STL10_ResNet18_finetuning_torch.ipynb

{
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   "source": [
    "# STL-10, ResNet-18 classification with fine-tuning\n",
    "\n",
    "The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods.\n",
    "\n",
    "__Overview__\n",
    "\n",
    "- 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck.\n",
    "- Images are 96x96 pixels, color.\n",
    "- 500 training images (10 pre-defined folds), 800 test images per class.\n",
    "- 100000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set.\n",
    "- Images were acquired from labeled examples on [ImageNet](http://www.image-net.org/).\n",
    "\n",
    "[Reference](http://ai.stanford.edu/%7Eacoates/stl10/), [torchvision STL-10 reference](https://pytorch.org/vision/stable/generated/torchvision.datasets.STL10.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2ffafc4-2075-49b3-af10-5a6907309b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arjun\\anaconda3\\envs\\torch-cuda\\lib\\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 torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm import trange\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio\n",
    "import cv2, pickle, os\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eb80940-686a-4695-8c29-6b202528f8fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1586c1c3-9e4e-4b34-90d9-02abbe8aff7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c22d8f0-9ddc-4cde-b23c-03c485baff9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 1.13.1\n",
      "Number of GPU(s) available = 1\n",
      "Current GPU: 0\n",
      "Current GPU name: NVIDIA GeForce RTX 3080 Laptop GPU\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef6720c9-0d1f-45c9-a281-7b9db128bfc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available device is cuda\n"
     ]
    }
   ],
   "source": [
    "# Device configuration-\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Use different GPUs (1 and 2) for model, while GPU 3 for data-\n",
    "# device = torch.device(\"cuda:0,1\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "print(f'Available device is {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa161a1a-74df-4e33-ae9a-4ded4d10a087",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e5e7eb0-28b1-4709-bac2-1e18da1667b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f65c2bcb-919e-4fb1-824a-f46222b6b221",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define batch-size for training-\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84dd1b97-b4ac-4265-9cfb-16e7475adb56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b910ba92-669b-4697-8820-09909d361ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define torchvision transformations for training and test sets-\n",
    "transform_train = transforms.Compose(\n",
    "    [\n",
    "        # transforms.RandomCrop(32, padding = 4),\n",
    "        transforms.RandomHorizontalFlip(p = 0.4),\n",
    "        transforms.RandomRotation(degrees = 40),\n",
    "        transforms.RandomVerticalFlip(p = 0.1),\n",
    "        transforms.ColorJitter(brightness = 0, contrast = 0, saturation = 0, hue = 0),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "transform_test = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d54512c-c0b5-43bb-a8cb-2b26f3764737",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Define albumentations transformations using albumentations-\n",
    "transform_train = A.Compose(\n",
    "    [\n",
    "        # A.Resize(width = 32, height = 32),\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.Normalize(\n",
    "            mean = [0, 0, 0],\n",
    "            std = [1, 1, 1],\n",
    "            max_pixel_value = 255\n",
    "        ),\n",
    "        ToTensorV2()\n",
    "    ]\n",
    ")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008d9fcd-a6c5-419e-b7b3-4a2514813dd2",
   "metadata": {},
   "source": [
    "[Add data augmentation with albumentation to image classification framework](https://stackoverflow.com/questions/71476099/how-to-add-data-augmentation-with-albumentation-to-image-classification-framewor)\n",
    "\n",
    "[Error loading images](https://github.com/ageitgey/face_recognition/issues/933)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1847f064-8637-48e3-9930-36514bfd4ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "class ImageDataset(torch.utils.data.Dataset):\n",
    "    def __init__(\n",
    "        self, images_filepaths,\n",
    "        transform = None\n",
    "    ):\n",
    "        self.images_filepaths = images_filepaths\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images_filepaths)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image_filepath = self.images_filepaths[idx]\n",
    "        image = cv2.imread(image_filepath)\n",
    "        print(f\"img shape: {image.shape}\")\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        if self.transform is not None:\n",
    "            image = self.transform(image=image)[\"image\"]\n",
    "        return image\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8cb4d4-6e2d-4232-ad78-78b88e084219",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6862e91-1863-47fd-9c21-160a701ba745",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2a42ced3-27de-4c53-a541-28396a133620",
   "metadata": {},
   "source": [
    "#### torchvision STL10\n",
    "\n",
    "```\n",
    "torchvision.datasets.STL10(root: str, split: str = 'train', folds: Optional[int] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)[source]\n",
    "```\n",
    "\n",
    "__STL10 Dataset Parameters__:\n",
    "\n",
    "- root (string) – Root directory of dataset where directory stl10_binary exists.\n",
    "\n",
    "- split (string) – One of {‘train’, ‘test’, ‘unlabeled’, ‘train+unlabeled’}. Accordingly dataset is selected.\n",
    "\n",
    "- folds (int, optional) – One of {0-9} or None. For training, loads one of the 10 pre-defined folds of 1k samples for the standard evaluation procedure. If no value is passed, loads the 5k samples.\n",
    "\n",
    "- transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop\n",
    "\n",
    "- target_transform (callable, optional) – A function/transform that takes in the target and transforms it.\n",
    "\n",
    "- download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a287b42-facf-470c-8a48-380f6bee3c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define training dataset-\n",
    "train_dataset = torchvision.datasets.STL10(\n",
    "    root = 'C:/Users/arjun/Downloads/data/', split = 'train',\n",
    "    folds = None, transform = transform_train,\n",
    "    target_transform = None, download = True\n",
    ")\n",
    "\n",
    "# Define testing dataset-\n",
    "test_dataset = torchvision.datasets.STL10(\n",
    "    root = 'C:/Users/arjun/Downloads/data/', split = 'test',\n",
    "    folds = None, transform = transform_test,\n",
    "    target_transform = None, download = True\n",
    ")\n",
    "\n",
    "# NOTE: this needs to be executed the first time to download dataset into 'root'!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6de850b0-67a1-4d2d-b901-d6d846c431c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Define training and testing datasets-\n",
    "train_dataset = ImageDataset(images_filepaths = 'C:/Users/arjun/Downloads/data/', transform = transform_train)\n",
    "test_dataset = ImageDataset(images_filepaths = 'C:/Users/arjun/Downloads/data/', transform = transform_val)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9aca8d3e-7bb5-4fa1-85ce-a352daf232ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dataloaders-\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset = train_dataset, batch_size = batch_size,\n",
    "    shuffle = True\n",
    "    )\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    dataset = test_dataset, batch_size = batch_size,\n",
    "    shuffle = True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20b91eeb-438f-43ce-8aee-974b586ecc82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(train_dataset) = 5000 & len(test_dataset) = 8000\n",
      "len(train_loader) = 40 & len(test_loader) = 63\n"
     ]
    }
   ],
   "source": [
    "print(f\"len(train_dataset) = {len(train_dataset)} & len(test_dataset) = {len(test_dataset)}\")\n",
    "print(f\"len(train_loader) = {len(train_loader)} & len(test_loader) = {len(test_loader)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5eb97dec-1e35-4fbf-8330-fa50eeb848df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.0625, 62.5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "len(train_dataset) / batch_size, len(test_dataset) / batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "742233ca-4058-476c-accd-5c91d09598b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2089b2e6-0938-4f95-9ed0-5abf237f45ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Sanity check-\n",
    "images, labels = next(iter(train_loader))\n",
    "images.size(), labels.shape\n",
    "\n",
    "images.max(), images.min()\n",
    "\n",
    "# Labels and their count/distribution (only for 'train' split!)-\n",
    "lab, cnt = np.unique(labels.numpy(), return_counts = True)\n",
    "\n",
    "lab, cnt\n",
    "\n",
    "# Unnormalize images-\n",
    "images = images / 2 + 0.5\n",
    "\n",
    "# Transpose axes for visualization-\n",
    "images = np.transpose(images.numpy(), (0, 2, 3, 1))\n",
    "\n",
    "images.shape\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bfa1356-828d-46be-a7cd-755f8280d074",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x900 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize 50 images from training set-\n",
    "plt.figure(figsize = (10, 9))\n",
    "\n",
    "for i in range(50):\n",
    "    plt.subplot(5, 10, i + 1)\n",
    "    plt.imshow(images[i])\n",
    "    # get current axes-\n",
    "    ax = plt.gca()\n",
    "\n",
    "    # hide x-axis-\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "\n",
    "    # hide y-axis-\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "plt.suptitle(\"Sample STL-10 training images\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b77095ec-39fc-49f0-9739-e65ee853ceac",
   "metadata": {},
   "outputs": [],
   "source": [
    "del images, labels, lab, cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b064d1a-f70d-4777-b200-30b56b095e4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b3d7312-bc5d-4b44-80c3-2435dca6c6d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "24ead955-898f-4e86-82c5-2f5db6843b6c",
   "metadata": {},
   "source": [
    "### Change the first conv layer:\n",
    "\n",
    "Use the hyper-parameters: kernel_size=3, stride=1 and padding=1 instead of the original ResNet-50 hyper-parameters. This keeps the original input size for quite a while in forward propagation. In my experiments, doing this is leads to a higher validation accuracy and is probably more efficient for small input dataset(s) such as CIFAR-10.\n",
    "\n",
    "[PyTorch discussion](https://discuss.pytorch.org/t/resnet50-torchvision-implementation-gives-low-accuracy-on-cifar-10/82046/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56dd412c-7bcd-4985-aac0-f2099b913018",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_resnet18_stl(num_classes = 10):\n",
    "    # Load pre-trained ResNet-50 model-\n",
    "    model_ft = torchvision.models.resnet18(weights = torchvision.models.resnet.ResNet18_Weights.DEFAULT)\n",
    "    # Precise path: 'C:\\Users\\arjun\\.cache\\torch\\hub\\checkpoints\\'\n",
    "    \n",
    "    # Compute number of features for defining last linear/dense layer-\n",
    "    num_ftrs = model_ft.fc.in_features\n",
    "    # print(f\"number of input features = {num_ftrs}\")\n",
    "    # number of input features = 2048\n",
    "    \n",
    "    # Define last dense layer/output layer-\n",
    "    model_ft.fc = nn.Linear(in_features = num_ftrs, out_features = num_classes)\n",
    "    \n",
    "    # Change first conv layer of ResNet-18:\n",
    "    model_ft.conv1 = torch.nn.Conv2d(\n",
    "        in_channels = 3, out_channels = 64,\n",
    "        kernel_size = (3, 3), stride = (1, 1),\n",
    "        padding = (1, 1), bias = False\n",
    "    )\n",
    "    \n",
    "    return model_ft\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7afda03-07c8-4d41-b8c7-625da5854eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to C:\\Users\\arjun/.cache\\torch\\hub\\checkpoints\\resnet18-f37072fd.pth\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 44.7M/44.7M [00:07<00:00, 6.36MB/s]\n"
     ]
    }
   ],
   "source": [
    "model = get_resnet18_stl(num_classes = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb8d212-3fdf-4513-afdc-9295a0adf68c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "for layer_name, params in model.named_parameters():\n",
    "    print(f\"{layer_name} has {params.shape} params\")\n",
    "\"\"\"\n",
    "# A verbose output!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "469638f9-bc0a-4669-a92b-b5e0b9dba707",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sanity check-\n",
    "# model_ft.conv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe2a48bb-ed5a-4665-b23f-80e9c021dc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further sanity check-\n",
    "# print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6cee1456-3ddc-41f6-b88b-4c73bae22b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save weights before fine-tuning-\n",
    "# torch.save(model.state_dict(), \"ResNet18__pretrained_weights.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d7ed942-39b2-484c-8550-b94cf11e86b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load weights from above-\n",
    "model.load_state_dict(torch.load('ResNet18__pretrained_weights.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c9ede6-3975-4fea-a35a-f695aa662a20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79b0e84-818d-48cf-b5b0-b9a8b70b9170",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6ccc2f74-eee1-4ee7-a9d8-db8e71e73749",
   "metadata": {},
   "source": [
    "### Train parameters of newly added layers\n",
    "\n",
    "We need to freeze all the pre-trained network layers except for the newly added layers, viz., the first conv layer and the final dense layer. We need to set ```requires_grad = False``` to freeze the parameters so that the gradients are not computed in ```backward()```.\n",
    "\n",
    "[Transfer Learning PyTorch Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "96ec4013-afab-4752-b6e7-a273a833ac16",
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e98e88c-6e20-4439-85f9-0b9a0f367174",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set first conv and output layer to train-\n",
    "model.conv1.weight.requires_grad = True\n",
    "model.bn1.weight.requires_grad = True\n",
    "model.bn1.bias.requires_grad = True\n",
    "\n",
    "model.fc.weight.requires_grad = True\n",
    "model.fc.bias.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f7162a94-d67e-4201-bed0-028d7030b2d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer: conv1.weight, shape: torch.Size([64, 3, 3, 3]), requires grad? True\n",
      "layer: bn1.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: bn1.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.0.conv1.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? False\n",
      "layer: layer1.0.bn1.weight, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.0.bn1.bias, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.0.conv2.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? False\n",
      "layer: layer1.0.bn2.weight, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.0.bn2.bias, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.1.conv1.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? False\n",
      "layer: layer1.1.bn1.weight, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.1.bn1.bias, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.1.conv2.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? False\n",
      "layer: layer1.1.bn2.weight, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer1.1.bn2.bias, shape: torch.Size([64]), requires grad? False\n",
      "layer: layer2.0.conv1.weight, shape: torch.Size([128, 64, 3, 3]), requires grad? False\n",
      "layer: layer2.0.bn1.weight, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.0.bn1.bias, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.0.conv2.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? False\n",
      "layer: layer2.0.bn2.weight, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.0.bn2.bias, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.0.downsample.0.weight, shape: torch.Size([128, 64, 1, 1]), requires grad? False\n",
      "layer: layer2.0.downsample.1.weight, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.0.downsample.1.bias, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.1.conv1.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? False\n",
      "layer: layer2.1.bn1.weight, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.1.bn1.bias, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.1.conv2.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? False\n",
      "layer: layer2.1.bn2.weight, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer2.1.bn2.bias, shape: torch.Size([128]), requires grad? False\n",
      "layer: layer3.0.conv1.weight, shape: torch.Size([256, 128, 3, 3]), requires grad? False\n",
      "layer: layer3.0.bn1.weight, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.0.bn1.bias, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.0.conv2.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? False\n",
      "layer: layer3.0.bn2.weight, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.0.bn2.bias, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.0.downsample.0.weight, shape: torch.Size([256, 128, 1, 1]), requires grad? False\n",
      "layer: layer3.0.downsample.1.weight, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.0.downsample.1.bias, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.1.conv1.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? False\n",
      "layer: layer3.1.bn1.weight, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.1.bn1.bias, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.1.conv2.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? False\n",
      "layer: layer3.1.bn2.weight, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer3.1.bn2.bias, shape: torch.Size([256]), requires grad? False\n",
      "layer: layer4.0.conv1.weight, shape: torch.Size([512, 256, 3, 3]), requires grad? False\n",
      "layer: layer4.0.bn1.weight, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.0.bn1.bias, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.0.conv2.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? False\n",
      "layer: layer4.0.bn2.weight, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.0.bn2.bias, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.0.downsample.0.weight, shape: torch.Size([512, 256, 1, 1]), requires grad? False\n",
      "layer: layer4.0.downsample.1.weight, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.0.downsample.1.bias, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.1.conv1.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? False\n",
      "layer: layer4.1.bn1.weight, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.1.bn1.bias, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.1.conv2.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? False\n",
      "layer: layer4.1.bn2.weight, shape: torch.Size([512]), requires grad? False\n",
      "layer: layer4.1.bn2.bias, shape: torch.Size([512]), requires grad? False\n",
      "layer: fc.weight, shape: torch.Size([10, 512]), requires grad? True\n",
      "layer: fc.bias, shape: torch.Size([10]), requires grad? True\n"
     ]
    }
   ],
   "source": [
    "# Sanity check-\n",
    "for layer_name, param in model.named_parameters():\n",
    "    print(f\"layer: {layer_name}, shape: {param.shape}, requires grad? {param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f35f283-7597-46f3-a42e-d7edf239b0c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e9cdb4d-9cea-4580-a0d4-9f72edbbfb5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fda0c0b5-5079-46f7-a7cc-7fcdeb4daded",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define number of fine-tuning epochs-\n",
    "num_epochs = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddfa266-8017-487e-bb86-dae3ec035fe3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e440b650-84aa-4b70-ae96-ee53096530ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place model on GPU-\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "324abede-9400-4aaf-9901-fa4f97b9bd48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define cost function-\n",
    "cost_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "# Learning rate - Observe that all parameters are being optimized-\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 1e-3, momentum = 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0465cbca-9c75-4d73-934e-8e1337da3d31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f3663d56-c2ab-484f-9a7c-2180018420be",
   "metadata": {},
   "source": [
    "#### MultiStepLR PyTorch\n",
    "\n",
    "```\n",
    "torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=- 1, verbose=False)\n",
    "```\n",
    "\n",
    "Decays the learning rate of each parameter group by _gamma_ once the number of epoch reaches one of the milestones. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.\n",
    "\n",
    "Parameters:\n",
    "\n",
    "- optimizer (Optimizer) – Wrapped optimizer.\n",
    "- milestones (list) – List of epoch indices. Must be increasing.\n",
    "- gamma (float) – Multiplicative factor of learning rate decay. Default: 0.1.\n",
    "- last_epoch (int) – The index of last epoch. Default: -1.\n",
    "- verbose (bool) – If True, prints a message to stdout for each update. Default: False.\n",
    "\n",
    "Example:\n",
    "\n",
    "```\n",
    "# Assuming optimizer uses lr = 0.05 for all groups\n",
    "# lr = 0.05     if epoch < 30\n",
    "# lr = 0.005    if 30 <= epoch < 80\n",
    "# lr = 0.0005   if epoch >= 80\n",
    "scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)\n",
    "```\n",
    "\n",
    "[MultiStepLR](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.MultiStepLR.html). You might aso consider [StepLR](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html) scheduler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95ddc2f4-83ec-4ed9-92bd-ac96150293e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning rate scheduler - Decay LR by a factor of 0.1 every 10 epochs-\n",
    "# exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 10, gamma = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca3efb26-53c4-45de-87c2-86ab59b0a919",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decay lr at 9th and 20th epochs by a factor of 10-\n",
    "scheduler = torch.optim.lr_scheduler.MultiStepLR(\n",
    "    optimizer = optimizer, milestones = [9, 20],\n",
    "    gamma = 0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fa6c324-2cc6-41f2-9de4-b0ad19937054",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47af1c06-b793-4c7f-9c0b-ee0c22315fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_step(model, train_loader, train_dataset):\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0.0\n",
    "    \n",
    "    model.to(device)\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 = cost_fn(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",
    "            # 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 train_loss, train_acc.detach().cpu().item()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "652c1367-908c-454e-a00c-8b3ea2fa730a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_one_step(model, test_loader, test_dataset):\n",
    "    total = 0.0\n",
    "    correct = 0.0\n",
    "    running_loss_val = 0.0\n",
    "    \n",
    "    model.to(device)\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 = cost_fn(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.detach().cpu().item()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17df4ddb-5626-4e6b-a1a8-7a2f85f6420b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f3b50b9e-f3b6-4853-bc78-6e9a221295cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python3 dict to contain model training metrics-\n",
    "train_history = {}\n",
    "\n",
    "# To save 'best' parameters-\n",
    "best_val_acc = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a08eab32-61eb-4eb3-8090-4b280b1992de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4185a8b5-8f79-43ee-9c2a-08300e345dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:12<00:00,  3.18batch/s, accuracy=13.3, loss=2.33]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.77batch/s, val_acc=20.4, val_loss=2.17]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1; loss = 2.3296, acc = 13.26%  val loss = 2.1679, val acc = 20.36% & LR = 0.00100\n",
      "Saving model with highest val_acc = 20.36%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.15batch/s, accuracy=25.1, loss=2.07]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.76batch/s, val_acc=29.5, val_loss=1.95]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2; loss = 2.0745, acc = 25.12%  val loss = 1.9460, val acc = 29.53% & LR = 0.00100\n",
      "Saving model with highest val_acc = 29.53%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:06<00:00,  6.14batch/s, accuracy=34, loss=1.89]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.69batch/s, val_acc=36.1, val_loss=1.79]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3; loss = 1.8892, acc = 34.00%  val loss = 1.7931, val acc = 36.14% & LR = 0.00100\n",
      "Saving model with highest val_acc = 36.14%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=37.5, loss=1.77]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=40.5, val_loss=1.68]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4; loss = 1.7730, acc = 37.48%  val loss = 1.6819, val acc = 40.53% & LR = 0.00100\n",
      "Saving model with highest val_acc = 40.53%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=41.5, loss=1.68]\n",
      "Validation: : 100%|█████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=43.9, val_loss=1.6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5; loss = 1.6803, acc = 41.50%  val loss = 1.5964, val acc = 43.86% & LR = 0.00100\n",
      "Saving model with highest val_acc = 43.86%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=44.3, loss=1.61]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=44.9, val_loss=1.56]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6; loss = 1.6139, acc = 44.26%  val loss = 1.5583, val acc = 44.88% & LR = 0.00100\n",
      "Saving model with highest val_acc = 44.88%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=45.9, loss=1.56]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=48.3, val_loss=1.47]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7; loss = 1.5623, acc = 45.92%  val loss = 1.4734, val acc = 48.30% & LR = 0.00100\n",
      "Saving model with highest val_acc = 48.30%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:06<00:00,  6.11batch/s, accuracy=47, loss=1.52]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=49.3, val_loss=1.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8; loss = 1.5175, acc = 46.96%  val loss = 1.4349, val acc = 49.26% & LR = 0.00100\n",
      "Saving model with highest val_acc = 49.26%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.11batch/s, accuracy=48.4, loss=1.47]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=50.3, val_loss=1.41]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9; loss = 1.4687, acc = 48.42%  val loss = 1.4059, val acc = 50.34% & LR = 0.00010\n",
      "Saving model with highest val_acc = 50.34%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.11batch/s, accuracy=50.1, loss=1.44]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=51.5, val_loss=1.39]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10; loss = 1.4391, acc = 50.08%  val loss = 1.3915, val acc = 51.50% & LR = 0.00010\n",
      "Saving model with highest val_acc = 51.50%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=50.9, loss=1.43]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=52.3, val_loss=1.37]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11; loss = 1.4325, acc = 50.94%  val loss = 1.3743, val acc = 52.30% & LR = 0.00010\n",
      "Saving model with highest val_acc = 52.30%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=50.5, loss=1.43]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=52.1, val_loss=1.38]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12; loss = 1.4317, acc = 50.54%  val loss = 1.3802, val acc = 52.12% & LR = 0.00010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=50.1, loss=1.43]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=52.5, val_loss=1.37]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13; loss = 1.4301, acc = 50.12%  val loss = 1.3726, val acc = 52.50% & LR = 0.00010\n",
      "Saving model with highest val_acc = 52.50%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.1, loss=1.43]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.75batch/s, val_acc=52.1, val_loss=1.37]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14; loss = 1.4340, acc = 51.08%  val loss = 1.3669, val acc = 52.09% & LR = 0.00010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=50, loss=1.43]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.75batch/s, val_acc=52.5, val_loss=1.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15; loss = 1.4252, acc = 49.96%  val loss = 1.3640, val acc = 52.48% & LR = 0.00010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=49.8, loss=1.41]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=52.9, val_loss=1.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16; loss = 1.4129, acc = 49.80%  val loss = 1.3605, val acc = 52.93% & LR = 0.00010\n",
      "Saving model with highest val_acc = 52.93%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.8, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.68batch/s, val_acc=52.6, val_loss=1.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17; loss = 1.4032, acc = 51.84%  val loss = 1.3574, val acc = 52.61% & LR = 0.00010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.4, loss=1.41]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=52.8, val_loss=1.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18; loss = 1.4062, acc = 51.44%  val loss = 1.3553, val acc = 52.79% & LR = 0.00010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.10batch/s, accuracy=51.2, loss=1.41]\n",
      "Validation: : 100%|██████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=53, val_loss=1.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19; loss = 1.4085, acc = 51.24%  val loss = 1.3522, val acc = 52.98% & LR = 0.00010\n",
      "Saving model with highest val_acc = 52.98%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=50.2, loss=1.41]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=53.1, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20; loss = 1.4057, acc = 50.18%  val loss = 1.3438, val acc = 53.06% & LR = 0.00001\n",
      "Saving model with highest val_acc = 53.06%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.5, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=53.5, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21; loss = 1.3974, acc = 51.52%  val loss = 1.3429, val acc = 53.49% & LR = 0.00001\n",
      "Saving model with highest val_acc = 53.49%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.8, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=53.1, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22; loss = 1.3966, acc = 51.78%  val loss = 1.3428, val acc = 53.15% & LR = 0.00001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=51.5, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=53.1, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23; loss = 1.4028, acc = 51.50%  val loss = 1.3449, val acc = 53.11% & LR = 0.00001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.13batch/s, accuracy=51.7, loss=1.4]\n",
      "Validation: : 100%|██████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=53, val_loss=1.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 24; loss = 1.4048, acc = 51.66%  val loss = 1.3480, val acc = 53.03% & LR = 0.00001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:06<00:00,  6.12batch/s, accuracy=50.9, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=53.5, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 25; loss = 1.3991, acc = 50.88%  val loss = 1.3402, val acc = 53.53% & LR = 0.00001\n",
      "Saving model with highest val_acc = 53.53%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, num_epochs + 1):\n",
    "    \n",
    "    # One epoch of training-\n",
    "    train_loss, train_acc = train_one_step(\n",
    "        model = model, train_loader = train_loader,\n",
    "        train_dataset = train_dataset\n",
    "    )\n",
    "    \n",
    "    # Get validation metrics after 1 epoch of training-\n",
    "    val_loss, val_acc = test_one_step(\n",
    "        model = model, test_loader = test_loader,\n",
    "        test_dataset = test_dataset\n",
    "    )\n",
    "    \n",
    "    scheduler.step()\n",
    "    \n",
    "    current_lr = optimizer.param_groups[0][\"lr\"]\n",
    "    \n",
    "    print(f\"Epoch: {epoch}; loss = {train_loss:.4f}, acc = {train_acc:.2f}%\",\n",
    "          f\" val loss = {val_loss:.4f}, val acc = {val_acc:.2f}% & LR = {current_lr:.5f}\"\n",
    "         )\n",
    "    \n",
    "    # Save training metrics to Python3 dict-\n",
    "    train_history[epoch] = {\n",
    "        'train_loss': train_loss, 'val_loss': val_loss,\n",
    "        'train_acc': train_acc, 'val_acc': val_acc,\n",
    "        'lr': current_lr\n",
    "    }\n",
    "    \n",
    "    # Save model with best validation accuracy-\n",
    "    if (val_acc > best_val_acc):\n",
    "        best_val_acc = val_acc\n",
    "        print(f\"Saving model with highest val_acc = {val_acc:.2f}%\\n\")\n",
    "        torch.save(model.state_dict(), \"ResNet18_STL10_best_val_acc.pth\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4afb89e5-4f50-4166-870c-c12a693a5a04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8c64d2-d3f3-421e-a469-a30c01f0bf01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cf13f04f-421e-49ee-bcff-92c45e46fae6",
   "metadata": {},
   "source": [
    "### Unfreeze all layers and train with lr of last epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3fcadb3e-3263-4ffb-b8ff-559c78f8fb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eaf95eec-72ff-4c19-8e2f-06f432561181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer: conv1.weight, shape: torch.Size([64, 3, 3, 3]), requires grad? True\n",
      "layer: bn1.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: bn1.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.0.conv1.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? True\n",
      "layer: layer1.0.bn1.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.0.bn1.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.0.conv2.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? True\n",
      "layer: layer1.0.bn2.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.0.bn2.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.1.conv1.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? True\n",
      "layer: layer1.1.bn1.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.1.bn1.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.1.conv2.weight, shape: torch.Size([64, 64, 3, 3]), requires grad? True\n",
      "layer: layer1.1.bn2.weight, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer1.1.bn2.bias, shape: torch.Size([64]), requires grad? True\n",
      "layer: layer2.0.conv1.weight, shape: torch.Size([128, 64, 3, 3]), requires grad? True\n",
      "layer: layer2.0.bn1.weight, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.0.bn1.bias, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.0.conv2.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? True\n",
      "layer: layer2.0.bn2.weight, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.0.bn2.bias, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.0.downsample.0.weight, shape: torch.Size([128, 64, 1, 1]), requires grad? True\n",
      "layer: layer2.0.downsample.1.weight, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.0.downsample.1.bias, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.1.conv1.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? True\n",
      "layer: layer2.1.bn1.weight, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.1.bn1.bias, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.1.conv2.weight, shape: torch.Size([128, 128, 3, 3]), requires grad? True\n",
      "layer: layer2.1.bn2.weight, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer2.1.bn2.bias, shape: torch.Size([128]), requires grad? True\n",
      "layer: layer3.0.conv1.weight, shape: torch.Size([256, 128, 3, 3]), requires grad? True\n",
      "layer: layer3.0.bn1.weight, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.0.bn1.bias, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.0.conv2.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? True\n",
      "layer: layer3.0.bn2.weight, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.0.bn2.bias, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.0.downsample.0.weight, shape: torch.Size([256, 128, 1, 1]), requires grad? True\n",
      "layer: layer3.0.downsample.1.weight, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.0.downsample.1.bias, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.1.conv1.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? True\n",
      "layer: layer3.1.bn1.weight, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.1.bn1.bias, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.1.conv2.weight, shape: torch.Size([256, 256, 3, 3]), requires grad? True\n",
      "layer: layer3.1.bn2.weight, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer3.1.bn2.bias, shape: torch.Size([256]), requires grad? True\n",
      "layer: layer4.0.conv1.weight, shape: torch.Size([512, 256, 3, 3]), requires grad? True\n",
      "layer: layer4.0.bn1.weight, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.0.bn1.bias, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.0.conv2.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? True\n",
      "layer: layer4.0.bn2.weight, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.0.bn2.bias, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.0.downsample.0.weight, shape: torch.Size([512, 256, 1, 1]), requires grad? True\n",
      "layer: layer4.0.downsample.1.weight, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.0.downsample.1.bias, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.1.conv1.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? True\n",
      "layer: layer4.1.bn1.weight, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.1.bn1.bias, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.1.conv2.weight, shape: torch.Size([512, 512, 3, 3]), requires grad? True\n",
      "layer: layer4.1.bn2.weight, shape: torch.Size([512]), requires grad? True\n",
      "layer: layer4.1.bn2.bias, shape: torch.Size([512]), requires grad? True\n",
      "layer: fc.weight, shape: torch.Size([10, 512]), requires grad? True\n",
      "layer: fc.bias, shape: torch.Size([10]), requires grad? True\n"
     ]
    }
   ],
   "source": [
    "# Sanity check-\n",
    "for layer_name, param in model.named_parameters():\n",
    "    print(f\"layer: {layer_name}, shape: {param.shape}, requires grad? {param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb1598c2-4a5e-4259-b27e-5f1f425fa77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(model.parameters(), lr = 1e-5, momentum = 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d1365b5-aa9b-4504-ab18-eee903b5ea9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decay lr at 20th and 30th epochs by a factor of 10-\n",
    "scheduler = torch.optim.lr_scheduler.MultiStepLR(\n",
    "    optimizer = optimizer, milestones = [20, 30],\n",
    "    gamma = 0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e987b589-8573-4e2d-bfd2-9ae8dbca8e79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3d0eab88-c1e5-46c8-b266-88dc5b707098",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train for 50 more epochs-\n",
    "num_epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887de8fb-cfee-4d02-a550-c2f2e8c3b92c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae1e0a3e-2d2e-4745-8b9d-7ca594242b47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:08<00:00,  4.88batch/s, accuracy=50.9, loss=1.4]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.76batch/s, val_acc=52.9, val_loss=1.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26; loss = 1.3971, acc = 50.88%  val loss = 1.3460, val acc = 52.95% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.07batch/s, accuracy=52.1, loss=1.39]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.69batch/s, val_acc=53.8, val_loss=1.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 27; loss = 1.3927, acc = 52.14%  val loss = 1.3341, val acc = 53.76% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 53.76%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=51.5, loss=1.38]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.68batch/s, val_acc=53.6, val_loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 28; loss = 1.3841, acc = 51.48%  val loss = 1.3371, val acc = 53.60% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:07<00:00,  5.07batch/s, accuracy=52, loss=1.38]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=53.7, val_loss=1.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 29; loss = 1.3755, acc = 52.00%  val loss = 1.3269, val acc = 53.68% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=51.9, loss=1.38]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=53.3, val_loss=1.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30; loss = 1.3825, acc = 51.90%  val loss = 1.3296, val acc = 53.31% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=51.7, loss=1.38]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=53.9, val_loss=1.31]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31; loss = 1.3797, acc = 51.66%  val loss = 1.3149, val acc = 53.93% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 53.93%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=51.4, loss=1.37]\n",
      "Validation: : 100%|██████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=54, val_loss=1.32]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 32; loss = 1.3717, acc = 51.42%  val loss = 1.3160, val acc = 53.98% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 53.98%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=52.7, loss=1.36]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=54.2, val_loss=1.31]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 33; loss = 1.3609, acc = 52.68%  val loss = 1.3092, val acc = 54.18% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 54.18%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=52.1, loss=1.36]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=54.1, val_loss=1.31]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 34; loss = 1.3635, acc = 52.08%  val loss = 1.3109, val acc = 54.09% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.6, loss=1.36]\n",
      "Validation: : 100%|█████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=54.4, val_loss=1.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 35; loss = 1.3608, acc = 53.56%  val loss = 1.2989, val acc = 54.35% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 54.35%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.1, loss=1.35]\n",
      "Validation: : 100%|█████████████████████████████████████| 63/63 [00:05<00:00, 11.69batch/s, val_acc=54.3, val_loss=1.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 36; loss = 1.3467, acc = 53.06%  val loss = 1.3012, val acc = 54.29% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=52.6, loss=1.35]\n",
      "Validation: : 100%|█████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=54.2, val_loss=1.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 37; loss = 1.3546, acc = 52.60%  val loss = 1.3016, val acc = 54.18% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=53.6, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.67batch/s, val_acc=54.4, val_loss=1.29]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 38; loss = 1.3435, acc = 53.56%  val loss = 1.2933, val acc = 54.41% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 54.41%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.2, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.57batch/s, val_acc=54.7, val_loss=1.29]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 39; loss = 1.3411, acc = 53.22%  val loss = 1.2868, val acc = 54.73% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 54.73%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.2, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.69batch/s, val_acc=54.5, val_loss=1.29]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40; loss = 1.3436, acc = 53.22%  val loss = 1.2905, val acc = 54.48% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.9, loss=1.34]\n",
      "Validation: : 100%|██████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55, val_loss=1.28]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41; loss = 1.3405, acc = 53.88%  val loss = 1.2847, val acc = 54.99% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 54.99%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.4, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.66batch/s, val_acc=54.9, val_loss=1.28]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 42; loss = 1.3359, acc = 53.44%  val loss = 1.2836, val acc = 54.88% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.5, loss=1.34]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.68batch/s, val_acc=54.9, val_loss=1.28]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 43; loss = 1.3364, acc = 53.46%  val loss = 1.2810, val acc = 54.91% & LR = 0.00001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.1, loss=1.33]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=55.4, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 44; loss = 1.3267, acc = 54.12%  val loss = 1.2710, val acc = 55.40% & LR = 0.00001000\n",
      "Saving model with highest val_acc = 55.40%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.1, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.3, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 45; loss = 1.3168, acc = 54.06%  val loss = 1.2682, val acc = 55.26% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.6, loss=1.33]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=55.3, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46; loss = 1.3311, acc = 54.56%  val loss = 1.2657, val acc = 55.31% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.5, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.75batch/s, val_acc=54.9, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 47; loss = 1.3157, acc = 54.46%  val loss = 1.2719, val acc = 54.91% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.3, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 48; loss = 1.3224, acc = 54.30%  val loss = 1.2672, val acc = 55.16% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.1, loss=1.32]\n",
      "Validation: : 100%|██████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=55, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 49; loss = 1.3160, acc = 54.12%  val loss = 1.2701, val acc = 55.03% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.7, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=55.3, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50; loss = 1.3208, acc = 53.66%  val loss = 1.2678, val acc = 55.28% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=55, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.4, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 51; loss = 1.3090, acc = 54.96%  val loss = 1.2743, val acc = 55.40% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.9, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=55.4, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 52; loss = 1.3181, acc = 53.88%  val loss = 1.2666, val acc = 55.40% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.5, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.68batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 53; loss = 1.3239, acc = 53.54%  val loss = 1.2689, val acc = 55.19% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=54.5, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=55.3, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 54; loss = 1.3135, acc = 54.52%  val loss = 1.2703, val acc = 55.26% & LR = 0.00000100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.5, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.1, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 55; loss = 1.3124, acc = 54.48%  val loss = 1.2692, val acc = 55.15% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=53.7, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.1, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 56; loss = 1.3200, acc = 53.68%  val loss = 1.2699, val acc = 55.11% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.5, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 57; loss = 1.3187, acc = 54.50%  val loss = 1.2675, val acc = 55.25% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=55.1, loss=1.3]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.74batch/s, val_acc=55.5, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 58; loss = 1.3044, acc = 55.12%  val loss = 1.2678, val acc = 55.45% & LR = 0.00000010\n",
      "Saving model with highest val_acc = 55.45%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.8, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.1, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 59; loss = 1.3103, acc = 53.84%  val loss = 1.2691, val acc = 55.08% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=53.7, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 60; loss = 1.3208, acc = 53.74%  val loss = 1.2657, val acc = 55.21% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.8, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 61; loss = 1.3220, acc = 53.80%  val loss = 1.2695, val acc = 55.17% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.4, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 62; loss = 1.3188, acc = 54.00%  val loss = 1.2694, val acc = 55.39% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.1, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.6, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 63; loss = 1.3166, acc = 54.08%  val loss = 1.2653, val acc = 55.58% & LR = 0.00000010\n",
      "Saving model with highest val_acc = 55.58%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.4, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 64; loss = 1.3180, acc = 54.36%  val loss = 1.2696, val acc = 55.16% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.3, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.70batch/s, val_acc=55.1, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 65; loss = 1.3213, acc = 54.28%  val loss = 1.2746, val acc = 55.06% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 66; loss = 1.3203, acc = 53.98%  val loss = 1.2668, val acc = 55.23% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.6, loss=1.33]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.69batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 67; loss = 1.3253, acc = 53.56%  val loss = 1.2726, val acc = 55.16% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.4, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.3, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 68; loss = 1.3135, acc = 54.40%  val loss = 1.2699, val acc = 55.33% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=53.4, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.6, val_loss=1.26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 69; loss = 1.3214, acc = 53.44%  val loss = 1.2638, val acc = 55.58% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.7, loss=1.31]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.72batch/s, val_acc=55.6, val_loss=1.26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 70; loss = 1.3106, acc = 54.70%  val loss = 1.2619, val acc = 55.59% & LR = 0.00000010\n",
      "Saving model with highest val_acc = 55.59%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=54.2, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 71; loss = 1.3190, acc = 54.18%  val loss = 1.2662, val acc = 55.21% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.1, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.2, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 72; loss = 1.3209, acc = 54.14%  val loss = 1.2682, val acc = 55.24% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.04batch/s, accuracy=54.4, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.1, val_loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 73; loss = 1.3187, acc = 54.36%  val loss = 1.2716, val acc = 55.13% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.06batch/s, accuracy=54.5, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.73batch/s, val_acc=54.9, val_loss=1.28]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 74; loss = 1.3180, acc = 54.50%  val loss = 1.2779, val acc = 54.89% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████| 40/40 [00:07<00:00,  5.05batch/s, accuracy=54.3, loss=1.32]\n",
      "Validation: : 100%|████████████████████████████████████| 63/63 [00:05<00:00, 11.71batch/s, val_acc=55.4, val_loss=1.27]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 75; loss = 1.3218, acc = 54.28%  val loss = 1.2674, val acc = 55.36% & LR = 0.00000010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(26, 25 + num_epochs + 1):\n",
    "    \n",
    "    # One epoch of training-\n",
    "    train_loss, train_acc = train_one_step(\n",
    "        model = model, train_loader = train_loader,\n",
    "        train_dataset = train_dataset\n",
    "    )\n",
    "    \n",
    "    # Get validation metrics after 1 epoch of training-\n",
    "    val_loss, val_acc = test_one_step(\n",
    "        model = model, test_loader = test_loader,\n",
    "        test_dataset = test_dataset\n",
    "    )\n",
    "    \n",
    "    scheduler.step()\n",
    "    \n",
    "    current_lr = optimizer.param_groups[0][\"lr\"]\n",
    "    \n",
    "    print(f\"Epoch: {epoch}; loss = {train_loss:.4f}, acc = {train_acc:.2f}%\",\n",
    "          f\" val loss = {val_loss:.4f}, val acc = {val_acc:.2f}% & LR = {current_lr:.8f}\"\n",
    "         )\n",
    "    \n",
    "    # Save training metrics to Python3 dict-\n",
    "    train_history[epoch] = {\n",
    "        'train_loss': train_loss, 'val_loss': val_loss,\n",
    "        'train_acc': train_acc, 'val_acc': val_acc,\n",
    "        'lr': current_lr\n",
    "    }\n",
    "    \n",
    "    # Save model with best validation accuracy-\n",
    "    if (val_acc > best_val_acc):\n",
    "        best_val_acc = val_acc\n",
    "        print(f\"Saving model with highest val_acc = {val_acc:.2f}%\\n\")\n",
    "        torch.save(model.state_dict(), \"ResNet18_STL10_best_val_acc.pth\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "609f338c-431e-4bff-9235-06b86e68e198",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c54a04bd-a63f-4037-91a0-94fa506c66f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training metrics as pickled object-\n",
    "with open(\"ResNet18_STL10_training_history.pkl\", \"wb\") as file:\n",
    "    pickle.dump(train_history, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5210ebfb-5470-418c-af15-08ae225d3d10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4c20943-e5ad-4479-9dac-051e44e5d1c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "31c0929d-75f4-4c86-8ca4-d9bc6a5fa728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 7))\n",
    "plt.plot([train_history[e]['lr'] for e in train_history.keys()])\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"lr\")\n",
    "plt.title(\"LR Training\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3e8f3d5b-4f21-4b07-a1e3-bdab3f5f75de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7, 6))\n",
    "plt.plot([train_history[e]['train_loss'] for e in train_history.keys()], label = 'loss')\n",
    "plt.plot([train_history[e]['val_loss'] for e in train_history.keys()], label = 'val loss')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.title(\"Loss Visualization Training\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4c38ecaf-cbd3-4d59-b7e1-6d981a4f719e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7, 6))\n",
    "plt.plot([train_history[e]['train_acc'] for e in train_history.keys()], label = 'acc')\n",
    "plt.plot([train_history[e]['val_acc'] for e in train_history.keys()], label = 'val acc')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"acc %\")\n",
    "plt.title(\"Accuracy Visualization Training\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e93ac60-3f2c-4f8f-8b11-6ed6c4a465e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e57fe45-3b61-4647-b275-53c479678831",
   "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
}