DenseNet121_CIFAR10.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "d6101e03-21c8-4e93-b713-45dc30cac7b1",
   "metadata": {},
   "source": [
    "# DenseNet-121 fine-tuning, CIFAR-10, PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abdc8266-85be-4c78-9ce3-0a372f840066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0, 1, 2, 3\n"
     ]
    }
   ],
   "source": [
    "# Specify GPU to be used-\n",
    "%env CUDA_DEVICE_ORDER = PCI_BUS_ID\n",
    "'''\n",
    "Specify GPU(s) to be used here. Say you have 6 GPUs and you want to train Model A on 2 of them\n",
    "and Model B on 4 of them. You can do that as follows-\n",
    "CUDA_VISIBLE_DEVICES=0,1 python model_A.py\n",
    "CUDA_VISIBLE_DEVICES=2,3,4,5 python model_B.py\n",
    "'''\n",
    "%env CUDA_VISIBLE_DEVICES = 0, 1, 2, 3\n",
    "# %env CUDA_VISIBLE_DEVICES = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "716f673b-ba7b-44dd-8a4d-6c33d9512cec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/majumdar/anaconda3/envs/torch-cuda-new/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import datasets, models\n",
    "# import torchvision.transforms as transforms\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm import trange\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os, pickle\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9698d3e8-36a2-4af8-9263-0ce5fc79b61b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "id": "6ce63229-1df5-4ed9-93fa-70f77862d09f",
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20ba7538-b427-4fa4-afb1-880982d97edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 1.13.1\n",
      "Number of GPU(s) available = 4\n",
      "Current GPU: 0\n",
      "Current GPU name: NVIDIA GeForce GTX 1080 Ti\n",
      "Available device is cuda\n"
     ]
    }
   ],
   "source": [
    "print(f\"torch version: {torch.__version__}\")\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": "a9ce5d27-a5eb-459f-af31-65d788104de3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa5a9175-0d28-4502-aa88-498cff3b9df6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84914707-3b17-44cb-8f00-527db470358e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify hyper-parameters-\n",
    "batch_size = 64\n",
    "num_classes = 10\n",
    "num_epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0070b909-7ef2-451c-8afb-2882545f8431",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a00a3d6-41b0-4020-8df9-4326b90546db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "89b96d48-7751-4ad8-8b73-32b5adf7eda1",
   "metadata": {},
   "source": [
    "#### CIFAR-10 data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f97645bf-eead-4e6a-b654-2eec07712125",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define transformations using albumentations-\n",
    "transform_train = A.Compose(\n",
    "    [\n",
    "        A.Resize(width = 224, height = 224),\n",
    "        # A.RandomCrop(width = 20, height = 20),\n",
    "        A.Rotate(limit = 40, p = 0.9, border_mode = cv2.BORDER_CONSTANT),\n",
    "        A.HorizontalFlip(p = 0.5),\n",
    "        A.VerticalFlip(p = 0.1),\n",
    "        A.RGBShift(r_shift_limit = 25, g_shift_limit = 25, b_shift_limit = 25, p = 0.9),\n",
    "        A.OneOf([\n",
    "            A.Blur(blur_limit = 3, p = 0.5),\n",
    "            A.ColorJitter(p = 0.5),\n",
    "        ], p = 1.0),\n",
    "        A.Normalize(\n",
    "            # mean = [0.4914, 0.4822, 0.4465],\n",
    "            # std = [0.247, 0.243, 0.261],\n",
    "            mean = [0, 0, 0],\n",
    "            std = [1, 1, 1],\n",
    "            max_pixel_value = 255,\n",
    "        ),\n",
    "        # This is not dividing by 255, which it does in PyTorch-\n",
    "        ToTensorV2(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "transform_val = A.Compose(\n",
    "    [\n",
    "        A.Resize(width = 224, height = 224),\n",
    "        A.Normalize(\n",
    "            mean = [0, 0, 0],\n",
    "            std = [1, 1, 1],\n",
    "            max_pixel_value = 255\n",
    "        ),\n",
    "        ToTensorV2()\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ba8b1a5-3362-4621-a138-3f7430e35586",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cifar10Dataset(torchvision.datasets.CIFAR10):\n",
    "    def __init__(\n",
    "        self, root = \"~/data/cifar10\",\n",
    "        train = True, download = True,\n",
    "        transform = None\n",
    "    ):\n",
    "        super().__init__(\n",
    "            root = root, train = train,\n",
    "            download = download, transform = transform\n",
    "        )\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        image, label = self.data[index], self.targets[index]\n",
    "\n",
    "        if self.transform is not None:\n",
    "            transformed = self.transform(image = image)\n",
    "            image = transformed[\"image\"]\n",
    "\n",
    "        return image, label\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7392b156-6cf5-48fc-8e8c-aa14c990dfd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "848a73a1-81d4-48c1-a73b-5e2440f842ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Define train and validate sets-\n",
    "train_dataset = Cifar10Dataset(\n",
    "    root = '/home/majumdar/Downloads/.data', train = True,\n",
    "    download = True, transform = transform_train\n",
    ")\n",
    "\n",
    "val_dataset = Cifar10Dataset(\n",
    "    root = '/home/majumdar/Downloads/.data', train = False,\n",
    "    download = True, transform = transform_val\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a45697de-d47a-476c-90f5-bdfd7079048c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define train and validate loaders-\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset = train_dataset, batch_size = batch_size,\n",
    "    shuffle = True\n",
    "    )\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    dataset = val_dataset, batch_size = batch_size,\n",
    "    shuffle = True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "212ed45c-0152-4a0d-a831-2d0cc63d468a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(train_dataset) = 50000 & len(val_dataset) = 10000\n",
      "len(train_loader) = 782 & len(val_loader) = 157\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(781.25, 156.25)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"len(train_dataset) = {len(train_dataset)} & len(val_dataset) = {len(val_dataset)}\")\n",
    "print(f\"len(train_loader) = {len(train_loader)} & len(val_loader) = {len(val_loader)}\")\n",
    "\n",
    "# Sanity check-\n",
    "len(train_dataset) / batch_size, len(val_dataset) / batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cd42424-60ca-48f5-930c-c51986620270",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6dbdf7f0-caf5-4658-b630-e782763ea893",
   "metadata": {},
   "source": [
    "#### DenseNet-121 pre-trained CNN architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adb9a5ea-0cc6-4949-967e-fdc3fd330ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pre-trained DenseNet-121 model-\n",
    "model = models.densenet121(weights = torchvision.models.DenseNet121_Weights.IMAGENET1K_V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd9141b4-0f89-457e-9c8c-4f4932c7d148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove (2x2) pool since CIFAR-10 images are small and don't need it-\n",
    "del model.features.pool0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5094d8e-4ac6-4bc8-a34a-1bde3e21411d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify output layer to have 10 instead of 1000 output neurons-\n",
    "model.classifier = nn.Linear(\n",
    "    in_features = 1024, out_features = num_classes,\n",
    "    bias = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bed8c2e7-4745-4d3f-8d66-f5d67110394d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=1024, out_features=10, bias=True)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "model.classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e93c1f0-9f88-4d99-9f4a-72714d6642da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2862943-d2ba-40ce-adad-31c1b43b4705",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place model on GPU-\n",
    "# model = model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95d75c7-cc16-43ee-8fbb-752c49fc3f0e",
   "metadata": {},
   "source": [
    "#### Multi-GPU PyTorch training\n",
    "\n",
    "Using Data Parallelism\n",
    "\n",
    "To use data parallelism with PyTorch, you can use the DataParallel class. When using this class, you define your GPU IDs and initialize your network using a Module object with a DataParallel object.\n",
    "\n",
    "```\n",
    "parallel_net = nn.DataParallel(myNet, gpu_ids = [0,1,2])\n",
    "```\n",
    "\n",
    "Once defined, you can perform the standard model training steps just as you would with a standard ```nn.Module``` object. For example:\n",
    "\n",
    "```\n",
    "# performs a forward pass\n",
    "predictions = parallel_net(inputs) \n",
    "\n",
    "# computes a loss function\n",
    "loss = loss_function(predictions, labels)\n",
    "\n",
    "# averages GPU-losses and performs a backward pass\n",
    "loss.mean().backward()\n",
    "\n",
    "optimizer.step()\n",
    "```\n",
    "\n",
    "When using this method, you need to ensure that your data is initially stored on one GPU (the “primary GPU”). You should also place your data parallel object on that same GPU. You can do this using code like the following:\n",
    "\n",
    "```\n",
    "input = input.to(0)\n",
    "parallel_net = parellel_net.to(0)\n",
    "```\n",
    "\n",
    "Then, when you call your object it can split your dataset into batches that are distributed across your defined GPUs. Once the operations are complete, the outputs are aggregated on the primary GPU.\n",
    "\n",
    "[Reference](https://www.run.ai/guides/multi-gpu/pytorch-multi-gpu-4-techniques-explained)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e0cc447-b4cf-494e-a2e2-bf102ac51970",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use specific GPUs for training-\n",
    "parallel_net = nn.DataParallel(model, device_ids = [0, 1, 2, 3])\n",
    "\n",
    "# Move to CUDA GPU-\n",
    "parallel_net = parallel_net.to(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0a76c92-f02d-440e-b140-6dfe5dfdbff0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39993e29-c3c6-4d87-ba1c-585ed29cff8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of parameters in DenseNet-121 CNN = 6964106\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "print(f\"Total number of parameters in DenseNet-121 CNN = {tot_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ac6975-e8db-4470-914d-991ac28a3730",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "712282a6-c8e7-4376-bf2f-6e6d98271f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define cost function-\n",
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "# Learning rate - Observe that all parameters are being optimized-\n",
    "optimizer = torch.optim.SGD(params = model.parameters(), lr = 0.001, momentum = 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "278adfca-f801-42f4-82c1-180c451559b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning rate scheduler - Decay LR by a factor of 0.1 every 15 epochs-\n",
    "exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 15, gamma = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a831acb-f3e3-4236-9dd9-ff1fe50a9049",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84577f4e-6d5e-4c95-816e-03e5e6777f41",
   "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(0)\n",
    "            labels = labels.to(0)\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",
    "\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",
    "    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(0)\n",
    "                labels = labels.to(0)\n",
    "                \n",
    "                # Set model to evaluation mode-\n",
    "                model.eval()\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": "e1bf69a7-7f40-4f62-b7c0-63758e250536",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ae364d9-8bc7-48ba-b866-a57a08ac6e32",
   "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 = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "734fab92-51cd-40f4-8018-c52065a259fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "702e3e79-bbab-4e70-bfb7-af4164053cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████| 782/782 [08:45<00:00,  1.49batch/s, accuracy=61.3, loss=1.13]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:38<00:00,  4.11batch/s, val_acc=86, val_loss=0.414]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 1 training loss = 1.1345, training accuracy = 61.33%, val_loss = 0.4137, val_accuracy = 86.03% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 86.03%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=80.3, loss=0.585]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.10batch/s, val_acc=90.8, val_loss=0.272]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 2 training loss = 0.5848, training accuracy = 80.29%, val_loss = 0.2718, val_accuracy = 90.76% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 90.76%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=84, loss=0.467]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.20batch/s, val_acc=92.2, val_loss=0.241]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 3 training loss = 0.4672, training accuracy = 84.01%, val_loss = 0.2408, val_accuracy = 92.19% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 92.19%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=86.4, loss=0.397]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.05batch/s, val_acc=93.2, val_loss=0.203]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 4 training loss = 0.3967, training accuracy = 86.36%, val_loss = 0.2026, val_accuracy = 93.16% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.16%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:34<00:00,  1.52batch/s, accuracy=87.9, loss=0.358]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.16batch/s, val_acc=93.9, val_loss=0.183]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 5 training loss = 0.3583, training accuracy = 87.91%, val_loss = 0.1826, val_accuracy = 93.92% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.92%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=88.9, loss=0.325]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:39<00:00,  3.96batch/s, val_acc=93.9, val_loss=0.183]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 6 training loss = 0.3250, training accuracy = 88.88%, val_loss = 0.1829, val_accuracy = 93.94% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 93.94%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:36<00:00,  1.51batch/s, accuracy=89.9, loss=0.294]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:36<00:00,  4.25batch/s, val_acc=94.1, val_loss=0.173]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 7 training loss = 0.2942, training accuracy = 89.93%, val_loss = 0.1732, val_accuracy = 94.12% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 94.12%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:29<00:00,  1.54batch/s, accuracy=90.5, loss=0.275]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.17batch/s, val_acc=94.5, val_loss=0.167]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 8 training loss = 0.2751, training accuracy = 90.49%, val_loss = 0.1669, val_accuracy = 94.51% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 94.51%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=91.4, loss=0.257]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.15batch/s, val_acc=94.5, val_loss=0.162]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 9 training loss = 0.2573, training accuracy = 91.39%, val_loss = 0.1615, val_accuracy = 94.55% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 94.55%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=91.7, loss=0.245]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:36<00:00,  4.26batch/s, val_acc=94.2, val_loss=0.172]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 10 training loss = 0.2445, training accuracy = 91.74%, val_loss = 0.1720, val_accuracy = 94.17% & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=92.3, loss=0.231]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.22batch/s, val_acc=94.8, val_loss=0.157]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 11 training loss = 0.2308, training accuracy = 92.26%, val_loss = 0.1566, val_accuracy = 94.75% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 94.75%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=92.6, loss=0.218]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.12batch/s, val_acc=95.2, val_loss=0.142]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 12 training loss = 0.2176, training accuracy = 92.57%, val_loss = 0.1425, val_accuracy = 95.18% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 95.18%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:29<00:00,  1.53batch/s, accuracy=92.9, loss=0.208]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.09batch/s, val_acc=95.4, val_loss=0.141]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 13 training loss = 0.2079, training accuracy = 92.87%, val_loss = 0.1406, val_accuracy = 95.44% & LR = 0.00100\n",
      "\n",
      "Saving model with highest val_acc = 95.44%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=93.3, loss=0.198]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.22batch/s, val_acc=94.7, val_loss=0.161]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 14 training loss = 0.1976, training accuracy = 93.35%, val_loss = 0.1607, val_accuracy = 94.66% & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=93.7, loss=0.184]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.06batch/s, val_acc=95.1, val_loss=0.141]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 15 training loss = 0.1844, training accuracy = 93.73%, val_loss = 0.1413, val_accuracy = 95.10% & LR = 0.00100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=94.9, loss=0.153]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.23batch/s, val_acc=95.6, val_loss=0.133]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 16 training loss = 0.1532, training accuracy = 94.87%, val_loss = 0.1332, val_accuracy = 95.58% & LR = 0.00010\n",
      "\n",
      "Saving model with highest val_acc = 95.58%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=95.2, loss=0.145]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:39<00:00,  3.98batch/s, val_acc=95.7, val_loss=0.131]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 17 training loss = 0.1445, training accuracy = 95.21%, val_loss = 0.1309, val_accuracy = 95.73% & LR = 0.00010\n",
      "\n",
      "Saving model with highest val_acc = 95.73%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:35<00:00,  1.52batch/s, accuracy=95.4, loss=0.138]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.24batch/s, val_acc=95.8, val_loss=0.126]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 18 training loss = 0.1381, training accuracy = 95.45%, val_loss = 0.1257, val_accuracy = 95.83% & LR = 0.00010\n",
      "\n",
      "Saving model with highest val_acc = 95.83%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=95.5, loss=0.136]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 19 training loss = 0.1355, training accuracy = 95.48%, val_loss = 0.1234, val_accuracy = 95.92% & LR = 0.00010\n",
      "\n",
      "Saving model with highest val_acc = 95.92%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:34<00:00,  1.52batch/s, accuracy=95.7, loss=0.131]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 20 training loss = 0.1307, training accuracy = 95.72%, val_loss = 0.1248, val_accuracy = 95.85% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=95.7, loss=0.131]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.24batch/s, val_acc=96.1, val_loss=0.123]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 21 training loss = 0.1311, training accuracy = 95.73%, val_loss = 0.1226, val_accuracy = 96.12% & LR = 0.00010\n",
      "\n",
      "Saving model with highest val_acc = 96.12%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:35<00:00,  1.52batch/s, accuracy=95.8, loss=0.129]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 22 training loss = 0.1293, training accuracy = 95.79%, val_loss = 0.1236, val_accuracy = 96.01% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=95.7, loss=0.129]\n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 23 training loss = 0.1286, training accuracy = 95.69%, val_loss = 0.1270, val_accuracy = 95.79% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=95.8, loss=0.128]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 24 training loss = 0.1282, training accuracy = 95.79%, val_loss = 0.1238, val_accuracy = 95.96% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|█████████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=96, loss=0.123]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 25 training loss = 0.1230, training accuracy = 95.95%, val_loss = 0.1239, val_accuracy = 95.87% & LR = 0.00010\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 26 training loss = 0.1235, training accuracy = 95.88%, val_loss = 0.1267, val_accuracy = 95.90% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|█████████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=96, loss=0.122]\n",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 27 training loss = 0.1224, training accuracy = 96.02%, val_loss = 0.1226, val_accuracy = 96.10% & LR = 0.00010\n",
      "\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=96, loss=0.121]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.05batch/s, val_acc=95.9, val_loss=0.125]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 28 training loss = 0.1212, training accuracy = 96.02%, val_loss = 0.1252, val_accuracy = 95.85% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=96.1, loss=0.121]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:39<00:00,  3.95batch/s, val_acc=96, val_loss=0.125]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 29 training loss = 0.1209, training accuracy = 96.11%, val_loss = 0.1249, val_accuracy = 95.99% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=96.1, loss=0.118]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:36<00:00,  4.27batch/s, val_acc=96, val_loss=0.124]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 30 training loss = 0.1183, training accuracy = 96.12%, val_loss = 0.1244, val_accuracy = 95.95% & LR = 0.00010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:39<00:00,  4.01batch/s, val_acc=95.8, val_loss=0.128]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 31 training loss = 0.1132, training accuracy = 96.33%, val_loss = 0.1280, val_accuracy = 95.76% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:36<00:00,  1.51batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:39<00:00,  3.96batch/s, val_acc=96.2, val_loss=0.125]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 32 training loss = 0.1129, training accuracy = 96.35%, val_loss = 0.1246, val_accuracy = 96.19% & LR = 0.00001\n",
      "\n",
      "Saving model with highest val_acc = 96.19%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=96.3, loss=0.114]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:39<00:00,  3.94batch/s, val_acc=95.9, val_loss=0.125]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 33 training loss = 0.1145, training accuracy = 96.27%, val_loss = 0.1251, val_accuracy = 95.91% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:38<00:00,  1.51batch/s, accuracy=96.2, loss=0.116]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:38<00:00,  4.04batch/s, val_acc=95.9, val_loss=0.127]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 34 training loss = 0.1157, training accuracy = 96.20%, val_loss = 0.1266, val_accuracy = 95.88% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:31<00:00,  1.53batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.20batch/s, val_acc=96.1, val_loss=0.122]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 35 training loss = 0.1129, training accuracy = 96.32%, val_loss = 0.1217, val_accuracy = 96.08% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:33<00:00,  1.52batch/s, accuracy=96.2, loss=0.115]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:39<00:00,  4.01batch/s, val_acc=96, val_loss=0.124]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 36 training loss = 0.1146, training accuracy = 96.17%, val_loss = 0.1236, val_accuracy = 96.00% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=96.3, loss=0.114]\n",
      "Validation: : 100%|███████████████████████████████| 157/157 [00:37<00:00,  4.16batch/s, val_acc=96.1, val_loss=0.12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 37 training loss = 0.1140, training accuracy = 96.28%, val_loss = 0.1203, val_accuracy = 96.13% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:29<00:00,  1.53batch/s, accuracy=96.3, loss=0.116]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.16batch/s, val_acc=95.9, val_loss=0.124]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 38 training loss = 0.1162, training accuracy = 96.26%, val_loss = 0.1239, val_accuracy = 95.92% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=96.3, loss=0.114]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:37<00:00,  4.23batch/s, val_acc=96, val_loss=0.123]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 39 training loss = 0.1136, training accuracy = 96.28%, val_loss = 0.1230, val_accuracy = 96.05% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=96.4, loss=0.113]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.19batch/s, val_acc=96.1, val_loss=0.122]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 40 training loss = 0.1129, training accuracy = 96.36%, val_loss = 0.1219, val_accuracy = 96.08% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████| 782/782 [08:27<00:00,  1.54batch/s, accuracy=96.4, loss=0.111]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 41 training loss = 0.1109, training accuracy = 96.38%, val_loss = 0.1231, val_accuracy = 96.01% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:26<00:00,  1.54batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:37<00:00,  4.14batch/s, val_acc=96, val_loss=0.124]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 42 training loss = 0.1126, training accuracy = 96.33%, val_loss = 0.1238, val_accuracy = 95.99% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:28<00:00,  1.54batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:37<00:00,  4.18batch/s, val_acc=96, val_loss=0.123]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 43 training loss = 0.1134, training accuracy = 96.28%, val_loss = 0.1229, val_accuracy = 96.01% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=96.3, loss=0.114]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:36<00:00,  4.29batch/s, val_acc=95.9, val_loss=0.124]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 44 training loss = 0.1145, training accuracy = 96.31%, val_loss = 0.1239, val_accuracy = 95.94% & LR = 0.00001\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.52batch/s, accuracy=96.3, loss=0.112]\n",
      "Validation: : 100%|███████████████████████████████| 157/157 [00:38<00:00,  4.10batch/s, val_acc=96.2, val_loss=0.12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 45 training loss = 0.1124, training accuracy = 96.34%, val_loss = 0.1200, val_accuracy = 96.20% & LR = 0.00001\n",
      "\n",
      "Saving model with highest val_acc = 96.20%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=96.4, loss=0.112]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:36<00:00,  4.27batch/s, val_acc=95.9, val_loss=0.126]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 46 training loss = 0.1119, training accuracy = 96.41%, val_loss = 0.1260, val_accuracy = 95.88% & LR = 0.00000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:32<00:00,  1.53batch/s, accuracy=96.3, loss=0.112]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:37<00:00,  4.14batch/s, val_acc=96, val_loss=0.126]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 47 training loss = 0.1121, training accuracy = 96.32%, val_loss = 0.1262, val_accuracy = 96.01% & LR = 0.00000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=96.3, loss=0.112]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:36<00:00,  4.25batch/s, val_acc=96, val_loss=0.123]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 48 training loss = 0.1123, training accuracy = 96.31%, val_loss = 0.1227, val_accuracy = 96.05% & LR = 0.00000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:30<00:00,  1.53batch/s, accuracy=96.3, loss=0.113]\n",
      "Validation: : 100%|██████████████████████████████| 157/157 [00:37<00:00,  4.21batch/s, val_acc=96.2, val_loss=0.122]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 49 training loss = 0.1128, training accuracy = 96.33%, val_loss = 0.1217, val_accuracy = 96.15% & LR = 0.00000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████| 782/782 [08:37<00:00,  1.51batch/s, accuracy=96.4, loss=0.111]\n",
      "Validation: : 100%|████████████████████████████████| 157/157 [00:38<00:00,  4.11batch/s, val_acc=96, val_loss=0.122]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 50 training loss = 0.1109, training accuracy = 96.42%, val_loss = 0.1218, val_accuracy = 96.05% & LR = 0.00000\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 = parallel_net, train_loader = train_loader,\n",
    "        train_dataset = train_dataset\n",
    "    )\n",
    "    val_loss, val_acc = test_model_progress(\n",
    "        model = parallel_net, test_loader = val_loader,\n",
    "        test_dataset = 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\"LR = {optimizer.param_groups[0]['lr']:.5f}\\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",
    "    }\n",
    "\n",
    "    exp_lr_scheduler.step()\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:.2f}%\\n\")\n",
    "        \n",
    "        # Save trained model with 'best' validation accuracy-\n",
    "        # torch.save(model.state_dict(), \"DenseNet121_best_model.pth\")\n",
    "        torch.save(parallel_net.module.state_dict(), \"DenseNet121_best_model.pth\") \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a892cc-65f5-4697-8168-b6d2ce52d1af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b894a8b7-9a18-4ff5-a433-c44d2799867e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training metrics as Python3 history for later analysis-\n",
    "with open(\"DenseNet121_CIFAR10_training_history.pkl\", \"wb\") as file:\n",
    "    pickle.dump(training_history_lr_scheduler, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afb17b77-c719-494e-b00e-0743b385e93e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "262152f5-68d6-4041-88f4-9f50857f271d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize ResNet-training-\n",
    "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(\"DenseNet-121: Training Accuracy\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy (%)\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb18d537-0b33-431d-be11-01a41ac24b50",
   "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(\"DenseNet-121: Training Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d94061c4-75a6-4b42-80d3-7f331cfef268",
   "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(\"DensezeNet-121: Learning-Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7d10ec-4bb9-4d76-a6b4-306feec3d96c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1f0ee24-9b44-4da3-b813-a6afc0260d9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}