2rf/zLy8sjODiY3NxcgoKqzxRYUlJCcnIyPXv2xMdHIwx0dfp7EJHjRVahncWbU/ls42FW7s6kau3uiIQQzhkcTUywD8G+XoT4eZtLXy+CfL2waqx9kU6hvmfco6mnQERE5DhW5nCyJ6OQbal5bEvNZ3vF51BOcbXjhnUPZsrQWCYNjSEuRANBiHQ1CgpEREQ6qdTcEg7lFJNdaCer0E5WUcWy0Bxi81BOMbszCihz1J4UMCgmiCnDYpiSFEtCuEbhEenKFBSIiIh0MhsP5vDUN7v4emtao44PtHnSPzqQ/tGBDIgOZEBMEP2iAgn2rTl+v4h0TQoKREREOolf92fz9Dc7WbY9AzCnzuke6kuYv40wPy9C/b0J9/d2LyMDbfSLCtQQntI12YvgyA7I2A4Z2yBnH/gEQ0A0BHSDwIplQBT4dwNP78Zd1zCg8Ahk7oKs3eayrAQi+kLkAOg2EPzCWve7tQIFBY2kemwB/R2ISMval1nI+78cxN/mSa/IAHpF+pMQ5oeXtfpoPGv3ZvHkNztZsdOcC8bDAhcOj+OmM/rQOzKgPZou7e3ILlj5NGQlg0+Q+bBrCzaXrp+9/aE0H4oyq3yyKtfLSsDqBZ42c2m1VV8HMBxgOM2P02E+EBtOwACLR82Ph7X27VU/Vm+wBVZvq08w2ILMnx3lUJxtfkpyKteLs6G0wGyfly94+tRcFmZUCQL2m+1srNp+h7aKpZcv5B0yA4DM3VDawIiN/pFmgBDZ31xGJ0HCyc38h902FBQ0wMvL7FotKirSDLxCUVERUPl3ISLSHEX2cp5Ztov/fp+M3VF9jhirh4WEMD96RvjTK8KfLSl5/LQ7073v4opgoEeEf3s0Xdpbxnb4/hHY9F7Fw7nUyy+i8uE8tIcZJBWkVX7y06AwHZzlUJJrfnIbc2ELhMRDWG8I72MGU0d2VAYjhRnmZ+8K8/C4kfCHb1rxix47BQUNsFqthISEkJ6eDoCfn5+6YLsgwzAoKioiPT2dkJAQrFZrezdJRDohwzD4dGMK8z7fSmpeCQBjeoUTHuBN8pFCko8UUmR3uNe/rTjP08PCZSO7M+v0PsSHqSC4S0rfCt//CzZ9gPvtd7+JMOgCsBeaD7OleRUPtnnmemmB+UbeL7ziE1ZlPRy8fMBRBuWl5tJRWn0dS91v/qGyB8HVe+DqVXA6zDa6exicVY51gsNe0da8o9pd0XarJ/iGVn58QirXbQFm+8qKobykclleYvZ8+ARVvp2PHAD+jZgM1emE4iyzF6U0z+ydOLpt9kIIjIHwiiAgtKf5+6uNvdAMENK3mUFCxnYzpaiDU1DQCNHR0QDuwEC6rpCQEPffg4hIU2w5nMfcTzbz894swKwFuHfKIM4ZFOV+2WQYBml5pezJKGD3kUL2ZBTg42XlqtEJdA9VMNAlpW6C7xfAlo8rtw2YAqfdAbEntFuzjiseHmbw0JgAojG8/SF2uPnpRBQUNILFYiEmJoZu3bpRVlbW3s2RduLl5aUeAhFpktJyB+l5pfzn+z28vnofTgN8vDyYdXofbjitFz5e1f+bYrFYiA72ITrYh7F9WugBpSUVZ8PmD2HrZ2aR5im3msWV0jKcTjiyHQ6shgNrzGXmzsr9A883g4GYoe3XRjluKShoAqvVqodCERFxMwyD9Qdy+GpzKhn5pdXmC8guLKOgtLza8ZOHxnDPpIFtMzmYYZhpGu40iCrpGSW5Zm51QBREDTYf7K111EqV22HnEtj4FuxYbF7TZcObMPRyGD8Hwnode5vL7WAvMO9RXmouj163eleOGONdT12F02HmdmftNgtDM3eZKSZRSeYb9qgh4N1A70tBBqRugJSNZs75yOta7m0ymOk9h9bC/tVw8GczECg9OqHdAoMvhNPmQNSglru3yFEshoZTadIU0CIiIiVlDj5Zf5hXVu1l06G6RyHxpJwR1t0MDXMy5bRRnDAkycyPbqg2zV4IOQcg9wDkp9Y+Cktxjrmt3A7OMvOh1VFuLp3l5rbGsnqbedhRSWaQED3E3Pbbe7D5A/N+LlFDYMglcHANbP/C3GaxwvCrzLfYIQk1r28Y5oP5gdXmeXmHzKCktMDM2bYXmOuO0sa3GcA7wAwOAqLMQMEvHPJTzHtlJ1cPYI5msZrfOeYEM0iITjILQ1M2QupGc1mQWv0c3zCYON8MhJpTX5ifCvtXVXxWQupvZh5+VV5+EHcixI+G+FHQ/aROObyldAxNecZVUICCAhERaUBRFpQVsc8exGs/H+SdtQfJLTYfur09PZicFEP/6EDC/LyJ9sgmIWslkanL8TvwPRZ7fvVreQeao5YEx5vLwGjz+jn7zSAg54BZ9NiSbEcNr+gTZD5Q5x6EtM1wdBuPFhANQy+DoVeYAYPLoV9g2TzY9bX5s4cXjLgGxtxkPpwfWA0HfjY/TflOFo+K4TG9zeDEvW6D8mIoSIeyooavY7WZhaFhvcziUKsXpGyAw+vNEWcaboh5XsxQs1g0bZO5ufeZMOVxczSb+rh6WbZ9bgYB2ck1jwlOgITRlUFAt8Fmoa1IC1BQ0EQKCkREpIaSPNj2GcbGdyB5ORbDid2wctCI5KARSZZ3DNGJAxg8ZCiBQWGw9wfYuRTSfqt+Hd8w8+E/96A5Nnxj2YIrgoYY801x1RFYfEPBN8Tc5uUDHp7VP1avimXFePAe9aS+Op2Qu98saE3bbLY/dZP5EN9vIgy7AnqOr/8a+1fDsgcheXndx1htEDfCfPMd0c9sl+vjHVCxHmCu15XKVFVpvhkc5KdWDi9ZlGn2GrhGiAnqbhaRHs0wzKDl8HpIWW8u0zabqUHRSRAzDKKHmr0mtop5IBxl8NNT8N3DZo+Glx+c+TcY/cfqvxvDMK+5/k347d2jgiGLGVQljDHHrI8/GYLjGv6uIs2koKCJFBSIiAhgvtndtRQ2voOx4yss5SXuXWWGFS+Lo56TXSzmw2+fCdB3gjkCieuh0V5oBgc5B8wH8ZyK9CC/MAhJrN6D4BPcOt+xNe39Ab59EPb/BIGx5pvv+Iq34NFJjZ8xtiM7sgs+vQX2/WD+HDsCzn/aDCg2vmPWWaRvqTze1cvS63QzIOqM/1yl01JQ0EQKCkREjnO5B2H7l2aaTl3yDsKWT8w8/Qq7nTF85BjHEuupnHrSCGYMthFvSYecfZC9F7L3meuFGWYeeJ8J0Oesli1G7YzsRQ0X8XZmTiesewWW/N0sDLZYcY/LD2avyMApMGy6GQwoHUjaSVOecfVXKiIix6fsfbD1E3N894NrGn3aEUsoH5aN4SPHOPZ792Hm+J68Oa4nYf6ut9z9gVNbpcnHjeM5IAAzJenEmdD3XPjidtj2mbk9fjQMuxIGX2Smd4l0IgoKRETk+JG1x3zbv+UjOLyuyg6Lmccd2b/GqDF2h5M9GYVsSC3h46IkVjkHEeRn4/pxPblmbA+CfRuR3y5dU1AMXPG6WY/g6WPWMoh0UgoKRETk+PD1XPjh8cqfLR6QOA4GXQADp5qj/FSxK72AV1bu5f1fDlJoN2sFIgK8ufPUXlx1ciIBNv0vUhopanB7t0DkmOm/eCIi0vnt+6kyIOg53pzsacAUc+z6KpxOg2Xb03n5p72s2HnEvb1PtwBmjEnk0hPj8fXWJJUi0vUoKBARkc6tvBQ+nW2uj7jGHAnmKIZh8Prq/fzn+z3szzLHt7dY4KwBUcwc24NxfcKxNGcyKhGR44SCAhER6dx+eAKObAf/bjDh/loPeX31fv72kTnxVJCPJ1eMSuB3JycSH3acF8SKiDRSLTN6dCz5+fnMnj2bxMREfH19GTt2LGvWVI4iYRgGc+fOJTY2Fl9fX04//XQ2b97cji0WEZE2c2QnrHjEXJ8435zU6yg70/L552fmuPGzTu/N6nvO5p5JAxUQiIhU0eGDgt///vcsXbqUV199ld9++41zzjmHs88+m0OHDgGwYMECHnvsMRYuXMiaNWuIjo5mwoQJ5Oc3MGW7iIh0boZhpg057Ob8AEMuqXFISZmDP7+5jtJyJ6f1i+T2c/qrZkBEpBYdOigoLi7m/fffZ8GCBZx22mn06dOHuXPn0rNnT/79739jGAZPPPEEf/3rX7n44osZMmQIixYtoqioiDfeeKO9my8iIq1p3WvmrLJefjD50RpDjQI8/NU2tqXmE+7vzSOXDcXDQ3UDIiK16dBBQXl5OQ6HAx8fn2rbfX19+eGHH0hOTiY1NZVzzjnHvc9mszF+/Hh++umnOq9bWlpKXl5etY+IiHQiBRmw5G/m+hn3QGhijUOWbUvnpR/3AvDIZcPoFuhT4xgRETF16KAgMDCQMWPG8M9//pPDhw/jcDh47bXXWL16NSkpKaSmpgIQFRVV7byoqCj3vtrMnz+f4OBg9yc+Pr5Vv4eIiLSwxXdDSQ5ED4XR/1djd0Z+KXe8twGAmWN7cMaAbjWOERGRSh06KAB49dVXMQyDuLg4bDYbTz31FNOnT8dqrcwJPXoYOcMw6h1a7u677yY3N9f9OXDgQKu1X0REWtiur+G3d83JyaY+CdbqA+k5nQa3v7uBIwV2BkQHctd5A9qpoSIinUeHDwp69+7N8uXLKSgo4MCBA/z888+UlZXRs2dPoqPN2SmP7hVIT0+v0XtQlc1mIygoqNpHREQ6AXsRfPYXc330HyFuRI1DXvppL8t3ZGDz9OCpK4fj46XCYhGRhnT4oMDF39+fmJgYsrOzWbx4MRdccIE7MFi6dKn7OLvdzvLlyxk7dmw7tlZERFrF8ocgZx8EdYcz/lpj9+bDuTz85TYA/jZlEP2iAtu6hSIinVKHn7xs8eLFGIZB//792bVrF3fccQf9+/fn2muvxWKxMHv2bObNm0ffvn3p27cv8+bNw8/Pj+nTp7d300VEpCVl7oafFprrkx8BW0C13cV2Bze/uQ67w8nZA6O4enRCOzRSRKRz6vBBQW5uLnfffTcHDx4kLCyMSy65hAcffBAvLy8A5syZQ3FxMbNmzSI7O5vRo0ezZMkSAgP1dkhE5Liy9wcwHJAwFvqfV2P3Y0u3szujkG6BNhZcOrTe2jIREanOYhiG0d6NaG95eXkEBweTm5ur+gIRkY5q8V9h5UJztKHzHqqxe9xD33Iop5h/XzWC85Ji2qGBIiIdS1OecTtNTYGIiHRxGWatAJH9a+xKyyvhUE4xHhY4pW9EGzdMRKTzU1AgIiKdQ8Z2cxlZc4jRX/dlA9AvKpBAH6+2bJWIyHFBQYGIiHR8pQWQWzGnTC09Bb/uN4OCExND27JVIiLHDQUFIiLS8R3ZYS79I8EvrMbuXyp6CkYkKCgQEWkOBQUiItLx1ZM6VFruYNOhPEA9BSIizaWgQEREOr4jFUFBRL8auzYfzsPucBLm701iuF8bN0xE5PigoEBERDq+RhQZj0gI0dwEIiLNpKBAREQ6vnqGI3UVGY9Q6pCISLMpKBARkY6trASy95rrRwUFhmGoyFhEpAUoKBARkY4tcxcYTvAJhoCoarsO55aQlleK1cPCsO4h7dM+EZHjgIICERHp2NypQwPgqJoBVz3BoJggfL2tbd0yEZHjhoICERHp2FxzFNQy8pArdUhDkYqIHBsFBSIi0rFV7Sk4yrqKIuPhCSFt2CARkeOPggIREenY6hiOtKTMwebD5qRlKjIWETk2CgpERKTjcpRB5m5z/aiRhzYezKXcadAt0Eb3UN92aJyIyPFDQYGIiHRcWcngLAMvfwjuXm1X1aFINWmZiMixUVAgIiIdl7ueoF/NkYf2q8hYRKSlKCgQEZGO60jt9QSGYbiLjEckhrRxo0REjj8KCkREpONyFRkfNRzp/qwijhTY8bZ6MDg2uB0aJiJyfFFQICLSkeWngb2ovVvRfuoYjtSVOjQ4LggfL01aJiJyrBQUiIh0VEd2wZND4dWLwDDauzVtz+mAIzvN9aNGHnJPWqahSEVEWoSCAhGRjuq3d6G8BA6sgp1Lm35+xg7IPdTy7WorOfvN72+1QWiPart+3ZcDwAgVGYuItAgFBSIiHdW2zyrXVzzStN6CtC3w77Hwv7OgrLjp9y4rbv+0JXc9QV/wqEwRKigtZ1uqJi0TEWlJCgpERDqirD2QtgksVvNN+YHVsO+nxp//9VxzfP/8FNj4dtPubS+EZ0bDwpHt29Pgric4atKyAzk4DYgL8SU62KcdGiYicvxRUCAi0hFtregl6HEKDL/KXF/xaOPO3fsD7Fxc+fOqfzetl+HXVyBnH+QdgneugfLSxp/rKIOPZsGrF0NZSePPq82RHeYyonpQ4CoyHp4QcmzXFxERNwUFIiIdkSt1aOBUGHeL2WOw+xs4vK7+8wwDlv7dXE+aBt6B5hv33d807r7ldvjxKXPd4gGH1sJXdzXuXMOAT26G9a+b99u5pHHn1aWOngJ3kbHqCUREWoyCAhGRjiY/1UwXAhgw2SyyTbrM/HnFY/Wfu+VjOPQLePnDOQ/AiN+Z21c+07h7b3wL8g9DQDRMexWwwNoXYd3rDZ/79VzY8Eblz5s/bNw9a2MYZqE0VBuO1Ok0WHcgB1A9gYhIS1JQICLS0Wz73FzGjYSgWHP9lFvN5dZPKwtwj+Yog2/uN9fH/gkCo2D0jeYb/93fQvrW+u/rdMAPT1SeP3AKnH63+fNnt8Lh9XWfu+rf8GPFuaP/aC53fNX8YuW8w2DPN3tIwnq5N+85UkhOURk+Xh4Mig1q3rVFRKQGBQUiIh3N1k/N5cApldu6DYABUwCj8sH9aL+8DFm7wT8Sxv7Z3Bbaw+xtAFj1bP333fKxeb5PCJx4rbnttDug30RwlMLbv4OirJrn/fZeZYrRmffCxIcgJBHKipqfQuRKHQrvDZ7e7s2ueoKhcSF4WfW/MBGRlqL/oopI23KUmbP0Su2Ks2HvCnN9wNTq+079i7nc+DZk76u+rzQflj9sro+/E2yBlftOvslcbngbCo/Ufl/DqExNOvn/wBZgrnt4wEXPQ2hPyN0P719v9ii47F4GH1b0DIy6AU69DSwWGHyhua25KUSu3pCj6gl+ragnGJ4Y0rzriohIrRQUiEjbWnIvPDYA9ixv75Z0TDsWg7McIgdCRJ/q++JOhF5ngOGAn56uvm/lM1CYYT68j5hRfV/CyRA7wnzbv/bF2u+762tI+82sRRh1Q/V9viFw+Wvg6WumIS2bZ24/vB7evtoc+nTQhWYPgcVi7ht8UeX3sRc28ZcAHHHNUVD7yEOayVhEpGUpKBCRtrV/JRhO+Pk/7d2SjsmdOjS19v2n3mYuf32lsselIL1yxKCz/l4t3QYwH9THVPQW/Pzf2ocYdQ13OvJa8AuruT96CJxfEYiseMSsIXj9UrAXQI9T4eL/VJtgjJgTzNSl8mIzMGgqd09BZZFxbnEZO9MLAM1kLCLS0hQUiEjbyjtsLncshsLM9m1LR2Mvgl0VQ4dWrSeoqscp0H2U+dZ/VcWIQssfhrJCszfA9Yb+aIMugKA4KEw3awCq2veTGaxZvWHMn+pu39DLKouIv7rL7JmISoIrXgdPW/VjLZbKtjQ1hcgwqg1Hai938vaa/Vz4zI8YBiSG+xERYKv/GiIi0iQKCkSk7ZTbzQdJMFNONr3fvu3paHZ/Y75ZD0mA6KG1H2OxwGm3m+trXoCDv5gFxgAT7q9M3zma1QtG/cFcX/Vs9cnMXLUEJ0yHoJj623jOA5AwxlwPSYCr3wOf4NqPdQUFO5dAaUH9162q8AgUZ2NgYdEOL8b/axl3vv8byUcKCfHz4u9TBjX+WiIi0igKCkSk7RSkAlUeRquOaS+VsxgPmFr3wz1A33MgaoiZuvPqhWYNQp8J0PPU+q9/4kzw8oO0TZD8vbktZQPsWmoOWzrulobbaPWCK94w6weu/QoCo+s+NnqoOZxoeYk5PGkjFRzaBMBBuvGPL/eQkltCVJCNv00eyI93nslZA6MafS0REWkcz/ZugIi0k51LzQc1i9V80POwgocneHiZSy9fGHJJw2+OmyIvxVz6hkFpnjk7b/o2c7jNrs5RBju+NNfrqidwsVjMkYjeu878PWKBs+c2fA/fULM3YM3/zN6CXuPhh8fNfYMvrjYfQL38wswRihriSiFa8SilG97nR69T2Xgwl60peZSUOTEAwzAwDDAwcDrN5eBDn3KvBbY7YkkI8+OP43tzyYlx2DytDd5SRESaR0GBSFdkL4R3Zph56PVZ/Rz84VsI6NYy982vqCeI7G8+oG7/wuwtmHB/y1y/M9u7AkpyzTkG4kc1fPygCyHsAcjaA8OuNAuBG2P0/5lpRzu+gu1fweaPzO2uydFqUVhaTmpeCWm5JaTmmZ/MAjs2Tw/8bZ4E+ngSYKvy8fEkt7iMjQdzyUruz72AsXMpf960gkJ8G2ziRM8D4AmJA0bw7RXj8dR8BCIirU5BgUhXtP1LMyAIjDXfHDvLq38cZbDnO8jZZw45OePTmoWkzeEqMg6MMd8gb/8CNr4DZ/2j+sg1XZFr1KH+kxr3u/CwwkX/gQ1vwhn3NOoWDqfBfmLwjzmdbinLKHvrd3hhsNFvDE8tLqHc+TPlDoMyh5Nyp0FucRlpuSXkl5YfwxfzZ7p3DL09UpgeuoXMHuczJC6YIF8vPCxmZ4IFi7m0WLAAp618GlKh76ATQQGBiEibUFAg0hW5CnxPuBLOurf2Y47sgv+dCQdWw6ez4cJn689zbwxXUBAUC/3ONXsL8lPMAKTPWcd27cZwOjpm8OF0wrYvzPWB51fZbLB8ZwYHs4ooKXNSUuagpNxBSZmT0nIHJWXelDt+h89Xh/HxSsPX24qvlxU/bys+XuYnI7+UHWn57EjLZ1d6AaXlTsZ4jOVN72V4GXYA5mafy69Z9U8oF2DzJCrIRnSwD1FBPkQG2LA7nBSUlFNQWuVT8bPN04Ok7iEMjQvGlnEx/PYMf03cDpef0PDvY+kecxmptDIRkbaioECkqynKMusJAIZcWvdxEX3gspfhtUvNFJ9uA2Hczcd276pBgafNrFlY8z/zbXdjggJHOaRuMItsG9tzYRjmkJs/PWVO0HX63ZWj93QUh9aaRdi2IOh5GgBbU/K496NNrK2Ywbel+Hh5kB95MocK+xBXuouU0JFcNOoiLrN6YPWw4GW14OnhgZfVQqCPF1FBPkQH+xBgO4b/XaROh9+eMf/uSvLAJ6juY4uzoaAiQIns1/x7iohIkygoEOlqtn5qDgfabTBENTC0Y+8zzVFmvrwDlv4dIvpB/4nNv3d+RaFxYEXx8rCKotetnzX8sGgY8P71sOUjcwjMQRdC0mWQOA48akkxcTpg2+fw45PmQ7fLt/8Eb//GFcq2la2fmMt+55JfbuHxL7ewaOVeHE4DP28rp/aNwNer8u2/zcsDH09z6eXhQWm5g+IyB8V2Z8Wy3FyWOQn29aJ/VAD9ogLpFxVIfJgfVg8L7FsI380j5tz5/C66R+t+v6jBEN4XMneatQxDp9V9bMYOcxkUB7bA1m2XiIi4KSgQ6Wo2VUxclXRJ444f9QdI3wK/vGQ+lF+/tOFgoi55h8xlUJy5jBthBhpHdsCWj2HE7+o+99dFZkAAZkHur4vMT1Cc2eMwdJrZg1BeAuvfgJULzSJcAKvNTJWyBcJPT5sTb9kCYfjVzfseLckw3EORrvEdx02PLic935xx+Lwh0dw7ZRCxIQ0X5zZZ4hizVqQtuEYh+n6BOZFZvUFB5aRlIiLSdhQUiHQl+amQvMJcH9LIoMBigUn/gsxd5gg5b14Of1gG/hFNu7dhmPeHymFOLRZz5Jxv7jNTiOoKCjK2w5d3metnzzVn7v3tHdjyiRlo/PSU+YkcaE6OVnTEPNYnxAxqRt1gjqBkGOZn5UL45M9mYDDogqZ9j1rkFNnZcjiPtPwSUnNLScsrIa1ilJ70vFIy8ksJ8/emdzd/ekcG0KdbgHvZrWgnluxk7Hgz4/sgiiilR7gf910whPH9Io+5bR2GKyjY9bUZ1NU14dmRip4C1ROIiLQpBQUirS1nv5nGMuIaM22lPW36ADCg+ygI7dH486xeMO0V+O+ZkJ0M71wDv/sIPL0bf42iTHDYAQsEVJnwaujl8M39sO9HyN5bs13lpfDe9eZMv73OgLG3mOlCvcbDpEdh52L47V3YsRgytprnBMfDmD+ZPQG2gMprWSzmjLwlubDuVfO60/2hz9mN/x4VsvKLWPfDl5Ru/pyB+T8SYVj5o/0f5BFQ6/GuoTx/3JVZbfsdtg+5yQLLHUk4PP34yxl9uOG0Xvh4dcCC6GPRbSBE9Icj283Rr4ZdUfOYoizYv9Jcj1A9gYhIW1JQINLavrnffGhN2wwXLGzftrhTh+opMK6LXxhMfxv+d7b5AP/5X+D8pxs/IpErdcg/snowERxnPuDv+Q42vA2n31n9vK/nQtpv4BcOFz1XvX7Ay8d80z/oAijOMQMDL1/of54ZyNTGYoGpT0JpvpmO9NbV8LsPzXSaBmRlZrBlxYdYdnzJ4MLVnGWpmOfBYn7+EJtMctREooJ9iA7yISrIRlSQDxEBNjIKStmVXsDujAJ2pxewO6OQfZmFnGqsBQvs73YmS6ePJyHcr3G/z87GlUK0/CEzhahqUJC22ZwTY+M7ZvoXQMzQ9mmniEgXpaBApLWlbTGX614131wnnNw+7cjaA4d+AYuH+XDWHJH94dIX4Y1p5vc58VrofmLjznXNZhwUW3PfsOkVQcGbMH5OZaCxc6k58y7ABc9CYHTNc118Q2DY5Y1ri4cVLv6vOYnbrqXm95n5GcQMcx+SVWhnz8EU8natxHpoDWFH1jKg9DdOsTjMAyyQawkipdupJNgK8dv/HX/ufQQmn1DrLePD/BiREFptW2lhNt6P7AMDrrtmJpbg4zQgcBl8oRkU7PqmoldgFaz+NyR/X3lM9FAYd4uZIiYiIm1GQYFIa3I6K4tdAT77C9z4PVjb4V8919wEPccf2wzFfSeYaTy7v4G0TU0IClxFxrUEBQOnwOcBZmrS/lXmW/v8NPjwj+b+UTc2atSjnCI7peVOIgNseHg00IPh6Q3TXqHslYvwOriK4hcv4LX4f1CaeZDovA0MdmxnhOUAHhaj8hwLHLB250jsmUSfdBExQ8YT7GE1ZwXe/11l6ksj2VJ+AcMJIYlYgrs36dxOqdtAs+4jYys8dYKZxgVmoDpwqjnbcsLJxz4fhoiINJmCApHWlH/YzIX38DSLWtM3w8/Pw5ib2rYdhgG/HUPq0NHCeplBQfbexp9z9HCkVXn7mylA6183ewviR8NHfzQLhrsNhgn313pJp9Pgt0O5LNueznfbM9hwMAfDAG9PD+JDfUkI8yM+zM+9jAiwsS+zkO2p+WxNzWdbSh7F+b/nDe9Uksr28oc9sysvXpGllG6NJjV4GKXRJxJ34mTiew8h/uiGJFSkHqVtNtOYfEMa9zvZVxFEJI5t3PHHg8EXwXdbK4qNQ+DEGXDSHyCkxm9VRETakIICkdaUuctchvaEsX+GT2+GZfPMMfaD49quHWmbzaEerd4wYMqxX89VDJyd3Phzqk5cVoviQdPwXf86pRveY3WGH6cd+BaHh401J/4L75RiwvwchPp54zQMVuw6wnfb0lm+I4PMQnu163hYwF7uZHdGIbszChvRMD/u8b+PZx33EVN2gNzQITi7jyKozxhsPcfQLTCaBvtVAqPMQClrDxxcY/amNMb+LhgUjJkFJTkQ0dcsMm/v4nsREQEUFIi0LldQEN4Hhv8O1r0GB3+GxffAtEVt1w5XgXHfcxr/Frs+YT3NZVbzg4IjBaWs3ZvFz8nZrNmbxdaUAr7ziqC74winHfg3AH8vvYrXP8wBfqrzsgE2T07tG8Hp/SMZ368b4QHepOSUcCC7iP1ZlZ8DWUWk55WSEObHgJhA+kcHMiA6iP7RgeZsvc7zwXAS3tzUroQxZlCw76fGBQXlpXCwYlK1hC4UFNgCYeL89m6FiIgcRUGBSGvK3G0uw3ubo+ZMeQyeP80c9WbX140bCrO8FDxtzW+DYcBvFfUELZE6BGbPBzStp6AifejjPfDk19+x58jRb/EtLPU6g2vL3wVgY8ApHAi9gqSiMrKL7GQX2im0m0W+/aMCOb1/JKf378aJiaF4e1af0Tgh3I+EcD/GNeU7eXjgzhlqjoQxZvrT/lWNO/7Qr+AoNUdjCu/d/PuKiIi0AAUFIq3J3VNQ8dAXnQSj/2iOqPPFHfB/K81hNWtTkAFfzjFn+r30heaPGHTgZ8jdD94B0K/hYt1GcaUPleSao8j4hdV7eEmZA4+sg3gDT60pYI9hBgT9owI5qWcoJ/UIY1TPMGIcg+DZT8EvgqF/fJVXjrpuabmD0nInQT51DDfanlx1BYd+aVwgt/+nyvNUWCsiIu1MQYFIa6qaPuRy+t3mOO1Ze+DHJ+D0u6qfYxjmeO1f3QnF2ea23d82PyhwpQ4NmGyO4d8SvP0gIAoK0sxi4zqCgjKHk/d+Ocj/vt7IN44CALxCu/PoWUM5a2A3QvyOnvysJ9y0GmxBtV7T5mnF5tlBJ/UK7w1+EWZx9OF1DQ89u68iKEhsUn+GiIhIqziGvnIRqZejDLL3metVgwKfIDh3nrm+4rHKFCOAnAPw+mXw4Q1mQGALNre7rtPkNpSbAQhA0mXNu0Zd6kkhcjoNPl5/iLMfW87dH/xmjsIE2D0D+PS2iVxyYvdaAgLXdXs02PPQIVkslYFAQ0OTOh1mDw40atI0ERGR1qaeApHWkr0PDAd4+dUchnPwRebkX7u/NdOIrnoPfnkRlv4D7AXmKEHj74T4UbBoatOG/qxq7/dQmAG+YdDr9GP9RtU4QnpgPbCKA7s380vZyRzKKSYlt5iUnBJ2pOdzIKsYgHB/b24bFgS/gndod7Aex+8iEsfCts8aritI2wSleWaPSNSQtmmbiIhIPTr0/53Ly8v529/+Rs+ePfH19aVXr17cf//9OJ1O9zGGYTB37lxiY2Px9fXl9NNPZ/Pmze3YapEKVesJjs4Zt1hg0iPmw//ub+DZk+Hz28yAIH40/PFHOO32yh6G3IPmW/+mcs1NMPhCsB5bHv6RglI+35jC3z/exDmPL+fJdWZ7flrzC7PfXs+/Fm/ntVX7+WZbOgeyign08eT2c/rx/ZwzmJRQ8e9sbXMUHE/cPQWrzInr6uJKHYofbc6uLCIi0s46dE/Bww8/zHPPPceiRYsYPHgwa9eu5dprryU4OJhbbrkFgAULFvDYY4/x8ssv069fPx544AEmTJjA9u3bCQwMbOdvIF1abfUEVYX3hlNuheUPw5Ht4OUPZ/8DTvp95YNiQDRYbeYoNXkHKwt8G6OsBLZ+aq43I3UoPb+E1XuyWLUnk9XJWexKL6i2f5CHOXp/P+8jjEkIJybEh9hgX/dyRGIowb4VgUgDcxQcN6KHmj1DJTnmvBBRg2o/zl1PoNQhERHpGDp0ULBy5UouuOACJk+eDECPHj148803WbvWHNvbMAyeeOIJ/vrXv3LxxRcDsGjRIqKionjjjTe48cYb263tIg0GBQCn/MWcWMziAec8AKGJ1fd7eEBIAmTuNFOImhIU7PraTFEJioP4BopeKxzKKebL31L44rcUft2fU2P/gOhATu4VzuieYYz2CoK3nmV4QA5v3tDA9btKUGD1gu4nQfJys66gtqDAMCprDrrS/AQiItKhdeig4JRTTuG5555jx44d9OvXjw0bNvDDDz/wxBNPAJCcnExqairnnHOO+xybzcb48eP56aef6gwKSktLKS0tdf+cl5fXqt9DuqjGBAVePnDF6/VfJ7RHRVDQxGLjgxWFrP0mVozBX7sDWUV88VsKX2xKZcOBnGr7BsUEMbpXGCf3CmdUjzBC/asUBxdU/Ocj71DDQ3BWzFFw3KcPgTnEaPJyM4XopOtr7s/cZdZ5WG0QN6Lt2yciIlKLDh0U3HnnneTm5jJgwACsVisOh4MHH3yQK6+8EoDU1FQAoqKiqp0XFRXFvn11P0DNnz+f++67r/UaLp2PYcCm9yHuxMrZeo+Va1ShsGOcmMrVe9DUYmPX/SP7V9tcUuZg/YEcVu3J5Jut6fx2KNe9z2KBUT3CmJQUw7mDo4kOrmMOBQD/CHPuA3uBGbBE9qv7WHdPQVzTvkNn1NAIRK7Uoe4jj21SOhERkRbUoYOCt99+m9dee4033niDwYMHs379embPnk1sbCwzZsxwH2c5qojTMIwa26q6++67+ctf/uL+OS8vj/j4+Jb/AtJ5JC+H9683x5n//dfHHhiUFriH4Tzm2WpdKUM5TewpqAgK7ME9+GV3ZkVtQCa/7s/BXl5ZBOthgZN7hXNeUgznDo6iW2A9gUBVFos5LGnab+awpI0KCrpAT0H3k8BihdwD5hCzIUf9t8WdOqR6AhER6Tg6dFBwxx13cNddd3HFFVcAkJSUxL59+5g/fz4zZswgOjoaMHsMYmIqHzbS09Nr9B5UZbPZsNn0hk6qSN1kLouOwBvT4Pql4BvS/Otl7TGXvmHHPuZ+SON7CrIL7exIy2dnWh6XH9mNF3DeaynsLq8+RGZkoI2Te4Uzrnc4EwZFER7QzH8fQhMrgoJ62lZuN9NloGv0FNgCIGaoOYHZgdU1gwJ3kbHqCUREpOPo0EFBUVERHkflQlutVveQpD179iQ6OpqlS5cyfPhwAOx2O8uXL+fhhx9u8/ZKJ5ZVZQKxIzvgnWvg6vebP4xnY+oJGsvVU3BUTYG93MmSLamsSc5iR1oBO9MLOFJg1srEkcHVPnbshpW95WFEBtoY0yvcXR/QK8K/3t60RnP1qGTVnMDMrSAVMMzhV/3Cj/2enUHCGDMo2PcTJF1auT33kNnjY/Ew56AQERHpIDp0UDB16lQefPBBEhISGDx4MOvWreOxxx7juuuuA8y0odmzZzNv3jz69u1L3759mTdvHn5+fkyfPr2dWy+diiv//uRZ8OsrZjrRZ7fC+U/XnGOgMVxBRosEBRU9BUVHoLSAtFJPXl+9nzdW73cHAVXFhfhyYVAJpENJYAJL/3QmPVsqCKjRtrpnNXbLcxUZRzfvd9kZJZwMq56tOYmZK3UoeijYNGSyiIh0HB06KHj66ae59957mTVrFunp6cTGxnLjjTfy97//3X3MnDlzKC4uZtasWWRnZzN69GiWLFmiOQqkaVzpPoMuMGf+ffMKc8Zh11wCTeUKMo61ngDAJxjDNxRLcTYPvvEVL+30o9xpANAt0MbUYbEMiA6kX1QgfboF4G/zhJ93wxcQFDuAoMiAY29DXRrTU5B3yFx2hdQhF1e9QPoWKM4G31DzZ6UOiYhIB9Whg4LAwECeeOIJ9xCktbFYLMydO5e5c+e2WbvkOFNWYs4YDOZIQQGRMPEh+HIOfD3XfBs++MKmXbOF0odyi8v4fGMKI+3h9CObvTs3U+4cyageYVwzNpFzB0fjZa1luFFXkNMSQUl93KlNe80ZfGsb+rQrDUfqEtDN/FvK2g0HfoZ+55rbFRSIiEgH1aGDApE2kZ0MGGALMofZBBh9o/m2/+fn4cMbIbi7OYRkYx1DUJBbXMbSLWl88VsKK3ZmUOYwWOgVTj/rLi5ILOPWqacyKDao1e7fJMHx5kg7jlKzdqC2ycm6ysRlR0sYYwYF+1eaQUFRFmRsrdwnIiLSgSgoEHHPJ9Cres77xPnmG/Cdi810ot9/U3PG4doUZZkpI65rNkJOkZ0lFYHAj7uOUOYw3Pv6dAsgPnQg7FvNlHg7NBQQQJWgoJV7Cqxe5ug62XvNFCIFBZUSx8D61yrrClzLiH6VwaeIiEgHoaBAJKuO/H8PK1z6Arx4njns5hvTzMDA1kCOvuuBPKg7ePvVe2h2oZ2/fbSJxZtT3XUCAP2iApiUFMPkpBj6RgXC2t2wj8ZNYOYoqxypqLV7CsBMr8rea/a49BhXc78rfairBQWu3oBDv5gpavt+NH9W6pCIiHRACgpEXPn3tc08bAuE6W/Df8+EjG3w27sw8tr6r+d+S19/L8H21Hz+8Mpa9mcVATAgOpBJSTFMSoqmT7ejCuWbMoFZ9j4wHODl1zZ5/K621VVs7Co0DuxiQUFYL/CPNOdoOLyuyqRlCgpERKTjUVAg0tBIQcFxMOr38O0DsHNpE4KCut/SL9mcyq1vr6fQ7iA+zJdnp59IUvfguq/pnsBsHxhG/UN7uno+wnq3zRCgrhGIauvFcDohP9Vc7wqzGVdlsZi9BVs/gV1fQ8oGc3ui6glERKTjqWWoEJEupr6eApe+55jLPd9Bec25AarJrHuOAsMwWPjtTm549RcK7Q7G9Arnk5tOqT8ggIqCXg8oL4aC9Abu37ieihZT31wFRZngsAMWCIhum/Z0JK4UorUvgLPc/OcYktC+bRIREamFggLp2uxFlekt9RUFRw81H2rLCiuHlaxLHUFBkb2cP72xjkeW7ABgxphEXrl+FKH+3g2309O7cpz/huoK6glKWkV9cxXkVxQZ+0ea36GrSTjZXLoKzzXqkIiIdFAKCqRrc73d9gkGv7C6j7NYoO/Z5vrOpXUf53TWOpvxwewiLv33Sj7/LQUvq4X5Fydx3wVDap9joC6NrStw9RTU1/PRklztKs6Cktzq+/K6aJGxS/RQ8PKv/FmpQyIi0kEpKJCuLbMJ+feuFKKdS+o+Jj8FyorAwxNCEth7pJCXf0zmgoU/siUlj3B/b974w8lcOaoZKSTuuoK99R/nnrisjXoKbIHgVzHE5tG9Be7ZjLtoUGD1hPiTKn9OrGV0JhERkQ5AhcbStdU1HGltep1uPuxn7jQfvGtJNypO3Y4vcMQrhosf/cE9shDAoJgg/jtjJHEhvs1rq3v24Hp6CsqKIfeAud5WQQGYKURFR8yAJfaEyu1ddTjSqhLGmLUofuHmHAUiIiIdkHoKpGur2lPQEJ/gypzwnV+7NzudBq+u3Mv0/65i/mufArC+KIL9WUV4WS2M7R3OXycN5L3/G9P8gACqBAV76z7G1UvQUDpUS6ur2Ng1cVlbDI3aUQ26EDx9YOgVbTMalIiISDOop0C6NneqTSPz7/tOgL0rzBSi0TdgL3dy27sb+HSD+fB7pqf5Ztw3uj//O2MkY3qH429roX/NXLMp11dTULXIuC0fQOuaq6CrzmZcVbcBcPchczI8ERGRDko9BdK1NaWnACrrCvauoLAgj+sXreHTDYfxslq4c+IApvcuA2DcqFGcPSiq5QICqHzwzj0I5fbaj2nrImOXsAZ6CrpyUABmbYF6CUREpANTUCBdl70QCiom1mrsmP6RA8yx5stLePQ/L7Bi5xH8vK28MOMk/u/03vjlVzwUt0Y+v3+kOUsxRmXdwNFqGfmoTbjSh7L2Vt/uqinoarMZi4iIdDIKCqTrcqUO+YaBb2jjzrFYKEg4A4DErB8J9fPijT+czGn9IsFRVpna0xoP5RZLwyMQNTQ7c2tx9RTkVenFKM2H0jxzvavNZiwiItLJKCiQrqsZD9A70vKZu9WcRGyC1wbevXEMJ8SHmDtz9puz1nr5tV5hbWgHDQoCosDTFwxnZS+Ga44CW5A5bKmIiIh0WAoKpOtypdrUN5NxFb/sy+ay51byeUFf7HgSa6TTx+Nw5QFV8/k9WulfrfomMCvJg8L0yja0JYulZrFxvuoJREREOgsFBdJ1ZVakDzXiAXrJ5lSu+t8qcovLGJgQjaXHKeaOqhOZuYKC1nxLX1/6kCvI8e8GPkGt14a6HF1srOFIRUREOg0FBdJ1NWListJyB/d/uoUbXv2FkjInp/eP5LXfj8ZrwHnmAbUGBa1Y5FvfBGaZ7VRk7OIuNj4qKAiKa5/2iIiISKMpKJCuK7P+9KG9Rwq59N8refFH8yH32nE9+O81I/Hz9jTnKwDYt9JM24E2Cgrq6Slw37+RIym1NHdPwV5z6Q4K1FMgIiLS0SkokK6pav59LT0FH68/xOSnVvDboVxC/Lz43zUj+cfUwXhZPSrPCesNzjJIXm5ua4s39a70oZIcKM6pvq/dewp6mEtX+pB7OFIFBSIiIh2dggLpmlzDkfpFgE+we3ORvZw5723glrfWU2h3MKpHGF/ecipnD4qqeQ3XRGY7l4C9CPIOmT+3Zk2BLcCcrwBqFhu318RlLqFVegoMQ+lDIiIinYiCAumaaqkn2JqSx9Snf+CdtQexWODms/ryxh9GExPsW/s1XClEO5dWXs83FPzCWrHhVCk2rhIUGEb7TVzmEpIAFg8oK4KCNKUPiYiIdCKe7d0AkSbZ9D7sWAwentU/Vi/wsJpj5Z9wZWUqS12yqo889NWmVG55ax2l5U66Bdp44ooTGNs7ov5rJI4z5yTIT4EtH5vb2uKBPLQHHFpbva6gKBNKcs11V25/W/P0hqDukLsfjuyAwgxzu3oKREREOjwFBdJ5lJfCR7OgvKT+4w7+DL/7sP5jXMORhvfijdX7+dtHv+E04LR+kTw2bRgRAbaG2+PlAz3Hw44vYe2LFddri6CgoqegavqQq54gOB686ujZaAuhiWZQsH81YIDVG/zC2689IiIi0igKCqTzSNtkBgQ+wXDKreAoN2cQdpabBb9lJfDz87DnOyg8Av71vOmvSLX58rA/96z/DYArTorngQuH4GltQlZd3wlmUFCUaf7cFjMJuwt691Zuc9cTtNPIQy5hPWHvCtj3g/lzYIw5sZmIiIh0aAoKpPM49Ku57D7KDApqc/BnOLwOtn4CI6+r81JG5m4swMINBgB/PrMPf5nQD0tTH2BddQUubdFTUNsEZm0xHGpjuIqND/xsLjWbsYiISKegQmPpPA79Yi7jTqz7mMEXmcvNdacPlRZkYyk6AsA+opg7dRC3ndO/6QEBmMW1kQMrf26rmgKAnP3gdJrr7V1k7OKqZygrMpcajlRERKRTUFAgncfBteayvqBg0IXmcu8PUJBeY3d+SRn3L/oUgAwjmPlXjGXmuGMszK3aW9AW6TtBcWCxgsNeORdAZsOzM7eJowu81VMgIiLSKSgokM6hOAcyd5rr9QUFoYnmfsNpphBVkZFfypX/XUX+oe0AeHfry9RhLfDQ2v+8inv3BG//Y79eQ6yeEBJvrufsM3sLXKMptXdPQehRAZaCAhERkU5BQYF0DofXmcvQHuDfwGg2rhSiTZUpRDvS8rn43z+y6VAeg2zmUJnBcQNapm2JY+GSF+DSF1rmeo1Rtdg4P8VM17FYzXSm9uQbYs7V4KKgQEREpFNQUCCdQ2PqCVwGXWAu9/0I+aks257OJc/+xIGsYhLC/Li6X7m5P7wFU32SLm1c21pK1QnMXPUEoT3M+RraW9XegkAFBSIiIp2BggLpHJoSFIQkQPeTAIOVn73E9S+vIb+0nNE9w/j4pnEEFFSM7x/Wzvn3x6JqT4F75KEO8n2q1hWop0BERKRTUFAgHZ9hNK7IuArHwAsB8Nj6EU4DLh8Zz6vXjybU37tK/n0HeYhujqoTmGV2kJGHXNwzKlsgMLpdmyIiIiKNo6BAOr68Q1CYbubMxwxr8PCcIju3/GY+NJ9k2c6DZ4Xz0CVJeHt6QHE2FGeZB7b3RF/HolpPQUVQ0FG+jyt9yD+yY6QziYiISIMUFEjH50odihoMXr71Hrono4CLnv2Jz/Z68KvRHw+LwVWB6yrnIMis6CUIjGmbkYJaS0gPc5mfAmmbzfWO0lMQN8JcRie1bztERESk0RQUSMfXyHqCrzalctGzP5F8pJC4EF9ix003d1SdyMxVlNuZ6wkA/MLAO9Bcz91vLjtKOlTUYPi/lXDZS+3dEhEREWkkBQXS8R2sPyjIKynjtnc28MfXfiG3uIwRCSF8dNM4ok+eBljgwGrIPWge7E61OcYJy9qbxVJZVwBgtUFQ9/Zrz9GiBoFPcHu3QkRERBpJQYF0bE5H5RwF3UfW2P3T7iOc98QK3v/1IB4W+L/Te/PmDScTGWgzR75JGGMeuOVjc5nVQWb+bQlVR/kJ7w0e+tdZREREmsezvRsgUq+M7VBWCN4BENHPvbmkzMGCr7bz4o/JACSE+fHYtGGM7BFW/fzBF8H+n8wUojE3VekpOM6Cgo5SZCwiIiKdkoIC6dhc9QSxw8HDCsBvB3O59Z317EovAGD66AT+Omkg/rZa/pwHnQ9fzoGDayBn//HVUxBSJX2ooxQZi4iISKekoEA6tkOu+QlGYBgGz3+/h0cWb6fcaRAZaGPBJUM5Y0C3us8PjIbEcbDvB1jzApTkmttDO3lNAdRMHxIRERFpJgUF0rFV9BQYsSP41+LtPPud+aZ/UlI0D1yYRJi/d8PXGHJRZVAAEBQH3n6t1eK2E6qeAhEREWkZqkyU5snYDrmHWvce9iJI2wLAsztD3QHB3yYP5JnpIxoXEAAMPB8sHmDPN38+XvLvQxLAwwuwQHjf9m6NiIiIdGLqKZCmyz0Iz51iPmiffR+MuqF1Rr5J3QiGg3yvCP61Kh+w8I+pg7h2XBNTfwK6QY9TIPl78+fjJdXGyxcufh7KiiEgsr1bIyIiIp2Yegqk6bZ/CQ47lJfAV3fCK+ebRbwtzDho1hOsLEkELNx/weCmBwQugy+qXD8eRh5yGXIJDL+6vVshIiIinZyCAmm6HYvNZc/TwMsP9q6AZ8fCutfAMFrkFoZhsHnNMgDWO3vzzwsGc82YHs2/oCuFCI6fngIRERGRFqKgQJrGXliZhnPeAvjjDxA/2szX//gmeGs6FKQf0y0Mw+C+T7cQlLkBgBPHns3vjiUgAPCPgFE3QrdB5mhEIiIiIuKmoECaJvl7cJRCcAJEDjDful/7JZw9F6zesP0LePbkyhmEm8gwDOZ+splPftpIgkcGAGedNbFl2n7eQzBrJfiGtMz1RERERI4TCgqkaVypQ/3OAYvFXPewwim3wg3fQVQSFGXCO9eYtQdN9Pz3e1i0ch/DrBWTjEX0A5/glmm7iIiIiNRKQYE0nmHAziXmer9a3t5HDYY/fGvm7wPsXtaky+/LLOTxpTsAmD0gz9wYd2JzWysiIiIijaSgQBovbRPkHQJPX3OIz9p4ekPvM8z1nH2NvrRhGPz1w02UljsZ1yecoZaKngIFBSIiIiKtTkGBNJ4rdajXeHOM/LqEJJjLJgxT+uG6Q/yw6wg2Tw8evGAIloqZjBUUiIiIiLQ+BQXSeO7UoXPrPy6kh7nM2d+oIUqzCu088PlWAG4+qy89rOlQnG0WLkcNOYYGi4iIiEhjdPigoEePHlgslhqfm266CagYrWbuXGJjY/H19eX0009n8+bN7dzq41BhJhz42Vzve079xwZ3N5f2AijKavDSD36+laxCO/2jArnhtF5w6FdzR/RQMx1JRERERFpVhw8K1qxZQ0pKivuzdOlSAC677DIAFixYwGOPPcbChQtZs2YN0dHRTJgwgfz8/PZs9vFn19eAYb65dz3018XLBwKizfUG6gp+3HWE9389iMUC8y9JwsvqARUzGSt1SERERKRtdPigIDIykujoaPfns88+o3fv3owfPx7DMHjiiSf461//ysUXX8yQIUNYtGgRRUVFvPHGG+3d9OPLjq/MZUOpQy6hieaynqCgpMzBXz/8DYDfnZzIiIRQc4fqCURERETaVIcPCqqy2+289tprXHfddVgsFpKTk0lNTeWccyrTWWw2G+PHj+enn36q8zqlpaXk5eVV+0g9HOWw+xtzvW8jg4JGFBs//e1O9mYWER3kwx3n9q+4VxmkmDMZ031kMxssIiIiIk3RqYKCjz76iJycHGbOnAlAamoqAFFRUdWOi4qKcu+rzfz58wkODnZ/4uPjW63Nx4UDq6EkF3zDGv+gHlLRU5Bde0/B9tR8nl++B4C55w8m0MfL3JG22Zwx2ScYwnoda8tFREREpBE6VVDwwgsvcN555xEbG1ttu8U1s24FwzBqbKvq7rvvJjc31/05cOBAq7T3uOFKHeo7wZy9uDHq6SlwOg3u/mAj5U6DcwZFMXFIdOXOqqlD9fwzFBEREZGW49neDWisffv28fXXX/PBBx+4t0VHmw+TqampxMTEuLenp6fX6D2oymazYbPZWq+xxxvXUKQNjTpUVT1Bwes/7+fX/TkE2Dy574LB1XembzGX0UOb0VARERERaY5O01Pw0ksv0a1bNyZPnuze1rNnT6Kjo90jEoFZd7B8+XLGjh3bHs08/mTvhYxtYLFCn7Maf5670Lj6XAVZhXYWfLkNgDvO7U9M8FGToB3ZaS4j+h1Do0VERESkKTpFT4HT6eSll15ixowZeHpWNtlisTB79mzmzZtH37596du3L/PmzcPPz4/p06e3Y4uPIzsqegkSTgbf0MafF9QdsEB5MRRmQEA3ABZ+u4v80nIGxwZx9cmJNc/L3GUuI/oeW7tFREREpNE6RVDw9ddfs3//fq677roa++bMmUNxcTGzZs0iOzub0aNHs2TJEgIDA9uhpcehnYvNZVNSh8CcdCwoDvIOmsXGAd04mF3Ea6vMwuO7zhuA1eOomgF7IeQdMtfD+xxjw0VERESksTpFUHDOOedgVElBqcpisTB37lzmzp3bto3qCuyFkLzCXO83sennhySYQUHOPog/iceW7sDucDK2dzin9Imoebyrl8AvHPzCmt9uEREREWmSTlNTIO1gz3JzeNCQBIjs3/Tzq9QVbEvN48N1Zi/AnRMH1D46lKueIFypQyIiIiJtSUGB1M2dOnRu84YHdY9AtI9/fbUdw4BJSdEMiw+p/Xh3PYFSh0RERETaUqdIH5J2YBiVRcbNSR0Cd1CQm7Kbb/akY/WwcPs59fQ4qKdAREREpF2op0Bql/ob5B8GLz/ocUrzrlExq3Fhmjlz8bSR8fSKDKj7+EzXcKQKCkRERETakoICqZ0rdajnePDyad41KnoKwsvT8PWC2WfX87BvGHCkIn1IPQUiIiIibUrpQ1K7PcvNZd8Jzb6EIzAW8MBmKedPJwUSFVRPcJF3GMoKzUnSQns0+54iIiIi0nTqKZCanE5I2WCux49q9mU+WJ/KYWc4ADMGNvCn5kodCu1hznEgIiIiIm1GQYHUlLkLSvPA0xciBzbrEiVlDh5fuoODRiQAAcWH6z/hiOoJRERERNqLggKp6fCv5jJmKFibl2H22qp9HM4tIdMr2tyQva/+E1zDkWomYxEREZE2p6BAajpUERTEjmjW6XklZSxcZj7kJ/YZYG7MaSAoUE+BiIiISLtRUCA1uXoK4poXFPz7u93kFJXRO9KfQQOTzI0NBQWZmqNAREREpL1o9CGpzlFmzlEAzeop2JWez/9WmPMS3HXeQKz+FUXDOfvrPqmsGHIOmOvqKRARERFpcwoKpLr0rVBeArZgCOvVpFMNw+BvH22izGFw1oBunD2wG+SVmTtzD4LTAR7Wmidm7QEM857+kcf+HURERESkSZQ+JNW5Uodih4FH0/48Pl5/mFV7svDx8mDu+YOxWCwQGAMeXuAsN+ciqI27nqAPWCzH0HgRERERaQ4FBVJdM4uMc4vLeODzLQD8+cy+xIf5mTs8rBDc3VyvK4VI9QQiIiIi7UpBgVTXzCLjR5ds50iBnd6R/vzh1KPSjkITzWVdxcZHKoYjjdBwpCIiIiLt4ZiCgtLS0pZqh3QEZcWQZr7tb0pPwcaDOby6ynzg/+cFQ/D2POrPKiTBXKqnQERERKRDalJQsHjxYmbOnEnv3r3x8vLCz8+PwMBAxo8fz4MPPsjhww3MWisdW+pvYDjMYl9Xyk8DHE6zuNgw4MITYhnbJ6LmQSEVPQW1TWBmGFV6ChQUiIiIiLSHRgUFH330Ef3792fGjBl4eHhwxx138MEHH7B48WJeeOEFxo8fz9dff02vXr344x//SEZGRmu3W1rD4XXmMnZEowt+31i9j40Hcwn08eSeyQNrP8gVFNTWU1CYAaW5gKXJox2JiIiISMto1JCk8+bN45FHHmHy5Ml41DIizbRp0wA4dOgQTz75JK+88gq33XZby7ZUWt+hptUTZOSXsmDxdgDuOLc/3QJ9aj+wvvQh18hDIfHg5duU1oqIiIhIC2lUUPDzzz836mJxcXEsWLDgmBok7cg9HOnwRh0+74ut5JeUkxQXzFWjE+s+0FVonHfQnBzN6lW5T/UEIiIiIu3umEcfKigoIC8vryXaIu2pJK/yrX0jioxX7s7kw3WHsFjggQuHYPWoJ93IvxtYbWA4Ie9Q9X3uOQr6NbPhIiIiInKsmh0UbNmyhZEjRxIUFERoaChJSUmsXbu2JdsmbSllPWBAcDwE1D+rcJnDyb0fbwLgqtEJDIsPqf/aHh6VKURHFxtnajhSERERkfbW7KDgxhtv5E9/+hMFBQVkZmZy8cUXM2PGjJZsm7SlQ41PHVq+PYNd6QWE+nlxxzkDGnf9uuoKjih9SERERKS9NToouOCCCzh0qDL1IyMjg/PPPx8/Pz9CQkKYNGkSaWlprdJIaQNNmLTsg3UHAbhkRHeC/bwaOLpCbUFBuR2y95rrGo5UREREpN00Oii46qqrOOOMM3jqqacwDIM//elPDB48mCuuuIJLLrmEiRMnMnv27FZsqrSqQ1WGI61HTpGdr7ekA3DxiMbNZQDUPqtx9l5zXgTvAAiMaUJjRURERKQlNToomDZtGj///DObN29m9OjRjBs3jiVLljBu3DhOPfVUlixZwt/+9rfWbKu0lsIjkFvxBj/2hHoP/WxjCnaHk4ExQQyKDWr8PWrrKXCPPNS70fMiiIiIiEjLa9SQpC4hISE8//zz/PDDD8yYMYMJEybwz3/+Ez8/v9Zqn7QF16Rl4X3AJ7jeQ9//1ZU6FNe0e4T0MJdVC41VTyAiIiLSITSp0Dg7O5tffvmFpKQkfvnlFwIDAxk+fDiff/55a7VP2oK7yLj+1KE9GQWs25+D1cPC+SfENu0erp6C/BQoLzXXXT0FqicQERERaVeNDgrefvtt4uLimDx5MomJiXz55ZfMnTuXjz/+mAULFjBt2jQVGndWjSwy/nCdWWh+Wt+Iumcvrot/BHj5AQbkmr0NHKkYjjRcw5GKiIiItKdGBwV33nknL774IqmpqXzzzTfce++9AAwYMIDly5dz9tlnM2bMmFZrqLQSw2hUT4HTafDBr2ZQ0KQCYxeLpUpdQUUKkXoKRERERDqERgcF+fn59O/fH4DevXtTVFRUbf8NN9zAqlWrWrZ10vryDkFhOlisEJ1U52Grk7M4lFNMoI8nEwZFNe9eVYuNi7KgKNP8WT0FIiIiIu2q0YXGM2bMYPLkyZx++umsXbuW3/3udzWO6datW4s2TtqAq5eg2yDwrrtg3FVgPGVoDD5e1ubdK6RiWNLsfZUzGQfFgbd/864nIiIiIi2i0UHBY489xhlnnMG2bduYOXMm55xzTmu2S9qKa+ShuLpnMi6yl/PlbymAOWFZs1XtKXCPPKReAhEREZH21qQhSadOncrUqVNbqy3SHg43XE+wZHMahXYHCWF+nJgY2vx7VZ3ATPUEIiIiIh1Go2oK3nrrrUZf8MCBA/z444/NbpC0IcOo7CmIrbunwJU6dPGIOCzHMslYrT0FCgpERERE2lujgoJ///vfDBgwgIcffpitW7fW2J+bm8sXX3zB9OnTOfHEE8nKymrxhkoryNoDJblgtUHU4FoPSc0t4YddRwC4ePgxpA5BZU1BQRqkbTLXI5Q+JCIiItLeGpU+tHz5cj777DOefvpp7rnnHvz9/YmKisLHx4fs7GxSU1OJjIzk2muvZdOmTSo47ixcRcbRSWD1qvWQj9YfwjBgVI8wEsKPceZq31DwDgR7PmTvNbepp0BERESk3TW6pmDKlClMmTKFzMxMfvjhB/bu3UtxcTEREREMHz6c4cOH4+HRpAmSpb01MGmZYRi8/4uZOnTJiXHHfj/XXAXpm82fPX0gOP7YrysiIiIix6RJhcYA4eHhXHDBBa3RFmlrDUxatulQHjvTC7B5enBeUkzL3DM0sTIoCOsNCiRFRERE2p2eyLoqRzmkbDDX6+gpcBUYnzM4miCf2tOLmsxVbAyqJxARERHpIBQUdFUZ26C8GLwDas3rt5c7+WTDYQAuGdECqUMuVYMC1ROIiIiIdAgKCrqqA6vMZdyIWlN4lu/IIKvQTmSgjVP6RLTcfV0jEIHmKBARERHpIBQUdFX7K4KChLG17v54/SEALhoeh6e1Bf9M1FMgIiIi0uE0+Wnvu+++a4VmSJvbt9JcJo6pscvhNFix05yb4NzB0S1739AeYLGaH9UUiIiIiHQITR59aOLEicTFxXHttdcyY8YM4uM1pGSnk7Mf8g6Chyd0P6nG7t8O5ZJbXEaQjyfDuge37L19guDi/5jDk/q08LVFREREpFma3FNw+PBhbrnlFj744AN69uzJueeeyzvvvIPdbm+N9klrcPUSxAwDb/8au1fsyABgXJ+Ilk0dckm6FIZc0vLXFREREZFmafITX1hYGDfffDO//vora9eupX///tx0003ExMRw8803s2HDhtZop7Sk/T+Zy4SaqUOAO3Xo1L6RbdUiEREREWlHx/Qa+IQTTuCuu+7ipptuorCwkBdffJETTzyRU089lc2bN7dUG6WluYuMawYF+SVl/Lo/G4BT+7bgqEMiIiIi0mE1KygoKyvjvffeY9KkSSQmJrJ48WIWLlxIWloaycnJxMfHc9lll7V0W6UlFGWZcxRArUHBqj1ZlDsNekb4Ex/m18aNExEREZH20ORC4z//+c+8+eabAFx99dUsWLCAIUOGuPf7+/vz0EMP0aNHjxZrpLSg/RX1BBH9wT+8xu4VO816AvUSiIiIiHQdTQ4KtmzZwtNPP80ll1yCt7d3rcfExsaybNmyY26ctIJ9FfUEtQxFCqonEBEREemKmhwUfPPNNw1f1NOT8ePHN6tB0spcPQW1TFp2IKuI5COFeHpYOLlXWBs3TERERETaS5NrCubPn8+LL75YY/uLL77Iww8/3CKNklZiL4SUitGhEk6usdvVSzA8IYRAH6+2bJmIiIiItKMmBwXPP/88AwYMqLF98ODBPPfccy3SKGklB9eCsxyC4iAkocbuynoCpQ6JiIiIdCVNDgpSU1OJiYmpsT0yMpKUlJQWaVRVhw4d4uqrryY8PBw/Pz9OOOEEfvnlF/d+wzCYO3cusbGx+Pr6cvrpp2s41Lq4U4fGmDMKV1HucPLjLlc9gYqMRURERLqSJgcF8fHx/PjjjzW2//jjj8TGxrZIo1yys7MZN24cXl5efPnll2zZsoVHH32UkJAQ9zELFizgscceY+HChaxZs4bo6GgmTJhAfn5+i7bluFBPkfHGQ7nklZQT5OPJ0O4hbdsuEREREWlXTS40/v3vf8/s2bMpKyvjzDPPBMzi4zlz5nDbbbe1aOMefvhh4uPjeemll9zbqg51ahgGTzzxBH/961+5+OKLAVi0aBFRUVG88cYb3HjjjbVet7S0lNLSUvfPeXl5LdruDslRBgfXmOu1FBmv2GH2EpzSNwKrh6XGfhERERE5fjW5p2DOnDlcf/31zJo1i169etGrVy/+/Oc/c/PNN3P33Xe3aOM++eQTRo4cyWWXXUa3bt0YPnw4//3vf937k5OTSU1N5ZxzznFvs9lsjB8/np9++qnO686fP5/g4GD3Jz4+vkXb3SGlbISyIvAJgciaNSGqJxARERHpupocFFgsFh5++GEyMjJYtWoVGzZsICsri7///e8t3rg9e/bw73//m759+7J48WL++Mc/cvPNN/PKK68AZn0DQFRUVLXzoqKi3Ptqc/fdd5Obm+v+HDhwoMXb3uG46wlOBo/q/9jzSspYdyAHgFP6qJ5AREREpKtpcvqQS0BAACeddFJLtqUGp9PJyJEjmTdvHgDDhw9n8+bN/Pvf/+aaa65xH2c5qmjWMIwa26qy2WzYbLbWaXRHVbXI+Cgrd2ficBr0ivAnPsyvjRsmIiIiIu2tWUHBmjVrePfdd9m/fz92u73avg8++KBFGgYQExPDoEGDqm0bOHAg77//PgDR0dFAzRGR0tPTa/QedGmGURkUJNasJ/hhp0YdEhEREenKmpw+9NZbbzFu3Di2bNnChx9+SFlZGVu2bOHbb78lODi4RRs3btw4tm/fXm3bjh07SExMBKBnz55ER0ezdOlS93673c7y5csZO7bmw2+XdWQHFGWCpy/EnFBjt+oJRERERLq2JgcF8+bN4/HHH+ezzz7D29ubJ598kq1btzJt2jQSEmpOiHUsbr31VlatWsW8efPYtWsXb7zxBv/5z3+46aabADNtaPbs2cybN48PP/yQTZs2MXPmTPz8/Jg+fXqLtqVTcw1F2n0keHpX27U/s4i9mUV4elg4uXd4OzRORERERNpbk4OC3bt3M3nyZMDMzS8sLMRisXDrrbfyn//8p0Ubd9JJJ/Hhhx/y5ptvMmTIEP75z3/yxBNPcNVVV7mPmTNnDrNnz2bWrFmMHDmSQ4cOsWTJEgIDA1u0LZ1aPfUEK3aZvQQjEkMJsDW7xEREREREOrEmPwWGhYW5JwaLi4tj06ZNJCUlkZOTQ1FRUYs3cMqUKUyZMqXO/RaLhblz5zJ37twWv/dxo+rIQ0dxzU9wqkYdEhEREemymhwUnHrqqSxdupSkpCSmTZvGLbfcwrfffsvSpUs566yzWqONcixyD0HOfrB4QPyoarvKHU5+3F0RFPRTPYGIiIhIV9XkoGDhwoWUlJQA5nj/Xl5e/PDDD1x88cXce++9Ld5AOUauXoLooWCrnlK14WAu+SXlBPt6kRTXskXiIiIiItJ5NCkoKC8v59NPP+Xcc88FwMPDgzlz5jBnzpxWaZy0AFeRcS1DkbpGHTqlTwRWj7rndRARERGR41uTCo09PT35v//7P0pLS1urPdLS6isy1vwEIiIiIkIzRh8aPXo069ata422SEsrzob0Leb6UUFBbnEZ6w/kAHCKggIRERGRLq3JNQWzZs3itttu4+DBg5x44on4+/tX2z906NAWa5wco/2rzWV4XwioXki8JjkLh9OgV4Q/3UP92qFxIiIiItJRNDkouPzyywG4+eab3dssFguGYWCxWHA4HC3XOjk2+yvqCWoZinTNviwARvUMa8sWiYiIiEgH1OSgIDk5uTXaIa3B1VNQS5Hx2r3ZAIzsoaBAREREpKtrclCQmJjYGu2QluZ0QMoGcz1uZLVdJWUONh7MAWCUggIRERGRLq/JQcErr7xS7/5rrrmm2Y2RFnRkB5QXg3cAhPeptmvDgRzKHAbdAm3Eh/m2UwNFREREpKNoclBwyy23VPu5rKyMoqIivL298fPzU1DQUbh6CaKTwKP6IFNr95mpQyf1CMNi0fwEIiIiIl1dk4ckzc7OrvYpKChg+/btnHLKKbz55put0UZpjsPrzWXMCTV2rdlrFhmP7BHadu0RERERkQ6ryUFBbfr27ctDDz1UoxdB2pGrpyD2hGqbHU6DX6r0FIiIiIiItEhQAGC1Wjl8+HBLXU6OhdMJqRvN9Zhh1XbtSMsnv6Qcf28rA6ID26FxIiIiItLRNLmm4JNPPqn2s2EYpKSksHDhQsaNG9diDZNjkLkL7AXg6QsR/artWluROjQiMRRPa4vFhCIiIiLSiTU5KLjwwgur/WyxWIiMjOTMM8/k0Ucfbal2ybGoVmRsrbZrzV6lDomIiIhIdU0OCpxOZ2u0Q1pSynpzeVQ9gWEYKjIWERERkRqUP3I8cvUUHDXy0KGcYlJyS/D0sHBCfEibN0tEREREOqYmBwWXXnopDz30UI3t//rXv7jssstapFFyDJzOKkFB9SLjtRWpQ4PjgvHzbnInkYiIiIgcp5ocFCxfvpzJkyfX2D5x4kS+//77FmmUHIPsZCjNA08fiBxQbZcrdeikRKUOiYiIiEilJgcFBQUFeHt719ju5eVFXl5eizRKjoGrniBqMFir9wa4egpGqshYRERERKpoclAwZMgQ3n777Rrb33rrLQYNGtQijZJjUMdMxrlFZWxPywdUZCwiIiIi1TU5sfzee+/lkksuYffu3Zx55pkAfPPNN7z55pu8++67Ld5AaaI6ZjL+Zb+ZOtQrwp+IAFsbN0pEREREOrImBwXnn38+H330EfPmzeO9997D19eXoUOH8vXXXzN+/PjWaKM0lmHUWWT8c7LmJxARERGR2jVrCJrJkyfXWmws7SxnH5TkgNUbIgdW27VW8xOIiIiISB2aXFOwZs0aVq9eXWP76tWrWbt2bYs0SprJVU/QbRB4VhaDl5Q52HgwF1BPgYiIiIjU1OSg4KabbuLAgQM1th86dIibbrqpRRolzVTHTMa/HcrF7nASEWAjMdyvzZslIiIiIh1bk4OCLVu2MGLEiBrbhw8fzpYtW1qkUdJMdcxk7J6foEcoFouljRslIiIiIh1dk4MCm81GWlpaje0pKSl4emqW3HZjGFWGI619JmPNTyAiIiIitWlyUDBhwgTuvvtucnNz3dtycnK45557mDBhQos2Tpog9yAUZ4GHpzlxWQWn03AXGZ+kImMRERERqUWTX+0/+uijnHbaaSQmJjJ8+HAA1q9fT1RUFK+++mqLN1AayVVP0G0geFbOQ7AzvYC8knL8vK0Miglqn7aJiIiISIfW5KAgLi6OjRs38vrrr7NhwwZ8fX259tprufLKK/Hy8mqNNkpj1DGT8c8VvQQjEkLxtDa5Y0hEREREuoBmFQH4+/tzww03tHRb5FjUMWmZ5icQERERkYY0uzJ4y5Yt7N+/H7vdXm37+eeff8yNkiYyjCrDkQ6vtstVZKz5CURERESkLk0OCvbs2cNFF13Eb7/9hsViwTAMAPdQlw6Ho2VbKA3LT4HCDLBYqxUZH8op5lBOMVYPCyfEh7Rf+0RERESkQ2tykvktt9xCz549SUtLw8/Pj82bN/P9998zcuRIvvvuu1ZoojTIVU8QOQC8fN2bXalDg2OD8LdpuFgRERERqV2TnxRXrlzJt99+S2RkJB4eHnh4eHDKKacwf/58br75ZtatW9ca7ZT6uOoJjprJ2D0/QaJSh0RERESkbk3uKXA4HAQEBAAQERHB4cOHAUhMTGT79u0t2zppHFc9wVFFxms0P4GIiIiINEKTewqGDBnCxo0b6dWrF6NHj2bBggV4e3vzn//8h169erVGG6UhtQxHWlBazo60fABGJCooEBEREZG6NTko+Nvf/kZhYSEADzzwAFOmTOHUU08lPDyct99+u8UbKA3IT4WCVLB4QPQQ9+aNB3JwGhAX4ktUkE87NlBEREREOromBwXnnnuue71Xr15s2bKFrKwsQkND3SMQSRty1RNE9ANvf/fmdQdyABieENL2bRIRERGRTqVFhqQJC1Mha7txT1p2QrXNv+4zi4yHJyh1SERERETq1+RCY+lg3PUElUXGhmG4ewpGqKdARERERBqgoKCzc89kfIJ7077MIrIK7XhbPRgUG9QuzRIRERGRzkNBQWdWkAF5hwALRCe5N687YKYODYkLwuZpbafGiYiIiEhnoaCgM3P1EoT3AVuge/Ov+3IA1ROIiIiISOMoKOjM9q4wl/Gjqm129RSMUFAgIiIiIo2goKAz273MXPY6w72pyF7O1hRz0jINRyoiIiIijaGgoLMqzITUjeZ6r/Huzb8dzMXhNIgO8iE2xLedGiciIiIinYmCgs4q+TtzGTUEArq5N/+6PwdQL4GIiIiINJ6Cgs5qz3fmstfp1Tav2696AhERERFpGgUFnZFhwO7vzPUqQYFhGOopEBEREZEmU1DQGWXtgdz94OEFiWPdmw9mF3OkoBQvq4UhccHt2EARERER6UwUFHRGrtSh+NHg7e/e/GtF6tCgmCB8vDRpmYiIiIg0TocOCubOnYvFYqn2iY6Odu83DIO5c+cSGxuLr68vp59+Ops3b27HFreRPRVDkfY+vdrmde7UIdUTiIiIiEjjdeigAGDw4MGkpKS4P7/99pt734IFC3jsscdYuHAha9asITo6mgkTJpCfn9+OLW5lTgckf2+uV5mfACqLjFVPICIiIiJN0eGDAk9PT6Kjo92fyMhIwOwleOKJJ/jrX//KxRdfzJAhQ1i0aBFFRUW88cYb7dzqVnR4PZTkgi0YYk5wby4pc7D5cB6gkYdEREREpGk6fFCwc+dOYmNj6dmzJ1dccQV79uwBIDk5mdTUVM455xz3sTabjfHjx/PTTz/Ve83S0lLy8vKqfToNV+pQz1PB6unevOlQLuVOg4gAG91DNWmZiIiIiDRehw4KRo8ezSuvvMLixYv573//S2pqKmPHjiUzM5PU1FQAoqKiqp0TFRXl3leX+fPnExwc7P7Ex8e32ndocXXOT5ADwIiEECwWS5s2SUREREQ6tw4dFJx33nlccsklJCUlcfbZZ/P5558DsGjRIvcxRz8AG4bR4EPx3XffTW5urvtz4MCBlm98a7AXwYHV5vpR9QS/uusJlDokIiIiIk3ToYOCo/n7+5OUlMTOnTvdoxAd3SuQnp5eo/fgaDabjaCgoGqfTmH/T+CwQ3A8hPeutqtqT4GIiIiISFN0qqCgtLSUrVu3EhMTQ8+ePYmOjmbp0qXu/Xa7neXLlzN27Nh6rtKJ7a6oJ+g1Hqr0hhzOKSY1rwSrh4Wk7pq0TERERESaxrPhQ9rP7bffztSpU0lISCA9PZ0HHniAvLw8ZsyYgcViYfbs2cybN4++ffvSt29f5s2bh5+fH9OnT2/vpreOPcvNZY2hSHMAGBgTiJ93h/5HKiIiIiIdUId+gjx48CBXXnklR44cITIykpNPPplVq1aRmJgIwJw5cyguLmbWrFlkZ2czevRolixZQmBgYDu3vBUUpENaxRwNPcdX2+WuJ4hXPYGIiIiINF2HDgreeuutevdbLBbmzp3L3Llz26ZB7ck1YVlUEgREVtvlmrRsRGJIGzdKRERERI4HnaqmoEtz1RP0Pr3a5tJyB5sOmfMsqKdARERERJpDQUFnYBh1zk+w5XAedoeTMH9vEsP92rxpIiIiItL5KSjoDDJ3Q95BsHpDQvWRlX6tKDIeHq9Jy+T/27v/2Kqr+4/jr9uW3rb09iIUelupWGaBIcIQHF+BCUxoomSbYZtOUTDGbAo4KpugYGbV2CKLxB+dNRrjXBzDZcOMzWnoxqhzlVhxVb7FAF9F7NRa0dp7bUtLe8/3j3I/7aXgoPTez2nv85HcUD6fjzdv2xNyXz3nfQ4AAED/EAoGg/eOLx3KnyWlRs8G9PQTsHQIAAAA/UMoGAxOsXRI6tmOdHr+iHhVAwAAgCGGUGC7rs6enYe+Fn0+wSfBo/rwizYleaSphAIAAAD0E6HAdh/9W2oPSml+KfcbUbdq67+QJE3I8SnTa/XusgAAALAYocB2kX6CgsukpOSoWwcaQpKkyXlZ8a4KAAAAQwihwHZOP8GCPrf+79MvJUkXjMmMY0EAAAAYaggFtmv43+4/z/ufPrf+r7E7FBSO8cWzIgAAAAwxhAKbHQ1K7c3dX484L+pWOGz0LjMFAAAAGACEApsFP+z+M80veaNnAz78ok1Hj4WVmpyk/HPSXSgOAAAAQwWhwGbN/+n+05/f59bBxu4m4/GjhyslmR8jAAAA+o9PkzZzQsHYPrci/QRfY+kQAAAAzhKhwGaRUJB1bp9bPU3GhAIAAACcHUKBzb5ipuBgI03GAAAAGBiEAptFGo1P6CkwxjgzBYQCAAAAnC1Cgc2a67v/9EcvH2oMtSt0tFNJHqkge7gLhQEAAGAoIRTYKhyWmiMzBdHLhyKzBONGDZc3JTnelQEAAGCIIRTYquVTKXxMkkfy5UbdYukQAAAABhKhwFaRJmNfrpQ8LOpW5IwCQgEAAAAGAqHAVsH/fkbBBaMJBQAAADh7hAJbOduRnuyMghZJUmEOoQAAAABnj1Bgq1OcUfBFa4eOfNkuSfoaMwUAAAAYAIQCWzmhIPqMgsjSoXNHpGu4NyXeVQEAAGAIIhTYKhIKsqKXD0VOMv4aTcYAAAAYIIQCW51i+VBkpqCQUAAAAIABQiiwUWe71NLY/fUplg+xHSkAAAAGCqHARsHjJxmnpEkZI6NuEQoAAAAw0AgFNuq9dMjjcS63tHfqwy/aJHFGAQAAAAYOocBGzcdnCk7oJ3j30+5ZguzMVJ0zPDXeVQEAAGCIIhTYyNl56ORNxiwdAgAAwEAiFNgo+NU7DxEKAAAAMJAIBTZyegpOfkYB/QQAAAAYSIQCG53ijIJ3I2cU5PjiXREAAACGMEKBbYzpFQp6ziho7+zS+5+1SGL5EAAAAAYWocA2R5ulju4ZAWX1LB96/0irwkbypaVojM/rUnEAAAAYiggFtonMEqSPlFIznMu9m4w9vc4uAAAAAM4WocA2wZOfUXCwMSSJJmMAAAAMPEKBbZrru/88xXakhTmEAgAAAAwsQoFtTrHzEGcUAAAAIFYIBbZp7rt8qCts9N6R7p2HCsewHSkAAAAGFqHANpGZgl47D9V/3qqOzrDShiXp3BHpLhUGAACAoYpQYJtg3zMKIicZj8/OVFISOw8BAABgYBEKbBLukoIfdX/da/kQTcYAAACIJUKBTb78RAp3Sp5kyRdwLrMdKQAAAGKJUGCTSJNxVp6UlOxcfpeZAgAAAMQQocAmkTMKejUZG2PYjhQAAAAxRSiwyUnOKPi4+ahaOrqUkuTRuFHDXSoMAAAAQxmhwCbBvmcURGYJzs8ermHJ/LgAAAAw8PiUaZOTzBREtiOlyRgAAACxQiiwSaSngO1IAQAAEEeEAps0910+9C5NxgAAAIgxQoEtjrVJrUe6v+61+9Dhz1skSQXZNBkDAAAgNgZVKCgrK5PH41FxcbFzzRijkpIS5eXlKT09XfPnz1ddXZ17RfZX5CTjYcOl9HN6Lrd1SpLOyUh1oyoAAAAkgEETCmpqavTkk09q6tSpUdc3bdqkzZs3q7y8XDU1NQoEAlq0aJFCoZBLlfZT734Cj0eS1NEZVtuxLklSVvowtyoDAADAEDcoQsGXX36ppUuX6qmnntI55/T8Ft0Yo4cfflgbNmzQkiVLNGXKFD377LNqbW3Vli1bXKy4H5ydh3qWDoWOHpPUnRF83hQ3qgIAAEACGBShYOXKlVq8eLEWLlwYdf3QoUNqaGhQUVGRc83r9WrevHmqrq4+5fu1t7crGAxGvVx3kibj4NHupUOZ3hQlJXncqAoAAAAJwPpfP2/dulVvvvmmampq+txraGiQJOXk5ERdz8nJ0eHDh0/5nmVlZbr33nsHttCz5SwfyncuBdu6Zwqy0lg6BAAAgNixeqagvr5eq1ev1nPPPae0tLRTPufxRP8W3RjT51pvd911l5qbm51XfX39gNXcb5HlQ712HgoeXz5EPwEAAABiyeqZgj179qixsVEzZsxwrnV1demVV15ReXm59u/fL6l7xiA3N9d5prGxsc/sQW9er1derzd2hfdH8CTLh47vPJSVZvWPCQAAAIOc1TMFl19+ufbu3ava2lrnNXPmTC1dulS1tbUaP368AoGAKisrnf+mo6NDVVVVmj17touVnyFjejUa9+4pYKYAAAAAsWf1r6B9Pp+mTJkSdW348OEaNWqUc724uFilpaUqLCxUYWGhSktLlZGRoeuuu86NkvunrUk61tr9dVaec5meAgAAAMSD1aHgdKxdu1ZtbW1asWKFmpqaNGvWLO3YsUM+n8/t0k5fZJZg+GhpWLpzuWemYND/mAAAAGCxQfdpc9euXVF/93g8KikpUUlJiSv1DIiTNBlLPT0FPmYKAAAAEENW9xQkjJM0GUu9ZgpoNAYAAEAMEQpscJIzCqRePQU0GgMAACCGCAU2cHYeOmH50NHIlqSEAgAAAMQOocAGzadYPtRGozEAAABij1BgA2em4ITlQ0fZkhQAAACxRyhwW1enFPqo++tT7D7kp6cAAAAAMUQocNuXDZIJS0nDpMwc53JHZ1htx7okMVMAAACA2CIUuM05oyBPSur5cYSOLx2SpEy2JAUAAEAMEQrc5vQTnHhGwfGDy7wpSk7yxLsqAAAAJBBCgdtOFQo4owAAAABxwroUt028Uhqefcqdh3wsHQIAAECM8YnTbaMndL9OENl5iJkCAAAAxBrLhyzFGQUAAACIF0KBpTjNGAAAAPFCKLAUMwUAAACIF0KBpegpAAAAQLwQCizVM1PA8iEAAADEFqHAUpxTAAAAgHghFFgqcqIxPQUAAACINUKBpdh9CAAAAPFCKLAUuw8BAAAgXggFlorsPuSnpwAAAAAxRiiwUEdnWG3HuiQxUwAAAIDYIxRYKHR86ZAkZbIlKQAAAGKMUGChyM5DPm+KkpM8LlcDAACAoY5QYCHOKAAAAEA8EQosFNl5yMfSIQAAAMQBocBCkZ2HmCkAAABAPBAKLMQZBQAAAIgnQoGFOM0YAAAA8UQosBAzBQAAAIgnQoGF6CkAAABAPBEKLNQzU8DyIQAAAMQeocBCnFMAAACAeCIUWChyojE9BQAAAIgHQoGF2H0IAAAA8UQosBC7DwEAACCeCAUWiuw+5KenAAAAAHFAKLBMR2dYbce6JDFTAAAAgPggFFgmdHzpkCRlsiUpAAAA4oBQYJnIzkM+b4qSkzwuVwMAAIBEQCiwDGcUAAAAIN4IBZaJ7DzkY+kQAAAA4oRQYJnIzkPMFAAAACBeCAWW4YwCAAAAxBuhwDKcZgwAAIB4IxRYhpkCAAAAxBuhwDL0FAAAACDeCAWW6ZkpYPkQAAAA4oNQYBnOKQAAAEC8EQosEznRmJ4CAAAAxAuhwDLsPgQAAIB4IxRYht2HAAAAEG+EAstEdh/y01MAAACAOCEUWKSjM6y2Y12SmCkAAABA/FgdCioqKjR16lRlZWUpKytLl156qV566SXnvjFGJSUlysvLU3p6uubPn6+6ujoXKz47oeNLhyQpky1JAQAAECdWh4KxY8dq48aNeuONN/TGG2/o29/+tr73ve85H/w3bdqkzZs3q7y8XDU1NQoEAlq0aJFCoZDLlfdPZOchnzdFyUkel6sBAABAorA6FHznO9/RlVdeqQkTJmjChAl64IEHlJmZqd27d8sYo4cfflgbNmzQkiVLNGXKFD377LNqbW3Vli1b3C69XzijAAAAAG6wOhT01tXVpa1bt6qlpUWXXnqpDh06pIaGBhUVFTnPeL1ezZs3T9XV1V/5Xu3t7QoGg1EvG0R2HvKxdAgAAABxZH0o2Lt3rzIzM+X1enXLLbfohRde0OTJk9XQ0CBJysnJiXo+JyfHuXcqZWVl8vv9zis/Pz9m9Z+JyM5DzBQAAAAgnqwPBRMnTlRtba12796tW2+9VcuXL9e+ffuc+x5P9Np7Y0yfaye666671Nzc7Lzq6+tjUvuZ4owCAAAAuMH6dSqpqam64IILJEkzZ85UTU2NHnnkEa1bt06S1NDQoNzcXOf5xsbGPrMHJ/J6vfJ6vbErup84zRgAAABusH6m4ETGGLW3t6ugoECBQECVlZXOvY6ODlVVVWn27NkuVth/zBQAAADADVb/Snr9+vW64oorlJ+fr1AopK1bt2rXrl16+eWX5fF4VFxcrNLSUhUWFqqwsFClpaXKyMjQdddd53bp/UJPAQAAANxgdSj45JNPdMMNN+jjjz+W3+/X1KlT9fLLL2vRokWSpLVr16qtrU0rVqxQU1OTZs2apR07dsjn87lcef/0zBRY/WMBAADAEGP1p8+nn376K+97PB6VlJSopKQkPgXFGOcUAAAAwA2DrqdgKIucaExPAQAAAOKJUGARdh8CAACAGwgFFmH3IQAAALiBUGCRyO5DfnoKAAAAEEeEAkt0dIbVdqxLEjMFAAAAiC9CgSVCx5cOSVImW5ICAAAgjggFlojsPOTzpig5yeNyNQAAAEgkhAJLcEYBAAAA3EIosERk5yEfS4cAAAAQZ4QCS0R2HmKmAAAAAPFGKLAEZxQAAADALYQCS3CaMQAAANxCKLAEMwUAAABwC6HAEvQUAAAAwC2EAkv0zBSwfAgAAADxRSiwBOcUAAAAwC2EAktETjSmpwAAAADxRiiwBLsPAQAAwC2EAkuw+xAAAADcQiiwRGT3IT89BQAAAIgzQoEFOjrDajvWJYmZAgAAAMQfocACoeNLhyQpky1JAQAAEGeEAgtEdh7K9KYoOcnjcjUAAABINIQCCzg7DzFLAAAAABcQCizg7DxEkzEAAABcQCiwQGTnIZqMAQAA4AZCgQV6ZgpYPgQAAID4IxRYoKengJkCAAAAxB+hwAL0FAAAAMBNhAIL9PQUsHwIAAAA8UcosAAzBQAAAHATocACoaPsPgQAAAD3EAos4DQas/sQAAAAXEAosICzfIiZAgAAALiAUGABp9GYngIAAAC4gFBgAWYKAAAA4CZCgcuOdYXV2tEliZ4CAAAAuINQ4LLIzkOSlOklFAAAACD+CAUui+w8lOlNUUoyPw4AAADEH59CXdbTT8AsAQAAANxBKHAZOw8BAADAbYQCl7HzEAAAANxGKHAZpxkDAADAbYQClzFTAAAAALfx62mXfXfauZqS59eIjFS3SwEAAECCIhS4LOBPU8Cf5nYZAAAASGAsHwIAAAASHKEAAAAASHCEAgAAACDBEQoAAACABEcoAAAAABIcoQAAAABIcIQCAAAAIMERCgAAAIAEZ3UoKCsr0yWXXCKfz6cxY8boqquu0v79+6OeMcaopKREeXl5Sk9P1/z581VXV+dSxQAAAMDgY3UoqKqq0sqVK7V7925VVlaqs7NTRUVFamlpcZ7ZtGmTNm/erPLyctXU1CgQCGjRokUKhUIuVg4AAAAMHh5jjHG7iNP16aefasyYMaqqqtJll10mY4zy8vJUXFysdevWSZLa29uVk5OjBx98UD/5yU9O632DwaD8fr+am5uVlZUVy/8FAAAAIC7O5DOu1TMFJ2pubpYkjRw5UpJ06NAhNTQ0qKioyHnG6/Vq3rx5qq6uPuX7tLe3KxgMRr0AAACARDVoQoExRmvWrNHcuXM1ZcoUSVJDQ4MkKScnJ+rZnJwc597JlJWVye/3O6/8/PzYFQ4AAABYbtCEglWrVuntt9/W7373uz73PB5P1N+NMX2u9XbXXXepubnZedXX1w94vQAAAMBgkeJ2Aafjtttu0/bt2/XKK69o7NixzvVAICCpe8YgNzfXud7Y2Nhn9qA3r9crr9cbu4IBAACAQcTqmQJjjFatWqVt27Zp586dKigoiLpfUFCgQCCgyspK51pHR4eqqqo0e/bseJcLAAAADEpWzxSsXLlSW7Zs0Z/+9Cf5fD6nT8Dv9ys9PV0ej0fFxcUqLS1VYWGhCgsLVVpaqoyMDF133XUuVw8AAAAMDlaHgoqKCknS/Pnzo64/88wzuvHGGyVJa9euVVtbm1asWKGmpibNmjVLO3bskM/ni3O1AAAAwOA0qM4piBXOKQAAAMBQM2TPKQAAAAAw8AgFAAAAQIKzuqcgXiIrqDjZGAAAAENF5LPt6XQLEAokhUIhSeJkYwAAAAw5oVBIfr//K5+h0VhSOBzWRx99JJ/P95UnIfdXMBhUfn6+6uvraWTGgGJsIVYYW4gVxhZihbHVlzFGoVBIeXl5Skr66q4BZgokJSUlRZ2UHCtZWVkMUsQEYwuxwthCrDC2ECuMrWj/bYYggkZjAAAAIMERCgAAAIAERyiIA6/Xq3vuuUder9ftUjDEMLYQK4wtxApjC7HC2Do7NBoDAAAACY6ZAgAAACDBEQoAAACABEcoAAAAABIcoQAAAABIcISCGHv88cdVUFCgtLQ0zZgxQ//85z/dLgmDTFlZmS655BL5fD6NGTNGV111lfbv3x/1jDFGJSUlysvLU3p6uubPn6+6ujqXKsZgVVZWJo/Ho+LiYucaYwv99eGHH+r666/XqFGjlJGRoW984xvas2ePc5+xhf7o7OzU3XffrYKCAqWnp2v8+PG67777FA6HnWcYW/1DKIih559/XsXFxdqwYYP+/e9/61vf+pauuOIKffDBB26XhkGkqqpKK1eu1O7du1VZWanOzk4VFRWppaXFeWbTpk3avHmzysvLVVNTo0AgoEWLFikUCrlYOQaTmpoaPfnkk5o6dWrUdcYW+qOpqUlz5szRsGHD9NJLL2nfvn166KGHNGLECOcZxhb648EHH9QTTzyh8vJyvfPOO9q0aZN++ctf6rHHHnOeYWz1k0HMfPOb3zS33HJL1LVJkyaZO++806WKMBQ0NjYaSaaqqsoYY0w4HDaBQMBs3LjReebo0aPG7/ebJ554wq0yMYiEQiFTWFhoKisrzbx588zq1auNMYwt9N+6devM3LlzT3mfsYX+Wrx4sbnpppuiri1ZssRcf/31xhjG1tlgpiBGOjo6tGfPHhUVFUVdLyoqUnV1tUtVYShobm6WJI0cOVKSdOjQITU0NESNNa/Xq3nz5jHWcFpWrlypxYsXa+HChVHXGVvor+3bt2vmzJn64Q9/qDFjxmj69Ol66qmnnPuMLfTX3Llz9fe//10HDhyQJL311lt69dVXdeWVV0pibJ2NFLcLGKqOHDmirq4u5eTkRF3PyclRQ0ODS1VhsDPGaM2aNZo7d66mTJkiSc54OtlYO3z4cNxrxOCydetWvfnmm6qpqelzj7GF/nrvvfdUUVGhNWvWaP369Xr99df105/+VF6vV8uWLWNsod/WrVun5uZmTZo0ScnJyerq6tIDDzyga6+9VhL/bp0NQkGMeTyeqL8bY/pcA07XqlWr9Pbbb+vVV1/tc4+xhjNVX1+v1atXa8eOHUpLSzvlc4wtnKlwOKyZM2eqtLRUkjR9+nTV1dWpoqJCy5Ytc55jbOFMPf/883ruuee0ZcsWXXjhhaqtrVVxcbHy8vK0fPly5znG1plj+VCMZGdnKzk5uc+sQGNjY5/0CpyO2267Tdu3b9c//vEPjR071rkeCAQkibGGM7Znzx41NjZqxowZSklJUUpKiqqqqvToo48qJSXFGT+MLZyp3NxcTZ48Oera17/+dWejDf7dQn/dcccduvPOO/WjH/1IF110kW644QbdfvvtKisrk8TYOhuEghhJTU3VjBkzVFlZGXW9srJSs2fPdqkqDEbGGK1atUrbtm3Tzp07VVBQEHW/oKBAgUAgaqx1dHSoqqqKsYavdPnll2vv3r2qra11XjNnztTSpUtVW1ur8ePHM7bQL3PmzOmzdfKBAwc0btw4Sfy7hf5rbW1VUlL0x9fk5GRnS1LG1llwscl5yNu6dasZNmyYefrpp82+fftMcXGxGT58uHn//ffdLg2DyK233mr8fr/ZtWuX+fjjj51Xa2ur88zGjRuN3+8327ZtM3v37jXXXnutyc3NNcFg0MXKMRj13n3IGMYW+uf11183KSkp5oEHHjAHDx40v/3tb01GRoZ57rnnnGcYW+iP5cuXm3PPPdf85S9/MYcOHTLbtm0z2dnZZu3atc4zjK3+IRTE2K9+9Sszbtw4k5qaai6++GJnG0ngdEk66euZZ55xngmHw+aee+4xgUDAeL1ec9lll5m9e/e6VzQGrRNDAWML/fXnP//ZTJkyxXi9XjNp0iTz5JNPRt1nbKE/gsGgWb16tTnvvPNMWlqaGT9+vNmwYYNpb293nmFs9Y/HGGPcnKkAAAAA4C56CgAAAIAERygAAAAAEhyhAAAAAEhwhAIAAAAgwREKAAAAgARHKAAAAAASHKEAAAAASHCEAgAAACDBEQoAAFbatWuXPB6PvvjiC7dLAYAhj1AAAAAAJDhCAQAAAJDgCAUAgJMyxmjTpk0aP3680tPTNW3aNP3hD3+Q1LO058UXX9S0adOUlpamWbNmae/evVHv8cc//lEXXnihvF6vzj//fD300ENR99vb27V27Vrl5+fL6/WqsLBQTz/9dNQze/bs0cyZM5WRkaHZs2dr//79zr233npLCxYskM/nU1ZWlmbMmKE33ngjRt8RABi6UtwuAABgp7vvvlvbtm1TRUWFCgsL9corr+j666/X6NGjnWfuuOMOPfLIIwoEAlq/fr2++93v6sCBAxo2bJj27Nmjq6++WiUlJbrmmmtUXV2tFStWaNSoUbrxxhslScuWLdNrr72mRx99VNOmTdOhQ4d05MiRqDo2bNighx56SKNHj9Ytt9yim266Sf/6178kSUuXLtX06dNVUVGh5ORk1dbWatiwYXH7HgHAUOExxhi3iwAA2KWlpUXZ2dnauXOnLr30Uuf6zTffrNbWVv34xz/WggULtHXrVl1zzTWSpM8//1xjx47Vr3/9a1199dVaunSpPv30U+3YscP579euXasXX3xRdXV1OnDggCZOnKjKykotXLiwTw27du3SggUL9Le//U2XX365JOmvf/2rFi9erLa2NqWlpSkrK0uPPfaYli9fHuPvCAAMbSwfAgD0sW/fPh09elSLFi1SZmam8/rNb36jd99913mud2AYOXKkJk6cqHfeeUeS9M4772jOnDlR7ztnzhwdPHhQXV1dqq2tVXJysubNm/eVtUydOtX5Ojc3V5LU2NgoSVqzZo1uvvlmLVy4UBs3boyqDQBw+ggFAIA+wuGwJOnFF19UbW2t89q3b5/TV3AqHo9HUndPQuTriN6T0+np6adVS+/lQJH3i9RXUlKiuro6LV68WDt37tTkyZP1wgsvnNb7AgB6EAoAAH1MnjxZXq9XH3zwgS644IKoV35+vvPc7t27na+bmpp04MABTZo0yXmPV199Nep9q6urNWHCBCUnJ+uiiy5SOBxWVVXVWdU6YcIE3X777dqxY4eWLFmiZ5555qzeDwASEY3GAIA+fD6ffv7zn+v2229XOBzW3LlzFQwGVV1drczMTI0bN06SdN9992nUqFHKycnRhg0blJ2drauuukqS9LOf/UyXXHKJ7r//fl1zzTV67bXXVF5erscff1ySdP7552v58uW66aabnEbjw4cPq7GxUVdfffV/rbGtrU133HGHfvCDH6igoED/+c9/VFNTo+9///sx+74AwFBFKAAAnNT999+vMWPGqKysTO+9955GjBihiy++WOvXr3eW72zcuFGrV6/WwYMHNW3aNG3fvl2pqamSpIsvvli///3v9Ytf/EL333+/cnNzdd999zk7D0lSRUWF1q9frxUrVuizzz7Teeedp/Xr159WfcnJyfrss8+0bNkyffLJJ8rOztaSJUt07733Dvj3AgCGOnYfAgCcscjOQE1NTRoxYoTb5QAAzhI9BQAAAECCIxQAAAAACY7lQwAAAECCY6YAAAAASHCEAgAAACDBEQoAAACABEcoAAAAABIcoQAAAABIcIQCAAAAIMERCgAAAIAERygAAAAAEtz/A42eJ0hlNW68AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['acc'] for k in training_history_lr_scheduler.keys()], label = 'training acc')\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['val_acc'] for k in training_history_lr_scheduler.keys()], label = 'val acc')\n",
    "plt.title(\"ResNet-18: Training Accuracy\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy (%)\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d66914d0-2c06-46c2-9759-907197ecc7fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['loss'] for k in training_history_lr_scheduler.keys()], label = 'training loss')\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['val_loss'] for k in training_history_lr_scheduler.keys()], label = 'val loss')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.title(\"ResNet-18: Training Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "310fc81c-2259-4d4f-a253-5058bdd2d806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['lr'] for k in training_history_lr_scheduler.keys()])\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"lr\")\n",
    "plt.title(\"ResNet-18: Learning-Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb711d1d-d352-4389-9434-ff421257919e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8fEnS3//+d91+++165513NGbMGCUmJmr8+PEHXeryhhtuUGZmpm677bYG79+1a5cKCwsVFha23/Pv3Lkz8NwH07Vr13qPu//++xv9+vjdcccd8ng8+vTTT/WrX/1Kxx13nG699VY98cQT+ve//92ocHkgERERGj9+vB599FHNmjVLa9euVW5urv75z39q2bJlkqTs7Gx17dpVn3/+ucrKyjRnzpxAmN+6datWrVqlzz//XBEREQedwOk/RmpqqiZNmqSuXbuqa9eu+tvf/rbfvklJSfW+drvdknTIN2n++8PDwxu8v6EJz/5t/nkEu3bt0vvvv7/fz7xPnz6S1Kif+0033aQ//OEPGj9+vN5//33NmzdP8+fP14ABAxr8Hvz1Hur7a+xxd+/e3eyTjy+66CK53e5Az/by5cs1f/78epPVS0pKdOyxx2revHl68MEHNXPmTM2fP19vv/12o76/A9mzZ0+D8zsyMzMD9++rqedPc/n58/tr8D9/QUGBvF5vs/6M9uzZI6fTud+bA5vNpvT09EO+Rj+vEQh1rGYDtILevXsHJr2OGTNGXq9X//rXv/Tmm2/qnHPOUXJysiTpzjvv1C9/+csGj9GzZ09JUlRUlO677z7dd9992rVrV2CU/swzz9TKlSsbfGxERITuvfde/fa3v9WHH3643/3JyclKSkrSJ5980uDjG7Ps4fvvv6/KysrA1/7wcTgWL16s3NxcRUVF1ds+bNgwSdKPP/7YbMvnderUSb/97W91ww03aNmyZYEAO3bsWL377ruaNWuWfD6fjj/+eMXExCgzM1PTp0/X559/rmOPPTYQmg7k2GOP1bHHHiuv16sFCxboH//4h2644QalpaXpggsuOOL6/efM3r17Gwx/O3fuPOA2f7hJTk5W//799ac//anB52jMz/Dll1/WpZdeqoceeqje9vz8/AYn5u7du7de/Ud63JSUlMBfGppLQkKCzjrrLE2dOlUPPvigXnjhBYWHh9d7Az5jxgxt375dM2fOrHdO/nwC5uFKSkrSjh079tvu/0vMoV63IxUeHl7v99ivMW/sGpKYmCiHw9GsP6OkpCTV1NRo9+7d9QK9MUY7d+4M/HsBtBeMzAMWeOSRR5SQkKA//vGP8vl86tmzp7p3764lS5Zo6NChDd4aCtRpaWmaOHGiLrzwQq1ateqgF1a6/PLLA6tt/LwF5owzztCePXvk9XobfG7/GwnpwCNa/fr1q/eYpoT5zMxMLVu2TCUlJfW2+9dwb8roXnFx8X7H8/O3Quxb64knnqhdu3bpiSee0FFHHRV43ceOHatp06Zp/vz5h9XS4XA4NGLEiMCKJs110aRevXpJktatW9fg/V988UW9FXi8Xq/eeOMNde3aNfA6+pcJ7dq1a4M/d//rcrDRXpvNtt8bmw8//PCAbTTr16+X3W6vd041pLHHPe2007R69er92k/21ZTR6ssuu0zbt2/XRx99pJdffllnn312vTcR/pVdfl7jM888c0TPP3bsWC1fvny/82Tq1Kmy2WwaM2ZMo7+HpujcubPy8vLqnTtVVVVNvh5DRESERo8erf/9738HfUNwuK+RVPuGb19vvfWWSktLg36pTaC5MTIPWCAhIUF33nmnbrvtNr366qu6+OKL9cwzz+i0007TKaecookTJ6pDhw7au3evVqxYoUWLFul///ufJGnEiBE644wz1L9/fyUkJGjFihX6z3/+o5EjRyoyMvKAz+lwOPTQQw/p7LPPlvRTb60kXXDBBXrllVd0+umn6/rrr9fw4cPlcrm0detWffnllzrrrLMCj+vXr59ef/11vfHGG+rSpYvCw8PVr1+/g36/y5cv1/LlyyXVjg6XlZXpzTfflCTl5uYqNzdXUm070Pjx43XSSSfpxhtvVHJysubOnavJkycrNzc30Mst1Yabyy+/XP/+978P2je/atUqnXLKKbrgggs0evRoZWRkqKCgQB9++KGeffZZHX/88fVaZk444QTZbDZ99tln9dalP/HEEzVhwoTA5wczZcoUzZgxQ+PGjVOnTp1UUVERWAqvuXq7R4wYoYiICM2dO1e/+MUv9rs/OTlZJ5xwgv7whz8oKipKTz31lFauXFlvecr7779f06dP16hRo3TdddepZ8+eqqio0MaNG/XRRx9pypQp6tixo2JiYpSdna13331XY8eOVWJiopKTkwPL/r344ovq1auX+vfvr4ULF+rRRx894BuvuXPnauDAgYe8XkBjj3vDDTfojTfe0FlnnaU77rhDw4cPV3l5uWbNmqUzzjgj0NN9oPoP5OSTT1bHjh31u9/9Tjt37tzvehCjRo1SQkKCrrrqKt1zzz1yuVx65ZVXtGTJkv2O5f/9+POf/6zTTjtNDodD/fv3b3Cp1RtvvFFTp07VuHHjdP/99ys7O1sffvihnnrqKV199dXq0aPHQV+3I3X++efrj3/8oy644ALdeuutqqio0N///nd5vd4mH/Pxxx/XMcccoxEjRuiOO+5Qt27dtGvXLr333nt65plnFBMTE5iL8OyzzyomJkbh4eHKyclpsEXmpJNO0imnnKLbb79dHo9HRx99tH744Qfdc889GjRokC655JIm1wqEJKtn4AJt2YGWpjSmdkWFTp06me7duwdWo1iyZIk577zzTGpqqnG5XCY9Pd2ccMIJZsqUKYHH3XHHHWbo0KEmISHBuN1u06VLF3PjjTea/Pz8wD77rmbzc6NGjTKS6q1mY4wx1dXV5i9/+YsZMGCACQ8PN9HR0aZXr17myiuvNGvWrAnst3HjRnPyySebmJgYI+mgK9P8vJ6Gbj9fYWTGjBnm5JNPNunp6SYiIsL06NHD3HzzzfW+v31f20MtTVlQUGAefPBBc8IJJ5gOHTqYsLAwExUVZQYOHGgefPDBwLKb+xo0aJCRZL799tvAtm3btgVWwvn5yjo/X+ljzpw55uyzzzbZ2dnG7XabpKQkM3r06Hqr5xxoNRFjTKNXjrnkkktMbm5ug4+fNGmSeeqpp0zXrl2Ny+UyvXr1qre8pt/u3bvNddddZ3JycozL5TKJiYlmyJAh5u677zYlJSWB/T7//HMzaNAg43a7jaTACiwFBQXmN7/5jUlNTTWRkZHmmGOOMV9//bUZPXr0fqseFRcXm8jISPPYY48d8ns7nOMWFBSY66+/3nTq1Mm4XC6Tmppqxo0bV28JywPV39BqNn533XVXYJlP/2pS+5o9e7YZOXKkiYyMNCkpKeaKK64wixYt2u+8rKysNFdccYVJSUkxNput3vP9fDUbY4zZtGmT+fWvf22SkpKMy+UyPXv2NI8++mi9Go70/DnY4z/66CMzcOBAExERYbp06WKefPLJA65mM2nSpP0e39D3tHz5cnPuueeapKQkExYWZjp16mQmTpxYbxnJJ554wuTk5BiHw1HvNWxohZ3y8nJz++23m+zsbONyuUxGRoa5+uqrA0u/7lvLz/+tM8Y0eB4BocpmjDGt8q4BANCsFixYoGHDhmnu3LkaMWKE1eUc0vPPP6/rr79eW7ZsOewr+QIAGkaYB4AQdv7556u0tLTBq24Gk5qaGuXm5mrChAm6++67rS4HANoMJsACQAh77LHHNGzYMBUXF1tdykFt2bJFF198sW6++WarSwGANoWReQAAACBEMTIPAAAAhCjCPAAAABCiCPMAAABAiArpi0b5fD5t375dMTExgavxAQAAAKHOGKPi4mJlZmbKbj/w+HtIh/nt27crKyvL6jIAAACAFrFly5YDXlVbCvEwHxMTI6n2m4yNjbW4GgAAAKB5eDweZWVlBfLugYR0mPe31sTGxhLmAQAA0OYcqpWcCbAAAABAiCLMAwAAACGKMA8AAACEKMI8AAAAEKII8wAAAECIIswDAAAAIYowDwAAAIQowjwAAAAQogjzAAAAQIgizAMAAAAhijAPAAAAhCjCPAAAABCiCPMAAABAiCLMAwAAACGKMA8AAACEKMI8AAAAEKII8wAAAECIIswDAAAAIYowDwAAAIQowjwAAAAQopxWFwAAoeiTH3fqX1+vV25mrMb0TNVRXZIUEeawuiwAQDtDmAeAw1Dj9enRT1fpma/WS5IWbCrQ1Dmb5HbadVSXJI3pmaIxvVKVnRS132ONMSqurFFhabU8FdWKDXcpNdatcBdvAgAATWMzxhiri2gqj8ejuLg4FRUVKTY21upyALQyY4xmrtqt7zcXKCclSn0y49QlOUpOR8t0EOYVV+jaV7/XvA17JUkXjegkSfpyZZ62F1XU27dLcpS6pUarsLxaBaVVKiirVmFZlWp8+/+TGxfhUmqMW2mx4UqNcSs1NlyZ8eHqmhKtrinRSot1y2aztcj3BAAITo3NuYzMAwg51V6f3lu8Xc9+tV6rdhXXu8/ttKtXeoxyM2OVmxmn3IxYZSVGKC7CJbez6SPgCzbu1e9eWaS84kpFhTn0yDkDNK5/hqTaNxVr8kr05co8fbkqTws2Fmh9fqnW55c2eKwIl0Mx4U4VlVerssanovJqFZVXa01eSYP7R7ud6poSVRvuU6PVNSVKI7skKy7S1eTvBwDQNjAyDyBklFbW6PX5W/T81+sDI+HRbqfG9k7VtoJyrdjhUWmV94CPD3fZFRfhqndLinKrb4dYDeqUoF7pMfuN6htj9O9vN2ryRytU4zPqlhqtKRcPUbfU6AM+T3FFtb5dm6/dxZVKiApTQmTdLcqlhMiwQFuNMUae8hrlFVdol6dSuzwVyiuu/bi1oEzrdpdq894yeRsYzY8Mc+i8oVm6/OgcdUqKbMrLCQAIYo3NuYR5AEEvv6RSL83eqKlzNqmovFqSlBzt1uXHdNZFI7IVF1E7Qu3zGW3eW6Zl2z1avqOo9uN2j3aXVKox/9JFuBzq1zFOgzslaFCnePVOj9Ujn67UBz/skCSd0T9Df/5Vf0W5W++PmpU1Xm3eU6Z1u0u0bnep1uWVaPHWQq3fXTvqb7dJp/RJ1xXHdtGQ7IRWqwsA0LII8wBCXp6nQlNmrder321SRbVPkpSTHKXfHtdFZw/q0OiJoz5f7cRTT107S2FZdaC1ZWdRuRZvLdL3mwtUXFHT4OOddpt+P663JozqHBS968YYfbM2X899vUFfrd4d2D6oU7z+79guOqVPuhx26+sEADQdYR5AyNpZVKEps9bp1e82q6qmNsQP6Binq0Z31cktFFR9PqP1+SVatKlQ328p0KJNhVqdV6zMuAj9/cKBGpKd2OzP2RxW7SzWv75er3cXb1eVt/a1yk6K1OSz+2lUt2SLqwMANBVhHkDI2V5Yrimz1un177YEgunQ7ARdf2J3HdMtudVHxcuqahThcgTFaPyh5BVXaOrsTXp53iYVltW2Il12dGfdfmovlr4EgBBEmAcQMjbvKdMzX63T/xZsDYT44Z0Tdf2J3TWqa1JIhOlgUVpZoz99tEKvztssSeqaEqW/nj9Q/TvGW1sY0MYVV1RrY36ZNuwp1Ya6yesVNV7JSEa1UcuYupuMfKb2uhXVXqNqr081vtqP1V6jGq9Pvn3iWUNBzWW3y+mwyemwy2W3yemwyeWwy2mv3bbvR4fdJpej9qPTbpfNJtlkq/so2e022STJJjlstrrH2OWwSw577THs9trtdlvt/nZb7ec220+fO+w2hTnscjnscjntcjn2+dpR+7X/OP5aHP7j+o/tP559n8/rnqe9IcwDCGrVXp8+X75Lr363WV+vyQ9sP6pLoq4f20MjuyZZWF3o+3JVnm5/8wflFVfKYbfp2hO6adKYbnIdxhr8Xp/Rqp3FWrBpr+ZvLNCiTQWqrPEpsW5VnqTo2lV6EutW7EmNdevorslKiAprwe8MsEZZVY22F1ZoR1G5dhRWaEdRhbYUlGljfqk27ilVfkmV1SW2aba6NxoO+89uNlvgDcG+b15q38DY6+1nt0tOu112u02OujcfTrtdbpdd4U6Hwl12uV0OhTvrPrpqt104rJPsFsxDIswDCEpb9pbpte82678Ltiq/pDKw/bgeKZp0fFeN6EKIby4FpVX6/bs/6sO61Xj6d4zT4+cN3G9ZzX0nCG8tKNfCfcJ7cWXDk4IPxGm36ZjuyTqjf6ZO7pOm2HDWwkfwq6rxaWdRhbYXlWtHUbm2F1Zoe2G5theWa0dRbXD3r6R1MMnRbuUkR6pzUpQ6J0cpKqy2Tc8/qFw7+l07Cm631Y6Wuxz2wKi6y1EbLp2O2vC5r31Hpo0xgZH8Gq9Rja9uRL/uo9dXO7pf4zN19+/ztc9X99cB1Y7+131ujJExktcY+Xy1j/Huc/N/7TP+W+2/HYHPjQnUUuU1qq7x1f2lobamKq8vUJfXZ+Q1tcer9gZ3DLXZpPUPnW7JXwYI8wCChtdnNH35Tr0yr/4ofEqMW+cN7agLhnVSViJrpbeU95Zs1++nLZWnokZup12DOyWouLJuRZ+yahVX1hxw6c6oMIcGZydoWOdEDc1OUHxkmArKqrSntEoFpVXaW1oV+HpdXolW7vzpIl5hDrtG90zRGf0zdGLvtFZd0hOQagOq/9oNeZ7Knz4vrtTu4tqPO4oqlN/I5Wtj3E6lx4UrIz5CmXHhyoyPUE5ylHKSo5SdFKkY3rw2if/Ng1HdG4p93iQY438ToZ/eBHj9bwZ88vqkGp+v3hsO/5sK/5sZ//H2fXPif9NS7TOqrPaqssanimpv3a3u8xqffD6jf1402JLXhTAPwHIV1V69tWirnv1qvTbtKQtsP7Z7si4a0Ulje6cdVtsHmm5nUYVufXNJvTdTPxfusispyq2BneI1LDtBQzsnNnghrYNZv7tEH/ywQ+8v2V7virbhLrv6ZsYpIswht//P2XUfw10ORYY5lBEXoeykSHVKjFRGXPhhPS/at4LSKq3PL9WG/FJtyC/Rxvwyrc8v1cb8UpVXH/hCcvsKc9rVIT5CGXHhyoiLUIf42tCeURfaM+LCCetoVYR5AJbxVFTr5bmb9O9vNgZaaRIiXbpweCddOJxReKsYYzRr9W4VlVcrNtyl2Lqr4MZGOBUX4ZLb2byr3qzaWawPftiuD37YoQ35pYf1WIfdpg7xteE+KzFSnZMi1SU5Wl1To5WVEEHQbyeMMdpeVKGN+aWBEfXaEfbaz/OLa0fbSw7SDma31f4VMDUmXKkxbqXG+j+6lRYTrrTYcGXGhysxKqxdTrJE8CLMA2h1eZ4K/fvbjXpl7qZAr3WH+AhdcWyOzh+Wpcgw2izaI2OMlu/waPOe2tU9An/Crvapsu7r0soabSss1+a9Zdq8tyxwfYGGuBw2dU6KUteUaHVNjVKX5GjlpEQpJymKybchyusz2rK3TGvzSrQmr0Rr8oq1Lq9Ea/NKVFrVuJH1jLjwQMvLvresxEj+AoiQRJgH0GqMMfrLZ6v03NcbAiGsR1q0rhrdVWcOyOQ/UhwWn6+2z9kf7DfvKdWGPWVav7tE63aXBK4G3JC4CJc6J0cpJymy9mNylNJjw/dbicL/lc0mJUSGKTM+gvX4m5kxRmVVXpVW1qi0yhu44vL2wgrt9NROMN1ZN7l0l6dCNb6G44jTblN2UqTS48IDo+spdSPsKdG1I+wZceEMFqDNIcwDaBXGGP3x3WX6z9xNkqQh2Qm6enRXndAr1ZKlvNC2+XxGOzwVWpdXG+zX7S7RurzapQF3FFUc0bGTompDfYf4CGXGRygzvrZXOi22tkUjJcbdrgJ/UXm11uaVaG1esdbsqh0x3+Wp/xr721L8v+kVNXXhvdKr0qoDT6xuiNtpV9eUaHVPi1Y3/8fUGGUnMbKO9okwD6DFGWN073vL9NKcTbLZpD//sr/OG5ZldVlop8qrvNq4p3bS4wb/x/xS7dln/e+f/4fn9Rnll1SqrJGtHLHhzp96rmPcio8MU5TboSi3U9Fup6LCnIHPo8Odig13KjbCpdhwl8KcwRVIq70+7fLUjoz7l2DcXliudbtLtGZXifKKKw99kEaw2aToMKdiwn9aCSYjNlzpdRNL0+PClRkXoZQYtxwMAAABjc25/E0KQJMYY3Tf+8t/CvK/6q/zhhLkYZ2IMId6Z8Sqd8bhDe4YY+Qpr9HWwrJ664tvqwu4ecW1yxpW1vjkqaiRp6K2l/uw63M5ApONY8NdinI75XbWruaz70e3y64Il0PR7to3AjHhLsWGO2s/RtRuC3PYVV23Xrd/fXH/euLVXp+Kyqu1t7RKhWVVKiirVkHdEqIFZdXaXVypHUXlyis+9HKMGXHh6pYare6pMeqWGq0OCRFy2Gz1r2i6z+sYXld3lNupKHft5xEuBxNLgRZEmAdw2IwxeuCDFXpx9kZJ0sO/7EeQR8iy2WyKi3QpLjJOfTLjGtzHGCNPRU3t2uR165XnFVfIU16jksqaur7wGpVU+ttMalRcUSNPRbWKK2ong5dXe1Ve7dUuT/OMeDeHMIe9drR8n+UXc5Kj1D0tRl1ToliKEQgBhHkAh8UYoz99uEL//naDJGnyL/vp/GGdLK4KaFk2m01xdUt5dkuNOazHen1GJRU1Kiqvlqei7mJd5dUqq/IGVvPZ92NltU/lVV6VVNa+GfBU1Ki47rGe8hpVefefAFx7KXubXHVXD42LcCk+MkyJUWGKj3QpYZ/Pk6LcyoyvXUs9KSqMuS1AiCPMA2g0Y4we/nil/vVNbZB/6Ox+unA4QR44GIfdP/LfPKPcFdVeVXt9cjnsctptcthttLEA7RhhHkCjGGP0509W6Zmv1kuSHhzfV78eQZAHWlu4y9GuVtUBcHDBNbUeQND674ItmjJrnSTpgbP66OKjsi2uCAAAEOYBHFJFtVePT18tSbr5pB66ZGRnawsCAACSCPMAGuGVeZu1y1OpzLhw/XZ0F6vLAQAAdQjzAA6qtLJGT89cK0m6bmx3uZ306gIAECwI8wAO6qU5G5VfUqXspEj9akhHq8sBAAD7IMwDOCBPRbWemVW7es0NJ3aXy8E/GQAABBP+ZwZwQM9/vUFF5dXqlhqtXwzoYHU5AADgZwjzABpUUFql5+suDnXTST3k4CqRAAAEHcI8gAY989V6lVTWKDcjVqf2Sbe6HAAA0ADCPID95BVX6MXZtaPyN5/cQ3ZG5QEACEqEeQD7eerLdaqo9mlgVrxO6JVqdTkAAOAACPMA6tleWK5X522WJN1yck/ZbIzKAwAQrAjzAOr5x4y1qvL6NCInUUd3S7K6HAAAcBCEeQABm/aU6n8LtkiSbmZUHgCAoEeYBxDwty/WqMZndFyPFA3PSbS6HAAAcAiEeQCSpE9+3Km3F22TJN18Ug+LqwEAAI1BmAeglTs9uum/iyVJE0d11oCseEvrAQAAjUOYB9q5gtIq/d/UBSqr8mpU1yTdPa631SUBAIBGIswD7ViN16drXlukLXvLlZUYoX/+erBcDv5ZAAAgVPC/NtCOPfTRSn27do8iwxx67tKhSogKs7okAABwGAjzQDv15sKt+ve3GyRJj507QL3SYy2uCAAAHC7CPNAOfb+5QHdNWypJum5sd53WL8PiigAAQFMQ5oF2ZpenQlf+Z6Gqanw6KTdNN4ztbnVJAACgiQjzQDtSUe3Vlf9ZqLziSnVPjdbj5w2Q3c5VXgEACFWEeaAd+dsXa7R4S6Fiw5167tKhigl3WV0SAAA4AoR5oJ3Y5anQC3UTXh85Z4A6J0dZXBEAADhShHmgnXhyxlpVVPs0JDtBp/RJs7ocAADQDAjzQDuwZW+ZXp+/WZJ06yk9ZbPRJw8AQFtAmAfagSc+X6Nqr9Gx3ZN1VJckq8sBAADNhDAPtHFr84o17futkqRbTu5pcTUAAKA5EeaBNu7x6avlM9LJuWkakBVvdTkAAKAZEeaBNmzp1iJ9tHSnbDbpZkblAQBocwjzQBv2l89WSZLGD+ygnukxFlcDAACaG2EeaKO+27BXs1bvltNu0w0ndre6HAAA0AII80AbZIzRXz6tHZU/b1iWspO4QBQAAG0RYR5og75ak6/vNu5VmNOua0/oZnU5AACghRDmgTbGGKNHP10pSbr0qGxlxEVYXBEAAGgphHmgjfnkx536cZtHUWEOXX18V6vLAQAALYgwD7QhNV6fHpu+WpL0m2NylBTttrgiAADQkgjzQBvy6nebtTavRPGRLl1xXBerywEAAC2MMA+0EXtKKgMr2Nx8Ug/FhrssrggAALQ0wjzQRvzls1XyVNSod0asfj0i2+pyAABAKyDMA23AD1sL9fr8LZKk+8/qI4fdZnFFAACgNVge5rdt26aLL75YSUlJioyM1MCBA7Vw4UKrywJChs9n9Md3l8kYafzATA3rnGh1SQAAoJU4rXzygoICHX300RozZow+/vhjpaamat26dYqPj7eyLCCkvLVoqxZvKVRUmEN3nt7b6nIAAEArsjTM//nPf1ZWVpZeeOGFwLbOnTtbVxAQYjwV1frzJ7UXiLpubHelxYZbXBEAAGhNlrbZvPfeexo6dKjOPfdcpaamatCgQXruuecOuH9lZaU8Hk+9G9CePTF9jfJLqtQlJUqXHZ1jdTkAAKCVWRrm169fr6efflrdu3fXp59+qquuukrXXXedpk6d2uD+kydPVlxcXOCWlZXVyhUDwWP1rmK9NGejJOneM/sozGn5FBgAANDKbMYYY9WTh4WFaejQoZo9e3Zg23XXXaf58+drzpw5++1fWVmpysrKwNcej0dZWVkqKipSbGxsq9QMBANjjC761zzNXrdHJ+em6dlLh1pdEgAAaEYej0dxcXGHzLmWDuVlZGQoNze33rbevXtr8+bNDe7vdrsVGxtb7wa0Rx8t3anZ6/bI7bTrD2fkHvoBAACgTbI0zB999NFatWpVvW2rV69WdjYXvAEOpKyqRn/6cLkk6arRXZWVGGlxRQAAwCqWhvkbb7xRc+fO1UMPPaS1a9fq1Vdf1bPPPqtJkyZZWRYQ1J77aoO2F1WoQ3yErj6+q9XlAAAAC1ka5ocNG6Zp06bptddeU9++ffXAAw/oiSee0EUXXWRlWUDQMsbovwtqr/R66yk9Fe5yWFwRAACwkqXrzEvSGWecoTPOOMPqMoCQ8P2WQm0rLFdUmEOn9k23uhwAAGAx1rIDQsj7S7ZLkk7MTWNUHgAAEOaBUOHzGX20dIck6cz+mRZXAwAAggFhHggR8zfu1S5PpWLCnTq2R7LV5QAAgCBAmAdCxAc/1I7Kn9InXW4nLTYAAIAwD4SEGq9PH/9YG+bP6J9hcTUAACBYEOaBEDBvw17ll1QpIdKlo7vRYgMAAGoR5oEQ8MEPtavYnNo3XS4Hv7YAAKAWqQAIctVenz7+cack6QxWsQEAAPsgzANB7tu1+Sosq1ZydJhG5CRaXQ4AAAgihHkgyL2/pHbi6+n9MuSkxQYAAOyDZAAEscoarz5bTosNAABoGGEeCGJfrc5XcUWN0mPDNTQ7wepyAABAkCHMA0HMv4rN6f0yZLfbLK4GAAAEG8I8EKQqqr36fPkuSdIZA7hQFAAA2B9hHghSX67MU2mVVx3iIzQoK97qcgAAQBAizANB6oMfalexOaN/hmw2WmwAAMD+CPNAECqtrNEXK+tabFjFBgAAHABhHghCX6zMU0W1T52TItW3Q6zV5QAAgCBFmAeC0AdLalexOaN/Ji02AADggAjzQJAprqjWzFW7JbGKDQAAODjCPBBkZq3erSqvT11SotQzLcbqcgAAQBAjzANBZsbKPEnSib3TaLEBAAAHRZgHgojPZzSrrsVmTM9Ui6sBAADBjjAPBJElWwu1p7RKMeFODe2cYHU5AAAgyBHmgSDyZV2LzXHdU+Ry8OsJAAAOjrQABJEZq2rD/JhetNgAAIBDI8wDQSLPU6Eft3kkScf3TLG4GgAAEAoI80CQ+LJuVH5AxzglR7strgYAAIQCwjwQJPxLUtJiAwAAGoswDwSByhqvvlmTL0k6gTAPAAAaiTAPBIEFGwtUWuVVcrRbfTPjrC4HAACECMI8EAQCLTY9U2S3c9VXAADQOIR5IAj415enxQYAABwOwjxgsY35pVqfXyqn3aZjuidbXQ4AAAghhHnAYv4Wm2GdExUT7rK4GgAAEEoI84DF/OvL02IDAAAOF2EesFBpZY3mrd8rifXlAQDA4SPMAxb6Zm2+qrw+dUqMVNeUKKvLAQAAIYYwD1ho31VsbDaWpAQAAIeHMA9YxBgT6JenxQYAADQFYR6wyPIdHu3yVCrC5dCInESrywEAACGIMA9YxN9ic3S3JIW7HBZXAwAAQhFhHrCIf315WmwAAEBTEeYBC+wtrdL3WwolSWN6EuYBAEDTEOYBC8xanSdjpF7pMcqMj7C6HAAAEKII84AFvly5WxJXfQUAAEeGMA+0MmOMvl2bL0ka3SPF4moAAEAoI8wDrWz1rhLtKa1SuMuuQZ0SrC4HAACEMMI80Mpmr6sdlR/WOVFhTn4FAQBA05EkgFY2e90eSdLIrkkWVwIAAEIdYR5oRV6f0bz1tWF+VNdki6sBAAChjjAPtKLl2z3yVNQo2u1U38xYq8sBAAAhjjAPtKI562v75UfkJMrp4NcPAAAcGdIE0IrolwcAAM2JMA+0kmqvT99t2CuJMA8AAJoHYR5oJT9sLVJZlVfxkS71TqdfHgAAHDnCPNBK5tStLz+yS5LsdpvF1QAAgLaAMA+0kjnr6ZcHAADNizAPtIKKaq8WbCyQJI0izAMAgGZCmAdawfebC1VZ41NKjFtdU6KtLgcAALQRhHmgFQRabLokyWajXx4AADQPwjzQCvyTX2mxAQAAzYkwD7SwsqoaLd5SKEka1TXZ2mIAAECbQpgHWtiCjQWq9hp1iI9QVmKE1eUAAIA2hDAPtLDZ635akpJ+eQAA0JwI80AL23fyKwAAQHMizAMtyFNRraVbCyVxsSgAAND8CPNAC/pu/V75jJSTHKXMePrlAQBA8yLMAy3I32JzFC02AACgBRDmgRbkn/zK+vIAAKAlEOaBFrK3tEordngkMTIPAABaBmEeaCHz6lpseqRFKyXGbXE1AACgLSLMAy3kpxYbrvoKAABaBmEeaCGB9eXplwcAAC2EMA+0gDxPhdbmlchmk47KIcwDAICWQZgHWoB/VD43I1ZxkS6LqwEAAG0VYR5oAd+uzZckHd2NfnkAANByCPNAMzPG6Nu1tSPzhHkAANCSCPNAM9u0p0zbCsvlctg0rHOC1eUAAIA2jDAPNLNv19W22AzqlKDIMKfF1QAAgLaMMA80s9n+FhvWlwcAAC2MMA80I5/PaPY6/+RXlqQEAAAtizAPNKMVOz0qKKtWVJhDA7LirS4HAAC0cYR5oBn5W2yG5yTK5eDXCwAAtCzSBtCMvmF9eQAA0IoI80Azqarx6bsNeyUR5gEAQOsgzAPNZPGWQpVXe5UUFaaeaTFWlwMAANoBwjzQTL6ta7EZ2TVJdrvN4moAAEB7QJgHmslPS1LSYgMAAFoHYR5oBqWVNfp+c6EkLhYFAABaD2EeaAbfbdyrGp9Rx4QIdUqKtLocAADQTlga5u+9917ZbLZ6t/T0dCtLAppktn9JSkblAQBAK3JaXUCfPn30+eefB752OBwWVgM0zTd1F4sa1S3J4koAAEB7YnmYdzqdjMYjpO0pqdSKHR5J0ihG5gEAQCuyvGd+zZo1yszMVE5Oji644AKtX7/+gPtWVlbK4/HUuwFWm7O+dlS+V3qMUmLcFlcDAADaE0vD/IgRIzR16lR9+umneu6557Rz506NGjVKe/bsaXD/yZMnKy4uLnDLyspq5YqB/X3rb7FhVB4AALQymzHGWF2EX2lpqbp27arbbrtNN9100373V1ZWqrKyMvC1x+NRVlaWioqKFBsb25qlAgGjH/1Sm/aU6fkJQzW2d5rV5QAAgDbA4/EoLi7ukDnX8p75fUVFRalfv35as2ZNg/e73W653bQxIHhsLSjTpj1lcthtGp6TaHU5AACgnbG8Z35flZWVWrFihTIyMqwuBWiU2XUtNgM6xikm3GVxNQAAoL2xNMzfcsstmjVrljZs2KB58+bpnHPOkcfj0YQJE6wsC2i0b/zry3ejXx4AALQ+S9tstm7dqgsvvFD5+flKSUnRUUcdpblz5yo7O9vKsoBGMcZo9rrakXnCPAAAsIKlYf7111+38umBI7J6V4nySyoV7rJrUKd4q8sBAADtUFD1zAOh5Nu6FpthnRPldnLlYgAA0PoI80ATzV5HvzwAALAWYR5oAq/PaN6GvZKkUV2TLK4GAAC0V4R5oAlW7PCouKJGMW6n+mTGWV0OAABopwjzQBPMXV+7is3Qzgly2G0WVwMAANorwjzQBP4WmxFdaLEBAADWIcwDh8nnM5q/sS7M5yRaXA0AAGjPCPPAYVqdV6zCsmpFhjnUtwP98gAAwDqEeeAwzVtfOyo/JDtBLge/QgAAwDokEeAwzdtQO/mVFhsAAGA1wjxwGIwx+o7JrwAAIEgQ5oHDsG53ifJLquR22tW/I/3yAADAWoR54DD4l6Qc3ClBbqfD4moAAEB7R5gHDoN/8uuILvTLAwAA6xHmgUYyxgQmvw5n8isAAAgChHmgkTbtKdMuT6XCHHYN7pRgdTkAAACEeaCx/KPyA7LiFO6iXx4AAFiPMA80UqBfPoclKQEAQHAgzAONNG8Dk18BAEBwIcwDjbC1oEzbCsvltNs0JJt+eQAAEBwI80Aj+Fts+nWMU2SY0+JqAAAAahHmgUZgSUoAABCMCPNAI/j75Y9i8isAAAgihHngEHYWVWjTnjLZbdLQzvTLAwCA4EGYBw7B32LTJzNOMeEui6sBAAD4CWEeOITAkpT0ywMAgCBDmAcOYd762pH5EV3olwcAAMGFMA8cxO7iSq3bXSqbTRremZF5AAAQXAjzwEF8V9di0zMtRnGR9MsDAIDgQpgHDsI/+fUoWmwAAEAQIswDB+G/8iuTXwEAQDAizAMHUFBapVW7iiVx5VcAABCcCPPAAXy3sXZUvntqtJKi3RZXAwAAsD/CPHAA8+smvzIqDwAAghVhHjiABZsKJEnDWJISAAAEKcI80IDyKq9+3FYkSRraOcHiagAAABpGmAcasHhLoWp8Rhlx4eoQH2F1OQAAAA0izAMNWFA3+XVo50TZbDaLqwEAAGgYYR5owPxAvzwtNgAAIHgR5oGf8fqMFtWF+SHZhHkAABC8CPPAz6zc6VFJZY2i3U71So+1uhwAAIADIswDP7OwblR+cHaCHHb65QEAQPAizAM/M39jXb88LTYAACDIEeaBfRhjAld+HcrFogAAQJAjzAP72FZYrp2eCjntNg3Mire6HAAAgIMizAP7WFDXYtO3Q5wiwhwWVwMAAHBwhHlgH/P9F4uiXx4AAIQAwjywD//IPP3yAAAgFBDmgTpFZdVanVcsSRrKlV8BAEAIIMwDdRZtLpAxUpfkKCVHu60uBwAA4JAI80CdQL88o/IAACBEEOaBOvTLAwCAUEOYByRV1ni1eGuhJGkYYR4AAIQIwjwg6cdtRaqq8SkpKkydkyKtLgcAAKBRCPOApPmBFpsE2Ww2i6sBAABoHMI8IGlB3eRXWmwAAEAoIcyj3fP5jBZuYvIrAAAIPYR5tHvr80tUUFatcJddfTJjrS4HAACg0QjzaPf8/fKDshLkcvArAQAAQgfJBe3e/EC/PBeLAgAAoYUwj3bPf7GoIfTLAwCAEEOYR7u2y1OhzXvLZLdJgzvFW10OAADAYSHMo13zj8r3So9VTLjL4moAAAAOD2Ee7dqCTfTLAwCA0EWYR7u2YCPrywMAgNB12GG+urpal112mdavX98S9QCtpqi8Wsu2F0niyq8AACA0HXaYd7lcmjZtWkvUArSqOevy5TNS15QopceFW10OAADAYWtSm83ZZ5+td955p5lLAVrX12vyJUnHdk+xuBIAAICmcTblQd26ddMDDzyg2bNna8iQIYqKiqp3/3XXXdcsxQEt6Zu1tWH+mG7JFlcCAADQNDZjjDncB+Xk5Bz4gDZbq/XTezwexcXFqaioSLGxsa3ynGgbtuwt07GPfCmn3abF95ysaHeT3tcCAAC0iMbm3CYlmA0bNjS5MCAY+FtsBnWKJ8gDAICQ1egUc9NNNzVqP5vNpscee6zJBQGt4Zu1uyVJx3SjXx4AAISuRof577//vlH72Wy2JhcDtAavz+jbtXskScd0T7K4GgAAgKZrdJj/8ssvW7IOoNX8uK1IReXVinE7NaBjvNXlAAAANBlXgEW741/F5qiuSXI6+BUAAAChiySDdufrNbX98sd2Z0lKAAAQ2gjzaFfKqmq0cFOBJNaXBwAAoY8wj3Zl3oa9qvYadYiPUE5y1KEfAAAAEMQI82hXvlnz01VfWXkJAACEOsI82pVAmKdfHgAAtAGEebQbeZ4KrdpVLJtNOpp+eQAA0AYQ5tFu+Jek7JMZq8SoMIurAQAAOHKEebQbP/XLp1hcCQAAQPMgzKNdMMYERuZZXx4AALQVhHm0C6t3lSivuFJup11DshOsLgcAAKBZEObRLviv+jo8J1HhLofF1QAAADQPwjzaBVpsAABAW0SYR5tXWePVvPV7JTH5FQAAtC2EebR5izYVqrzaq+ToMPVKj7G6HAAAgGZDmEeb983a2n75o7sly263WVwNAABA8yHMo837aX15+uUBAEDbEjRhfvLkybLZbLrhhhusLgVtSGFZlX7YViRJOrY7/fIAAKBtCYowP3/+fD377LPq37+/1aWgjZm9bo+MkbqlRis9LtzqcgAAAJqV5WG+pKREF110kZ577jklJHAxHzSv7zbUrmJzdNckiysBAABofpaH+UmTJmncuHE68cQTD7lvZWWlPB5PvRtwMEvrWmwGdoq3thAAAIAW4LTyyV9//XUtWrRI8+fPb9T+kydP1n333dfCVaGtqPH6tGx7bZjv1yHe2mIAAABagGUj81u2bNH111+vl19+WeHhjetlvvPOO1VUVBS4bdmypYWrRChbk1eiimqfot1OdUmOsrocAACAZmfZyPzChQuVl5enIUOGBLZ5vV599dVXevLJJ1VZWSmHw1HvMW63W263u7VLRYhaurV2VL5vh1jWlwcAAG2SZWF+7NixWrp0ab1tl112mXr16qXbb799vyAPHK4lWwslSf07xltaBwAAQEuxLMzHxMSob9++9bZFRUUpKSlpv+1AU/gnv/bvGGdxJQAAAC3D8tVsgJZQWePVih21qx31Z/IrAABooyxdzebnZs6caXUJaCNW7SxWtdcoPtKlrMQIq8sBAABoEYzMo036Yat/Sco42WxMfgUAAG0TYR5tkn8lG/rlAQBAW0aYR5vkX8mGi0UBAIC2jDCPNqe8yqs1eSWSpAFZjMwDAIC2izCPNmf5Do+8PqPkaLfSYxt3dWEAAIBQRJhHm/ND4GJRTH4FAABtG2EebQ6TXwEAQHtBmEeb8wNXfgUAAO0EYR5tSklljdbtrp38yko2AACgrSPMo035cVuRjJEy48KVEuO2uhwAAIAWRZhHm+Lvl+9Hiw0AAGgHCPNoU5YEVrKJt7QOAACA1kCYR5uylMmvAACgHSHMo80oKqvWpj1lkqR+HQjzAACg7SPMo834YVuhJCk7KVLxkWHWFgMAANAKCPNoM37wT35lVB4AALQThHm0GVz5FQAAtDeEebQZP7CSDQAAaGcI82gTdhdXantRhWw2qU9mrNXlAAAAtArCPNqEH+uWpOySHKWYcJfF1QAAALQOwjzaBP/FogbQYgMAANoRwjzaBP/k135MfgUAAO0IYR4hzxijH7jyKwAAaIcI8wh5Oz0V2l1cKYfdptwMwjwAAGg/CPMIef6LRXVPjVZEmMPiagAAAFoPYR4hj4tFAQCA9oowj5C3hItFAQCAdoowj5BmjNFSJr8CAIB2ijCPkLZud6kKy6oV5rSrZ3qM1eUAAAC0KsI8QtqcdfmSpCGdEuR2MvkVAAC0L4R5hLQ56/dIkkZ1TbK4EgAAgNZHmEfI8vmM5qyrC/PdCPMAAKD9IcwjZK3aVayCsmpFhjlYyQYAALRLhHmErNl1o/LDOifK5eBUBgAA7Q8JCCHL32Izkn55AADQThHmEZK8PqN5G5j8CgAA2jfCPELSsu1FKq6oUUy4U30yuVgUAABonwjzCEn+fvkROUly2G0WVwMAAGANwjxCkj/M02IDAADaM8I8Qk5VjU8LNu6VxORXAADQvhHmEXJ+2FqosiqvEqPC1DMtxupyAAAALEOYR8jxL0l5VJdE2emXBwAA7RhhHiFndmB9+WSLKwEAALAWYR4hpaLaq4WbCyQx+RUAAIAwj5CyaHOBqmp8So1xq0tylNXlAAAAWIowj5AyZ58lKW02+uUBAED7RphHSJkT6JenxQYAAIAwj5BRWlmjxVsKJUmjmPwKAABAmEfoWLCpQDU+o44JEcpKjLS6HAAAAMsR5hEyZq/LlySN7EKLDQAAgESYRwiZ65/82o0wDwAAIBHmESI8FdVauq1IkjSyC/3yAAAAEmEeIeK79XvlM1KX5Cilx4VbXQ4AAEBQIMwjJMyua7E5iiUpAQAAAgjzCAn+ya+jCPMAAAABhHkEvb2lVVq5s1iSdBQr2QAAAAQQ5hH05q6vbbHpmRaj5Gi3xdUAAAAED8I8gp4/zI+kxQYAAKAewjyC3oKNBZKk4TmJFlcCAAAQXAjzCGollTVaudMjSRqSnWBxNQAAAMGFMI+gtmRLoXxG6hAfobRY1pcHAADYF2EeQW3hptoWG0blAQAA9keYR1AjzAMAABwYYR5By+czWrSZMA8AAHAghHkErbW7S1RcUaMIl0O90mOsLgcAACDoEOYRtPwtNgOz4uV0cKoCAAD8HAkJQYt+eQAAgIMjzCNoLSLMAwAAHBRhHkFpb2mV1ueXSpIGdYq3thgAAIAgRZhHUPKPyndLjVZ8ZJjF1QAAAAQnwjyC0kL/kpSdaLEBAAA4EMI8ghKTXwEAAA6NMI+gU+31acmWQknSYMI8AADAARHmEXSWb/eossan+EiXuiRHWV0OAABA0CLMI+j4W2wGd0qQ3W6zuBoAAIDgRZhH0AlMfqXFBgAA4KAI8wg6i/YZmQcAAMCBEeYRVLYXlmtHUYUcdpsGZMVZXQ4AAEBQI8wjqPj75XMzYhUZ5rS4GgAAgOBGmEdQYX15AACAxiPMI6gsqpv8yvryAAAAh0aYR9Aoq6rRsu0eSYzMAwAANAZhHkHjh61F8vqM0mPDlRkXbnU5AAAAQY8wj6Cxb7+8zcbFogAAAA6FMI+gEVhfnhYbAACARiHMIygYY7jyKwAAwGEizCMorM8vVWFZtdxOu3IzYq0uBwAAICQQ5hEU/P3yAzrGK8zJaQkAANAYpCYEBfrlAQAADh9hHkGBK78CAAAcPsI8LFdQWqU1eSWSpMGd4q0tBgAAIIQQ5mG5/y3cIknKzYhVUrTb4moAAABCh6Vh/umnn1b//v0VGxur2NhYjRw5Uh9//LGVJaGV1Xh9emn2JknShFHZFlcDAAAQWiwN8x07dtTDDz+sBQsWaMGCBTrhhBN01llnadmyZVaWhVY0ffkubSssV0KkS2cN7GB1OQAAACHFaeWTn3nmmfW+/tOf/qSnn35ac+fOVZ8+ffbbv7KyUpWVlYGvPR5Pi9eIlvXCtxslSReNyFa4y2FtMQAAACEmaHrmvV6vXn/9dZWWlmrkyJEN7jN58mTFxcUFbllZWa1cJZrTj9uK9N3GvXLabbpkJC02AAAAh8vyML906VJFR0fL7Xbrqquu0rRp05Sbm9vgvnfeeaeKiooCty1btrRytWhO/lH50/tlKC023NpiAAAAQpClbTaS1LNnTy1evFiFhYV66623NGHCBM2aNavBQO92u+V2s9pJW7C7uFLvL9kuSbrs6M7WFgMAABCiLA/zYWFh6tatmyRp6NChmj9/vv72t7/pmWeesbgytKRX5m1SldengVnxGtSJC0UBAAA0heVtNj9njKk3yRVtT2WNVy/P3SyJUXkAAIAjYenI/F133aXTTjtNWVlZKi4u1uuvv66ZM2fqk08+sbIstLAPf9ih/JJKpcW6dXq/DKvLAQAACFmWhvldu3bpkksu0Y4dOxQXF6f+/fvrk08+0UknnWRlWWhBxpjAxNdLR3aWyxF0fxwCAAAIGZaG+eeff97Kp4cFFm4q0NJtRXI77bpweCerywEAAAhpDIuiVflH5ccP7KDEqDBriwEAAAhxhHm0mm2F5fpk2U5J0mXHdLa2GAAAgDaAMI9WM3XORnl9RiO7JKlXeqzV5QAAAIQ8wjxaRVlVjV7/rvaKvZcfk2NxNQAAAG0DYR6tYtr321RUXq1OiZE6oVeq1eUAAAC0CYR5tIpX59VeJOrSkdly2G0WVwMAANA2EObR4lbs8GjZdo9cDpt+Nbij1eUAAAC0GYR5tLi3Fm6VJI3tlaYElqMEAABoNoR5tKhqr0/vLN4mSTpnCKPyAAAAzYkwjxb11erdyi+pUlJUmEb3TLG6HAAAgDaFMI8W9WZdi834QR3kcnC6AQAANCfSFVpMQWmVvliRJ0lMfAUAAGgBhHm0mPd/2K4qr0+5GbHKzeSKrwAAAM2NMI8W41/F5ldMfAUAAGgRhHm0iDW7irVka5GcdpvOGphpdTkAAABtEmEeLeLNRbWj8sf3TFVytNviagAAANomwjyaXY3Xp2mL/GvLd7C4GgAAgLaLMI9m983afOUVVyoh0qUTeqVZXQ4AAECbRZhHs3urblT+FwMyFebkFAMAAGgpJC00q6Lyan26bKckVrEBAABoaYR5NKsPf9ihqhqfeqRFq1+HOKvLAQAAaNMI82hWby7cIqn2iq82m83iagAAANo2wjyazfrdJVq0uVB2m3T2IFaxAQAAaGmEeTSbt+rWlj+uR4pSY8MtrgYAAKDtI8yjWXh9Rm8H1pZn4isAAEBrIMyjWcxclacdRRWKDXfqxN6sLQ8AANAaCPM4YsYY/WPGWknShcM7KdzlsLgiAACA9oEwjyP2zdp8Ld5SqHCXXVcc28XqcgAAANoNwjyO2D+++GlUPiXGbXE1AAAA7QdhHkdk7vo9+m7jXoU57LryuK5WlwMAANCuEOZxRP4xY40k6dyhHZUex3KUAAAArYkwjyZbuKlA367dI6fdpquPZ1QeAACgtRHm0WT+UflfDu6gjgmRFlcDAADQ/hDm0SRLtxZp5qrdstuk3x3fzepyAAAA2iXCPJrEPyp/1sAO6pwcZXE1AAAA7RNhHodtxQ6PPlu+SzabNGkMo/IAAABWIczjsD35Ze268qf3y1C31GiLqwEAAGi/CPM4LGvzivXR0h2SpGsYlQcAALAUYR6H5Z9frpMx0km5aeqdEWt1OQAAAO0aYR6NtjG/VO8u3iZJuu6E7hZXAwAAAMI8Gm3KrHXyGen4ninq1zHO6nIAAADaPcI8GqWi2qv3l2yXJF09mqu9AgAABAPCPBrlq9W7VVrlVUZcuIZ1TrS6HAAAAIgwj0b6+MedkqTT+mbIbrdZXA0AAAAkwjwaobLGq8+X75Iknd4v3eJqAAAA4EeYxyF9syZfxZU1Sot1a3CnBKvLAQAAQB3CPA7po6W02AAAAAQjwjwOqqrGp+nLa8P86f0yLK4GAAAA+yLM46C+XZcvT0WNUmLcGpJNiw0AAEAwIczjoD76YYck6dQ+6XLQYgMAABBUCPM4oGqvT58FVrGhxQYAACDYEOZxQLPX7VFRebWSo8M0PIcLRQEAAAQbwjwO6OOltS02p9BiAwAAEJQI82hQjdenT5exig0AAEAwI8yjQXPX71VBWbUSo8I0ghYbAACAoESYR4M++tHfYpMmp4PTBAAAIBiR0rCfGq9Pn/7401VfAQAAEJwI89jPdxv3ak9pleIjXRrZNcnqcgAAAHAAhHns56O6VWxOzk2TixYbAACAoEVSQz1en9EnP3KhKAAAgFBAmEc9CzbuVX5JpWLDnRrVNdnqcgAAAHAQhHnUE2ix6ZOuMCenBwAAQDAjrSHA5zP6+Ef/haLSLa4GAAAAh0KYR8Db329TXnGlYtxOHd2NFhsAAIBg57S6AFjPGKNnv1qvyR+vlCSdNyxLbqfD4qoAAABwKIT5dq7G69O97y/Ty3M3S5Imjuqsu07vbXFVAAAAaAzCfDtWWlmja1/7XjNW5slmk34/Lle/OSbH6rIAAADQSIT5diqvuEKXvzhfP27zyO20628XDNSpfVlXHgAAIJQQ5tuhNbuKNfGF+dpWWK7EqDD9a8JQDe6UYHVZAAAAOEyE+XZm7vo9+r+pC1RcUaOc5Ci9MHGYOidHWV0WAAAAmoAw344s216ky1+cr7Iqr4ZmJ+jZS4cqMSrM6rIAAADQRIT5diLPU6ErXlqgsiqvju6WpOcnDFO4i+UnAQAAQhkXjWoHyqu8+r+pC7SjqEJdU6L01EVDCPIAAABtAGG+jfP5jG7+32It2VqkhEiX/j1xmOIiXFaXBQAAgGZAmG/j/vr5an20dKdcDpumXDxE2UlMdgUAAGgrCPNt2LTvt+ofM9ZKkh46u59GdEmyuCIAAAA0J8J8G7Vg417d/uZSSdLVx3fVuUOzLK4IAAAAzY0w3wZt3lOm3/5noaq8Pp3SJ023ntzT6pIAAADQAgjzbYynolq/eWm+9pZWqW+HWP31/IGy221WlwUAAIAWQJhvQ3w+oxtfX6w1eSVKi3XrX5cOU2QYlxIAAABoqwjzbcjfvlijL1bmKcxp13OXDlV6XLjVJQEAAKAFEebbiM+W7dTfvlgjqXblmv4d460tCAAAAC2OMN8GrM0r0U3/XSJJmjiqs84Z0tHiigAAANAaCPNBqKisWqt2FsvrM4fc11NRrd/+Z4FKKms0PCdRd4/r3QoVAgAAIBgwO9JiecUVWrbdo2XbivTjNo9+3F6krQXlkqSeaTG6a1xvje6R0uBjfT6jm95YovW7S5URF65//nqwXA7enwEAALQXhHkLrNpZrKdnrtXsdXuUV1zZ4D5hDrtW7SrWhH9/p2O7J+vO03orNzO23j5/n7FGn6/YpTCnXVMuHqKUGHdrlA8AAIAgQZhvRWvzSvS3L9bogx+2y9R10NhsUteUaPXJjFXfzDj16RCrPhlxMjJ6csZavTRno75ek69v1n6tcwZ31M0n91R6XLimL9+lJz6vnfD6p/F9NSAr3rpvDAAAAJawGWMO3ZgdpDwej+Li4lRUVKTY2NhDP8AiG/NL9fcv1uidxdvkb4M/vV+6JozsrH4d4w66FvzmPWV65NOV+uCHHZKkcJddF4/I1hvzt6i4skaXjszW/Wf1bY1vAwAAAK2ksTmXMN+Ctuwt09+/WKO3v98WmMx6Um6abjyxx34tM4eyaHOBHvpwhRZsKghsG945Ua/83wj65AEAANqYxuZc2mxayLuLt+nm/y5RTV2IP6FXqm48sYf6dYxr0vEGd0rQ/64aqU+X7dRjn62WJP3zIia8AgAAtGeE+RZQUe3Vgx+uUI3PaFTXJN1ySk8N7pRwxMe12Ww6tW+GTu2bIWOMbDZbM1QLAACAUGXpsO7kyZM1bNgwxcTEKDU1VePHj9eqVausLKlZvL1om3YXVyojLlwvXja8WYL8zxHkAQAAYGmYnzVrliZNmqS5c+dq+vTpqqmp0cknn6zS0lIryzoiXp/RM1+tkyRdcWwXhTlpgwEAAEDLsLTN5pNPPqn39QsvvKDU1FQtXLhQxx13nEVVHZmPf9yhTXvKFB/p0gXDsqwuBwAAAG1YUPXMFxUVSZISExMbvL+yslKVlT9dZMnj8bRKXY1ljNHTM2tH5SeM7Kwod1C9vAAAAGhjgqYHxBijm266Scccc4z69m143fTJkycrLi4ucMvKCq6R76/X5GvZdo8iXA5NGNXZ6nIAAADQxgVNmL/mmmv0ww8/6LXXXjvgPnfeeaeKiooCty1btrRihYc2ZVbtqPwFw7OUGBVmcTUAAABo64KiD+Taa6/Ve++9p6+++kodO3Y84H5ut1tut7sVK2u8xVsKNXvdHjntNl1xbBerywEAAEA7YGmYN8bo2muv1bRp0zRz5kzl5ORYWc4RmVLXK3/WwA7qEB9hcTUAAABoDywN85MmTdKrr76qd999VzExMdq5c6ckKS4uThERoROI1+aV6NPltbVfNZpReQAAALQOS3vmn376aRUVFen4449XRkZG4PbGG29YWdZhe/ardTJGOik3Td3TYqwuBwAAAO2E5W02oW5HUbmmfb9NknT18V0trgYAAADtSdCsZhOqnv96g6q9RiNyEjW4U4LV5QAAAKAdIcwfgcKyKr363WZJjMoDAACg9RHmj8DUOZtUVuVV74xYje6RYnU5AAAAaGcI801UVlWjF77dIKl2BRubzWZxRQAAAGhvCPNNNHPVbhWUVSsrMULj+mVYXQ4AAADaoaC4AmwoOr1fht6ddLQKy6vldPCeCAAAAK2PMH8EBmTFW10CAAAA2jGGlAEAAIAQRZgHAAAAQhRhHgAAAAhRhHkAAAAgRBHmAQAAgBBFmAcAAABCFGEeAAAACFGEeQAAACBEEeYBAACAEEWYBwAAAEIUYR4AAAAIUYR5AAAAIEQR5gEAAIAQRZgHAAAAQhRhHgAAAAhRhHkAAAAgRBHmAQAAgBBFmAcAAABCFGEeAAAACFGEeQAAACBEEeYBAACAEOW0uoAjYYyRJHk8HosrAQAAAJqPP9/68+6BhHSYLy4uliRlZWVZXAkAAADQ/IqLixUXF3fA+23mUHE/iPl8Pm3fvl0xMTGy2WzNfnyPx6OsrCxt2bJFsbGxzX58tF+cW2gpnFtoKZxbaCmcWw0zxqi4uFiZmZmy2w/cGR/SI/N2u10dO3Zs8eeJjY3l5EKL4NxCS+HcQkvh3EJL4dza38FG5P2YAAsAAACEKMI8AAAAEKII8wfhdrt1zz33yO12W10K2hjOLbQUzi20FM4ttBTOrSMT0hNgAQAAgPaMkXkAAAAgRBHmAQAAgBBFmAcAAABCFGEeAAAACFGE+YN46qmnlJOTo/DwcA0ZMkRff/211SUhhEyePFnDhg1TTEyMUlNTNX78eK1atarePsYY3XvvvcrMzFRERISOP/54LVu2zKKKEaomT54sm82mG264IbCNcwtNtW3bNl188cVKSkpSZGSkBg4cqIULFwbu59xCU9TU1Oj3v/+9cnJyFBERoS5duuj++++Xz+cL7MO51TSE+QN44403dMMNN+juu+/W999/r2OPPVannXaaNm/ebHVpCBGzZs3SpEmTNHfuXE2fPl01NTU6+eSTVVpaGtjnkUce0eOPP64nn3xS8+fPV3p6uk466SQVFxdbWDlCyfz58/Xss8+qf//+9bZzbqEpCgoKdPTRR8vlcunjjz/W8uXL9dhjjyk+Pj6wD+cWmuLPf/6zpkyZoieffFIrVqzQI488okcffVT/+Mc/AvtwbjWRQYOGDx9urrrqqnrbevXqZe644w6LKkKoy8vLM5LMrFmzjDHG+Hw+k56ebh5++OHAPhUVFSYuLs5MmTLFqjIRQoqLi0337t3N9OnTzejRo831119vjOHcQtPdfvvt5phjjjng/ZxbaKpx48aZyy+/vN62X/7yl+biiy82xnBuHQlG5htQVVWlhQsX6uSTT663/eSTT9bs2bMtqgqhrqioSJKUmJgoSdqwYYN27txZ7zxzu90aPXo05xkaZdKkSRo3bpxOPPHEets5t9BU7733noYOHapzzz1XqampGjRokJ577rnA/ZxbaKpjjjlGX3zxhVavXi1JWrJkib755hudfvrpkji3joTT6gKCUX5+vrxer9LS0uptT0tL086dOy2qCqHMGKObbrpJxxxzjPr27StJgXOpofNs06ZNrV4jQsvrr7+uRYsWaf78+fvdx7mFplq/fr2efvpp3XTTTbrrrrv03Xff6brrrpPb7dall17KuYUmu/3221VUVKRevXrJ4XDI6/XqT3/6ky688EJJ/Lt1JAjzB2Gz2ep9bYzZbxvQGNdcc41++OEHffPNN/vdx3mGw7VlyxZdf/31+uyzzxQeHn7A/Ti3cLh8Pp+GDh2qhx56SJI0aNAgLVu2TE8//bQuvfTSwH6cWzhcb7zxhl5++WW9+uqr6tOnjxYvXqwbbrhBmZmZmjBhQmA/zq3DR5tNA5KTk+VwOPYbhc/Ly9vvHSNwKNdee63ee+89ffnll+rYsWNge3p6uiRxnuGwLVy4UHl5eRoyZIicTqecTqdmzZqlv//973I6nYHzh3MLhysjI0O5ubn1tvXu3Tuw+AP/bqGpbr31Vt1xxx264IIL1K9fP11yySW68cYbNXnyZEmcW0eCMN+AsLAwDRkyRNOnT6+3ffr06Ro1apRFVSHUGGN0zTXX6O2339aMGTOUk5NT7/6cnBylp6fXO8+qqqo0a9YszjMc1NixY7V06VItXrw4cBs6dKguuugiLV68WF26dOHcQpMcffTR+y2hu3r1amVnZ0vi3y00XVlZmez2+rHT4XAElqbk3DoCFk6+DWqvv/66cblc5vnnnzfLly83N9xwg4mKijIbN260ujSEiKuvvtrExcWZmTNnmh07dgRuZWVlgX0efvhhExcXZ95++22zdOlSc+GFF5qMjAzj8XgsrByhaN/VbIzh3ELTfPfdd8bpdJo//elPZs2aNeaVV14xkZGR5uWXXw7sw7mFppgwYYLp0KGD+eCDD8yGDRvM22+/bZKTk81tt90W2Idzq2kI8wfxz3/+02RnZ5uwsDAzePDgwJKCQGNIavD2wgsvBPbx+XzmnnvuMenp6cbtdpvjjjvOLF261LqiEbJ+HuY5t9BU77//vunbt69xu92mV69e5tlnn613P+cWmsLj8Zjrr7/edOrUyYSHh5suXbqYu+++21RWVgb24dxqGpsxxlj5lwEAAAAATUPPPAAAABCiCPMAAABAiCLMAwAAACGKMA8AAACEKMI8AAAAEKII8wAAAECIIswDAAAAIYowDwAAAIQowjwAoFnNnDlTNptNhYWFVpcCAG0eYR4AAAAIUYR5AAAAIEQR5gGgjTHG6JFHHlGXLl0UERGhAQMG6M0335T0UwvMhx9+qAEDBig8PFwjRozQ0qVL6x3jrbfeUp8+feR2u9W5c2c99thj9e6vrKzUbbfdpqysLLndbnXv3l3PP/98vX0WLlyooUOHKjIyUqNGjdKqVasC9y1ZskRjxoxRTEyMYmNjNWTIEC1YsKCFXhEAaLucVhcAAGhev//97/X222/r6aefVvfu3fXVV1/p4osvVkpKSmCfW2+9VX/729+Unp6uu+66S7/4xS+0evVquVwuLVy4UOedd57uvfdenX/++Zo9e7Z+97vfKSkpSRMnTpQkXXrppZozZ47+/ve/a8CAAdqwYYPy8/Pr1XH33XfrscceU0pKiq666ipdfvnl+vbbbyVJF110kQYNGqSnn35aDodDixcvlsvlarXXCADaCpsxxlhdBACgeZSWlio5OVkzZszQyJEjA9uvuOIKlZWV6be//a3GjBmj119/Xeeff74kae/everYsaNefPFFnXfeebrooou0e/duffbZZ4HH33bbbfrwww+1bNkyrV69Wj179tT06dN14okn7lfDzJkzNWbMGH3++ecaO3asJOmjjz7SuHHjVF5ervDwcMXGxuof//iHJkyY0MKvCAC0bbTZAEAbsnz5clVUVOikk05SdHR04DZ16lStW7cusN++QT8xMVE9e/bUihUrJEkrVqzQ0UcfXe+4Rx99tNasWSOv16vFixfL4XBo9OjRB62lf//+gc8zMjIkSXl5eZKkm266SVdccYVOPPFEPfzww/VqAwA0HmEeANoQn88nSfrwww+1ePHiwG358uWBvvkDsdlskmp77v2f++37R9yIiIhG1bJv24z/eP767r33Xi1btkzjxo3TjBkzlJubq2nTpjXquACAnxDmAaANyc3Nldvt1ubNm9WtW7d6t6ysrMB+c+fODXxeUFCg1atXq1evXoFjfPPNN/WOO3v2bPXo0UMOh0P9+vWTz+fTrFmzjqjWHj166MYbb9Rnn32mX/7yl3rhhReO6HgA0B4xARYA2pCYmBjdcsstuvHGG+Xz+XTMMcfI4/Fo9uzZio6OVnZ2tiTp/vvvV1JSktLS0nT33XcrOTlZ48ePlyTdfPPNGjZsmB544AGdf/75mjNnjp588kk99dRTkqTOnTtrwoQJuvzyywMTYDdt2qS8vDydd955h6yxvLxct956q8455xzl5ORo69atmj9/vn71q1+12OsCAG0VYR4A2pgHHnhAqampmjx5stavX6/4+HgNHjxYd911V6DN5eGHH9b111+vNWvWaMCAAXrvvfcUFhYmSRo8eLD++9//6o9//KMeeOABZWRk6P777w+sZCNJTz/9tO666y797ne/0549e9SpUyfdddddjarP4XBoz549uvTSS7Vr1y4lJyfrl7/8pe67775mfy0AoK1jNRsAaEf8K80UFBQoPj7e6nIAAEeInnkAAAAgRBHmAQAAgBBFmw0AAAAQohiZBwAAAEIUYR4AAAAIUYR5AAAAIEQR5gEAAIAQRZgHAAAAQhRhHgAAAAhRhHkAAAAgRBHmAQAAgBD1/0WnWAuXs3tFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history_lr_scheduler.keys()), [training_history_lr_scheduler[k]['beta'] for k in training_history_lr_scheduler.keys()])\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"lr\")\n",
    "plt.title(\"ResNet-18: Swish (beta) activation function\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "01ebd79a-0549-48c2-a888-5a403c5da06f",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8611fb2a-a41c-404d-bee6-a961c486e892",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5c960f8-3abe-4f6a-9efb-267a99ef27ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e80c1e4-30a7-40bf-9ddf-048c6cb7e35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize and load 'best' trained parameters from above-\n",
    "best_model = ResNet18(beta = 1.0)\n",
    "best_model.load_state_dict(torch.load(\"ResNet18_swish_best_model.pth\"))\n",
    "\n",
    "# Move to GPU device-\n",
    "best_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a86270be-d5a9-4aa7-ad5a-70e96b620638",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validation: : 100%|███████████████████████████████████| 40/40 [00:02<00:00, 16.53batch/s, val_acc=93.2, val_loss=0.231]\n"
     ]
    }
   ],
   "source": [
    "# Get validation metrics of 'best' trained model-\n",
    "val_loss, val_acc = test_model_progress(\n",
    "    model = best_model, test_loader = val_loader,\n",
    "    test_dataset = val_dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48adcd3e-47aa-4206-bbf8-f2d3b691e0af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet-18 'best' trained validation metrics:  loss = 0.2306 & acc = 93.19%\n"
     ]
    }
   ],
   "source": [
    "print(\"ResNet-18 'best' trained validation metrics: \",\n",
    "      f\"loss = {val_loss:.4f} & acc = {val_acc:.2f}%\"\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c662e4a2-c39a-4f0f-ad78-bb7bf511fac8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88bc9729-52e1-40cf-95f9-053a80633718",
   "metadata": {},
   "outputs": [],
   "source": [
    "# max([train_history[k]['val_acc'] for k in train_history.keys()])\n",
    "max_val_acc = 0\n",
    "max_epoch = 0\n",
    "\n",
    "for e in training_history_lr_scheduler.keys():\n",
    "    if training_history_lr_scheduler[e]['val_acc'] > max_val_acc:\n",
    "        max_val_acc = training_history_lr_scheduler[e]['val_acc']\n",
    "        max_epoch = e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce1b2528-1ac5-4d22-a4c1-f0e9a2557e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highest val acc = 93.19%  is obtained for beta = 6.5875\n"
     ]
    }
   ],
   "source": [
    "print(f\"highest val acc = {training_history_lr_scheduler[max_epoch]['val_acc']:.2f}%\",\n",
    "      f\" is obtained for beta = {training_history_lr_scheduler[max_epoch]['beta']:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4551d098-c95c-41fd-a44c-70609fd8c8eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a43fe0b-ed65-46c3-bee3-2a466b4c7c86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8c60194b-ff72-4ec4-88d3-899c1f3d62dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_swish_fn(x, b = 1.0):\n",
    "    return x * (1 / (1 + np.exp(-b * x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e15a11e-a233-4340-a93b-e97a34123ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-10, 11, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8871751e-30b0-4ab7-8677-28d630c92b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, compute_swish_fn(x = x, b = 6.5875), label = '6.5875')\n",
    "plt.plot(x, compute_swish_fn(x = x, b = 1.0), label = '1.0')\n",
    "plt.plot(x, compute_swish_fn(x = x, b = 10.0), label = '10.0')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Swish activation fn, different betas\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6f87d0a9-4c34-476c-a71c-ebceb84d9166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, compute_swish_fn(x = x, b = 6.5875), label = '6.5875')\n",
    "plt.plot(x, np.where(x <= 0, 0.0, x), label = 'relu')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Swish vs. ReLU\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "668048ec-0f27-4712-a424-d4f145e79b01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "928b98a4-3e3f-47e4-af8d-b0a91aaadc01",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-2, 2, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2a331aa8-1f58-48aa-b4f4-5c8d4a9548c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, compute_swish_fn(x = x, b = 6.5875), label = '6.5875')\n",
    "plt.plot(x, np.where(x <= 0, 0.0, x), label = 'relu')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Swish vs. ReLU\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cb9aec9-557e-4242-b66a-ca3c5a36c79b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e0ece13c-64fa-49e2-beda-fd9071a44029",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-1.2, 1.2, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "63ad4115-bcaa-4685-82ff-003b3f1cedda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, compute_swish_fn(x = x, b = 6.5875), label = '6.5875')\n",
    "plt.plot(x, np.where(x <= 0, 0.0, x), label = 'relu')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Swish vs. ReLU\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635c92e7-063b-4db0-a6e2-dc3d6b98c063",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Except for a small interval $x \\in [-1.0, + 1.0]$ (approximately), both, trainable swish and relu (seem to) behave in a similar manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a001e2-6a0c-41e6-ac3a-5b3985983bca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d183d002-a67f-4981-a2d5-8766bb04a823",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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