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{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQEKXT7Z7T0M"
      },
      "source": [
        "# PyTorch CNN Tutorial - SCR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiJA6hRb7fOJ"
      },
      "source": [
        "First of all import all packages needed in this problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gjzSNb_Hw1dB"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "# import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from torch.utils.data import Dataset, TensorDataset, DataLoader, random_split\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.models as models\n",
        "from sklearn.metrics import (accuracy_score, confusion_matrix, precision_score,recall_score)\n",
        "from torchvision.transforms import ToTensor, Resize, Lambda, RandomHorizontalFlip\n",
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lkYUp8QWn6al"
      },
      "source": [
        "## Load CIFAR10 dataset:\n",
        "\n",
        "In the first step, load CIFAR10 with torchvision and split it to train, valid and test data. Also in this step, convert each label to onehot vector with lambda function.\n",
        "\n",
        "At the end, visualize one sample of each class in CIFAR10 dataset randomly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "RP6GtbrAoXm6",
        "outputId": "ea2e757c-e285-40f8-8dc3-f323da3b5976"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using cuda device\n",
            "\n",
            "\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3500x2500 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# number of mini-batch size:\n",
        "Batch_size=32\n",
        "\n",
        "# Define input image size\n",
        "img_size=(32,32,3)\n",
        "\n",
        "# Selecting the appropriate training device:\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Using {device} device\\n\\n\")\n",
        "\n",
        "# Define a train and test Transform to resize image and convert to tensor:\n",
        "train_transforms = torchvision.transforms.transforms.Compose(\n",
        "        [Resize([32, 32]),\n",
        "         RandomHorizontalFlip(),\n",
        "         ToTensor()])\n",
        "\n",
        "test_transforms = torchvision.transforms.transforms.Compose(\n",
        "        [Resize([32, 32]),\n",
        "         ToTensor()])\n",
        "# load Cifar10 dataset with torchvision: (Use lambda to convert each label to onehot vector)\n",
        "dataset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=train_transforms, download=True,\n",
        "                                       target_transform = Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)))\n",
        "\n",
        "# Split dataset to train_set and validation_set (85%-15%):\n",
        "train_size = int(0.85 * len(dataset.data))\n",
        "valid_size = len(dataset.data) - train_size\n",
        "train_set, val_set = random_split(dataset, [train_size, valid_size])\n",
        "\n",
        "# Use Dataloader to ordination train_set and validation_set according to it's mini-batch size:\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=Batch_size, shuffle=True )\n",
        "validate_loader = torch.utils.data.DataLoader(val_set, batch_size=Batch_size, shuffle=True)\n",
        "\n",
        "# load Cifar10 Test dataset with torchvision and load them with dataloader:\n",
        "Test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=test_transforms, download=True,\n",
        "                                            target_transform = Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)))\n",
        "test_loader = torch.utils.data.DataLoader(Test_dataset,batch_size=Batch_size, shuffle=True)\n",
        "\n",
        "# define a convert function to convert each onehot vector to specific label of that class.\n",
        "def convert(vector):\n",
        "     label=torch.where(vector==1)[0].item()\n",
        "     Classes= {0:'airplane',1:'automobile',2:'bird',3:'cat',4:'deer',5:'dog',6:'frog',7:'horse',8:'ship',9:'truck'} \n",
        "     return Classes[label]\n",
        "print(\"\\n\")\n",
        "\n",
        "# Divide data according to the their classes(separate each class and their data):\n",
        "sample_Indexs=[]\n",
        "for i in range(10) :sample_Indexs.append([] )\n",
        "for num in range(0,len(dataset.targets)):\n",
        "    sample_Indexs[dataset.targets[num]].append(num)\n",
        "\n",
        "# select a random data of each class and plot it with it's label.\n",
        "fig=plt.figure(figsize=(35,25),facecolor='w')\n",
        "for i in range(10):\n",
        "     rand_num= np.random.randint(1,50,1).item()\n",
        "     img, label = dataset[sample_Indexs[i][rand_num]]\n",
        "     ax = plt.subplot(1,10, i+1)\n",
        "     plt.imshow(img.permute(1,2,0))\n",
        "     ax.set_title(f\"Label = {convert(label)}\", fontsize=15)\n",
        "     ax.get_xaxis().set_visible(False)\n",
        "     ax.get_yaxis().set_visible(False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-c3jim8lyxlZ"
      },
      "source": [
        "## Implementation of a CNN based Image Classifier from Scratch:\n",
        "\n",
        "First of all, Define a train and test loop for our training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-gvYX3uov-5H"
      },
      "outputs": [],
      "source": [
        "def train_loop(dataloader, model, loss_fn, optimizer):\n",
        "    model.train()\n",
        "    size = len(dataloader.dataset)\n",
        "    for batch, (img, y) in enumerate(dataloader):\n",
        "        img=img.to(device)\n",
        "        y=y.to(device)\n",
        "        # Compute prediction and loss\n",
        "        pred = model(img)\n",
        "        loss = loss_fn(pred, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch % 100 == 0:\n",
        "            loss, current = loss.item(), batch * len(img)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
        "\n",
        "\n",
        "def test_loop(dataloader, model, loss_fn):\n",
        "    model.eval()\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    valid_loss, correct = 0, 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for img, y in dataloader:\n",
        "            img=img.to(device)\n",
        "            y=y.to(device)\n",
        "            pred = model(img)\n",
        "            valid_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
        "\n",
        "    valid_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {valid_loss:>8f} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRmVuDyXULgU"
      },
      "source": [
        "### Secondly, Define a CNN model:\n",
        "\n",
        "According to the Torch API we have: [Source](https://pytorch.org/docs/stable/nn.html)\n",
        "\n",
        "- torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)\n",
        "\n",
        "- torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)\n",
        "\n",
        "- torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=None, dtype=None)\n",
        "\n",
        "- torch.nn.Dropout2d(p=0.5, inplace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tNc4KrlDoX2G"
      },
      "outputs": [],
      "source": [
        "# Creating the model\n",
        "class ConvNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ConvNet, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            #Input = 3 x 32 x 32, Output = 32 x 32 x 32\n",
        "            nn.Conv2d(in_channels = 3, out_channels = 32, kernel_size = 3, padding = 'same'), \n",
        "            nn.ReLU(),\n",
        "            nn.Dropout2d(0.05),\n",
        "            #Input = 32 x 32 x 32, Output = 32 x 16 x 16\n",
        "            nn.MaxPool2d(kernel_size=2),\n",
        "            nn.BatchNorm2d(32),\n",
        "  \n",
        "            #Input = 32 x 16 x 16, Output = 64 x 16 x 16\n",
        "            nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1),\n",
        "            nn.ReLU(),\n",
        "            #Input = 64 x 16 x 16, Output = 64 x 8 x 8\n",
        "            nn.MaxPool2d(kernel_size=2),\n",
        "              \n",
        "            #Input = 64 x 8 x 8, Output = 64 x 8 x 8\n",
        "            nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, padding = 1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(),\n",
        "            #Input = 64 x 8 x 8, Output = 64 x 4 x 4\n",
        "            nn.MaxPool2d(kernel_size=2),\n",
        "  \n",
        "            nn.Flatten(),\n",
        "            nn.BatchNorm1d(64*4*4),\n",
        "            nn.Linear(64*4*4, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.Linear(128, 32),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(32, 10)\n",
        "        )\n",
        "  \n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.model(x)\n",
        "        x = F.softmax(x, dim=1)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zvtFQ2uv1P7",
        "outputId": "98bb2658-3efd-49a0-cc4e-2adf1f78a2f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Network Architecture:\n",
            "ConvNet(\n",
            "  (model): Sequential(\n",
            "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
            "    (1): ReLU()\n",
            "    (2): Dropout2d(p=0.05, inplace=False)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (6): ReLU()\n",
            "    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (9): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (10): ReLU()\n",
            "    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (12): Flatten(start_dim=1, end_dim=-1)\n",
            "    (13): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (14): Linear(in_features=1024, out_features=128, bias=True)\n",
            "    (15): ReLU()\n",
            "    (16): Dropout(p=0.1, inplace=False)\n",
            "    (17): Linear(in_features=128, out_features=32, bias=True)\n",
            "    (18): ReLU()\n",
            "    (19): Linear(in_features=32, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "CNN_model = ConvNet().to(device)\n",
        "\n",
        "for param in CNN_model.parameters():\n",
        "    param.requires_grad = True\n",
        "CNN_model.train()    \n",
        "print(\"Network Architecture:\")\n",
        "print(CNN_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUx4gNWcwKcd",
        "outputId": "170acda5-a494-40fa-eaca-709d66bc0bfd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 2.302272  [    0/42500]\n",
            "loss: 2.219692  [ 3200/42500]\n",
            "loss: 2.149276  [ 6400/42500]\n",
            "loss: 2.187615  [ 9600/42500]\n",
            "loss: 2.091666  [12800/42500]\n",
            "loss: 2.019790  [16000/42500]\n",
            "loss: 2.123006  [19200/42500]\n",
            "loss: 2.125691  [22400/42500]\n",
            "loss: 2.040968  [25600/42500]\n",
            "loss: 2.073212  [28800/42500]\n",
            "loss: 2.027201  [32000/42500]\n",
            "loss: 2.023804  [35200/42500]\n",
            "loss: 2.117084  [38400/42500]\n",
            "loss: 1.960984  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 45.8%, Avg loss: 2.016824 \n",
            "\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 2.111652  [    0/42500]\n",
            "loss: 1.910316  [ 3200/42500]\n",
            "loss: 2.048353  [ 6400/42500]\n",
            "loss: 2.028718  [ 9600/42500]\n",
            "loss: 2.021635  [12800/42500]\n",
            "loss: 2.013133  [16000/42500]\n",
            "loss: 2.057028  [19200/42500]\n",
            "loss: 2.031589  [22400/42500]\n",
            "loss: 1.955248  [25600/42500]\n",
            "loss: 1.870217  [28800/42500]\n",
            "loss: 1.897251  [32000/42500]\n",
            "loss: 1.944971  [35200/42500]\n",
            "loss: 1.867519  [38400/42500]\n",
            "loss: 1.865793  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 50.8%, Avg loss: 1.974163 \n",
            "\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 2.100747  [    0/42500]\n",
            "loss: 2.107758  [ 3200/42500]\n",
            "loss: 1.999364  [ 6400/42500]\n",
            "loss: 1.983569  [ 9600/42500]\n",
            "loss: 1.972469  [12800/42500]\n",
            "loss: 1.927096  [16000/42500]\n",
            "loss: 1.996785  [19200/42500]\n",
            "loss: 1.987665  [22400/42500]\n",
            "loss: 1.944978  [25600/42500]\n",
            "loss: 2.080106  [28800/42500]\n",
            "loss: 1.964132  [32000/42500]\n",
            "loss: 1.935995  [35200/42500]\n",
            "loss: 1.884246  [38400/42500]\n",
            "loss: 1.951888  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 51.3%, Avg loss: 1.971815 \n",
            "\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 2.049090  [    0/42500]\n",
            "loss: 1.915015  [ 3200/42500]\n",
            "loss: 2.004883  [ 6400/42500]\n",
            "loss: 1.921412  [ 9600/42500]\n",
            "loss: 1.912034  [12800/42500]\n",
            "loss: 2.026011  [16000/42500]\n",
            "loss: 2.000444  [19200/42500]\n",
            "loss: 1.908592  [22400/42500]\n",
            "loss: 1.950819  [25600/42500]\n",
            "loss: 1.994440  [28800/42500]\n",
            "loss: 2.037249  [32000/42500]\n",
            "loss: 1.888280  [35200/42500]\n",
            "loss: 2.053716  [38400/42500]\n",
            "loss: 1.947366  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 52.8%, Avg loss: 1.964727 \n",
            "\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 1.909482  [    0/42500]\n",
            "loss: 2.028015  [ 3200/42500]\n",
            "loss: 1.939408  [ 6400/42500]\n",
            "loss: 1.939403  [ 9600/42500]\n",
            "loss: 1.954373  [12800/42500]\n",
            "loss: 2.026207  [16000/42500]\n",
            "loss: 1.855179  [19200/42500]\n",
            "loss: 2.066285  [22400/42500]\n",
            "loss: 1.922667  [25600/42500]\n",
            "loss: 2.099559  [28800/42500]\n",
            "loss: 1.879844  [32000/42500]\n",
            "loss: 2.039129  [35200/42500]\n",
            "loss: 2.016253  [38400/42500]\n",
            "loss: 2.056733  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 53.7%, Avg loss: 1.970545 \n",
            "\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# Defining the model hyper parameters:\n",
        "lr = 2e-4\n",
        "epochs = 5\n",
        "weight_decay = 0.01\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(filter(lambda param: param.requires_grad, CNN_model.parameters()), lr=lr,  weight_decay = weight_decay)\n",
        "\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train_loop(train_loader, CNN_model, loss_fn, optimizer)\n",
        "    test_loop(validate_loader, CNN_model, loss_fn)\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75V3yNo2zkOC"
      },
      "source": [
        "### Evaluation:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEK3PF9XSfAQ",
        "outputId": "0d6d3a1d-31a5-4af8-99d2-cdafc937ce9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation results for train data:\n",
            "Error: \n",
            " Accuracy: 55.8%, Avg loss: 1.955975 \n",
            "\n",
            "Evaluation results for validate data:\n",
            "Error: \n",
            " Accuracy: 53.7%, Avg loss: 1.970314 \n",
            "\n",
            "Evaluation results for test data:\n",
            "Error: \n",
            " Accuracy: 54.8%, Avg loss: 1.964239 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "CNN_model.eval()\n",
        "loss_fn_eval = nn.CrossEntropyLoss()\n",
        "# CNN evaluation\n",
        "print(\"Evaluation results for train data:\")\n",
        "test_loop(train_loader, CNN_model, loss_fn_eval)\n",
        "print(\"Evaluation results for validate data:\")\n",
        "test_loop(validate_loader, CNN_model, loss_fn_eval)\n",
        "print(\"Evaluation results for test data:\")\n",
        "test_loop(test_loader, CNN_model, loss_fn_eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-MpXbzHVySyl",
        "outputId": "1453df95-0e9e-4f9e-92a7-a9a71b043eea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy for class: airplane is 74.7 %\n",
            "Accuracy for class: automobile is 75.9 %\n",
            "Accuracy for class: bird  is 0.0 %\n",
            "Accuracy for class: cat   is 0.0 %\n",
            "Accuracy for class: deer  is 61.8 %\n",
            "Accuracy for class: dog   is 0.0 %\n",
            "Accuracy for class: frog  is 88.8 %\n",
            "Accuracy for class: horse is 80.7 %\n",
            "Accuracy for class: ship  is 82.5 %\n",
            "Accuracy for class: truck is 83.6 %\n"
          ]
        }
      ],
      "source": [
        "Cifar10_classes = ('airplane','automobile','bird','cat',\n",
        "                   'deer','dog','frog','horse','ship','truck')\n",
        "# prepare to count predictions for each class\n",
        "correct_pred = {classname: 0 for classname in Cifar10_classes}\n",
        "total_pred = {classname: 0 for classname in Cifar10_classes}\n",
        "\n",
        "# again no gradients needed\n",
        "with torch.no_grad():\n",
        "    for data in test_loader:\n",
        "        img, y = data\n",
        "        img=img.to(device)\n",
        "        y=y.to(device)\n",
        "        labels = y.argmax(1)\n",
        "        pred = CNN_model(img)\n",
        "        predictions = pred.argmax(1)\n",
        "        # collect the correct predictions for each class\n",
        "        for label, prediction in zip(labels, predictions):\n",
        "            if label == prediction:\n",
        "                correct_pred[Cifar10_classes[label]] += 1\n",
        "            total_pred[Cifar10_classes[label]] += 1\n",
        "\n",
        "\n",
        "# print accuracy for each class\n",
        "for classname, correct_count in correct_pred.items():\n",
        "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Qs6lWVnzpRK"
      },
      "source": [
        "## Transfer Learning:\n",
        "\n",
        "We can use PyTorch for loading pre-trained model, Also we can use Timm ([Medium](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055)) library to load a variety of pre-trained models trained on diferent datasets.(PyTorch image models: [github](https://github.com/huggingface/pytorch-image-models))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydWyJk-J_jjV",
        "outputId": "162a9e1d-f208-4a94-d8bc-80bdd49a65ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "# Define input image size\n",
        "img_size=(224,224,3)\n",
        "\n",
        "# Define a train and test Transform to resize image and convert to tensor:\n",
        "train_transforms = torchvision.transforms.transforms.Compose(\n",
        "        [Resize([224, 224]),\n",
        "         RandomHorizontalFlip(),\n",
        "         ToTensor()])\n",
        "\n",
        "test_transforms = torchvision.transforms.transforms.Compose(\n",
        "        [Resize([224, 224]),\n",
        "         ToTensor()])\n",
        "# load Cifar10 dataset with torchvision: (Use lambda to convert each label to onehot vector)\n",
        "dataset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=train_transforms, download=True,\n",
        "                                       target_transform = Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)))\n",
        "\n",
        "# Split dataset to train_set and validation_set (85%-15%):\n",
        "train_size = int(0.85 * len(dataset.data))\n",
        "valid_size = len(dataset.data) - train_size\n",
        "train_set, val_set = random_split(dataset, [train_size, valid_size])\n",
        "\n",
        "# Use Dataloader to ordination train_set and validation_set according to it's mini-batch size:\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=Batch_size, shuffle=True )\n",
        "validate_loader = torch.utils.data.DataLoader(val_set, batch_size=Batch_size, shuffle=True)\n",
        "\n",
        "# load Cifar10 Test dataset with torchvision and load them with dataloader:\n",
        "Test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=test_transforms, download=True,\n",
        "                                            target_transform = Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)))\n",
        "test_loader = torch.utils.data.DataLoader(Test_dataset,batch_size=Batch_size, shuffle=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "4gI8YdsTzxvM"
      },
      "outputs": [],
      "source": [
        "# Load pre-trained ResNet50 on imageNet\n",
        "based_model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).to(device)\n",
        "# Freeze all parameters    \n",
        "for param in based_model.parameters():\n",
        "    param.requires_grad = False   \n",
        "# Modified FC layers\n",
        "based_model.fc = nn.Sequential(\n",
        "               nn.BatchNorm1d(based_model.fc.in_features),\n",
        "               nn.Linear(based_model.fc.in_features, 128),\n",
        "               nn.ReLU(),\n",
        "               nn.Dropout(0.25),\n",
        "               nn.Linear(128, 10),\n",
        "               nn.Softmax(dim=1)).to(device)\n",
        "# Set all parameters in FC layer requires grad to update it's weights.\n",
        "for param in based_model.fc.parameters():\n",
        "    param.requires_grad = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WU15K2Az8O4"
      },
      "source": [
        "### Train the Fully Connected layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnyrJ4yNz63T",
        "outputId": "410a4084-5ba2-4814-fcad-04c65f66567a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 2.304326  [    0/42500]\n",
            "loss: 2.042099  [ 3200/42500]\n",
            "loss: 1.832334  [ 6400/42500]\n",
            "loss: 1.895695  [ 9600/42500]\n",
            "loss: 1.841786  [12800/42500]\n",
            "loss: 1.794591  [16000/42500]\n",
            "loss: 1.678760  [19200/42500]\n",
            "loss: 1.785957  [22400/42500]\n",
            "loss: 1.831340  [25600/42500]\n",
            "loss: 1.788245  [28800/42500]\n",
            "loss: 1.747087  [32000/42500]\n",
            "loss: 1.806434  [35200/42500]\n",
            "loss: 1.766329  [38400/42500]\n",
            "loss: 1.779386  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 79.7%, Avg loss: 1.714312 \n",
            "\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 1.745071  [    0/42500]\n",
            "loss: 1.619226  [ 3200/42500]\n",
            "loss: 1.729790  [ 6400/42500]\n",
            "loss: 1.778083  [ 9600/42500]\n",
            "loss: 1.761370  [12800/42500]\n",
            "loss: 1.755188  [16000/42500]\n",
            "loss: 1.824540  [19200/42500]\n",
            "loss: 1.704269  [22400/42500]\n",
            "loss: 1.784478  [25600/42500]\n",
            "loss: 1.724730  [28800/42500]\n",
            "loss: 1.751894  [32000/42500]\n",
            "loss: 1.666143  [35200/42500]\n",
            "loss: 1.673313  [38400/42500]\n",
            "loss: 1.701102  [41600/42500]\n",
            "Error: \n",
            " Accuracy: 80.1%, Avg loss: 1.710152 \n",
            "\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# Defining the model hyper parameters:\n",
        "lr = 1e-4\n",
        "epochs = 2\n",
        "weight_decay = 0.01\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(filter(lambda param: param.requires_grad, based_model.parameters()), lr=lr, weight_decay = weight_decay)\n",
        "\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train_loop(train_loader, based_model, loss_fn, optimizer)\n",
        "    test_loop(validate_loader, based_model, loss_fn)\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeAsMevZ2XoL"
      },
      "source": [
        "### Evaluating:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwLzmDZY0IUn",
        "outputId": "8bd940cd-eb6a-4c32-e4c8-b67c6060ca04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation results for train data:\n",
            "Error: \n",
            " Accuracy: 80.2%, Avg loss: 1.707139 \n",
            "\n",
            "Evaluation results for validate data:\n",
            "Error: \n",
            " Accuracy: 80.1%, Avg loss: 1.711155 \n",
            "\n",
            "Evaluation results for test data:\n",
            "Error: \n",
            " Accuracy: 79.4%, Avg loss: 1.713852 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# compute model accuracy and Ave loss on train, valid and test data.\n",
        "based_model.eval()\n",
        "# Define cross entropy loss function\n",
        "loss_fn_eval = nn.CrossEntropyLoss()\n",
        "# based_model evaluation\n",
        "print(\"Evaluation results for train data:\")\n",
        "test_loop(train_loader, based_model, loss_fn_eval)\n",
        "print(\"Evaluation results for validate data:\")\n",
        "test_loop(validate_loader, based_model, loss_fn_eval)\n",
        "print(\"Evaluation results for test data:\")\n",
        "test_loop(test_loader, based_model, loss_fn_eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oIkISnBi0Pc2",
        "outputId": "fdf6e2d8-de24-4f90-9919-b56208c3a08e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "Test Accuracy: 79.45 %\n",
            "Test Precision: 79.38 %\n",
            "Test Recall: 79.45 %\n"
          ]
        }
      ],
      "source": [
        "# Confusion Matrix for Test Data\n",
        "preds_test = torch.zeros(len(Test_dataset))\n",
        "true_label = torch.zeros(len(Test_dataset))\n",
        "for i in range(len(Test_dataset)):\n",
        "    true_label[i] = Test_dataset[i][1].to(device).argmax()\n",
        "    with torch.no_grad():\n",
        "        preds_test[i] = based_model((Test_dataset[i][0][None,...]).to(device)).argmax()\n",
        "\n",
        "\n",
        "test_acc = accuracy_score(true_label.numpy(), preds_test.numpy())\n",
        "test_prc = precision_score(true_label.numpy(), preds_test.numpy(), average='macro')\n",
        "test_rcl = recall_score(true_label.numpy(), preds_test.numpy(), average='macro')\n",
        "test_cf = confusion_matrix(true_label.numpy(), preds_test.numpy())\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(test_cf, xticklabels=Cifar10_classes, yticklabels=Cifar10_classes, annot=True, fmt='.0f')\n",
        "plt.title('Confusion Matrix for Test Data')\n",
        "plt.show()\n",
        "print(\"\\n\\n\")\n",
        "print(f\"Test Accuracy: {test_acc*100:.2f} %\")\n",
        "print(f\"Test Precision: {test_prc*100:.2f} %\")\n",
        "print(f\"Test Recall: {test_rcl*100:.2f} %\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsL1Z-PeTP4S"
      },
      "source": [
        "## Training a model without transfer learning:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rBTHnIvoTa3e"
      },
      "outputs": [],
      "source": [
        "# Load just ResNet18 model without any pre-trained weights:\n",
        "resnet18_model = models.resnet18(weights=None).to(device)\n",
        "# Modified FC layers\n",
        "resnet18_model.fc = nn.Sequential(\n",
        "               nn.BatchNorm1d(resnet18_model.fc.in_features),\n",
        "               nn.Linear(resnet18_model.fc.in_features, 10),\n",
        "               nn.Softmax(dim=1)).to(device)\n",
        "# Set all parameters in resnet18 requires grad to update it's weights.\n",
        "for param in resnet18_model.parameters():\n",
        "    param.requires_grad = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45GplLPfTt3h",
        "outputId": "12ec0e04-3e64-491e-938f-c6903c00fca6"
      },
      "outputs": [],
      "source": [
        "# Defining the model hyper parameters:\n",
        "lr = 2e-4\n",
        "epochs = 2\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(filter(lambda param: param.requires_grad, resnet18_model.parameters()), lr=lr)\n",
        "\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train_loop(train_loader, resnet18_model, loss_fn, optimizer)\n",
        "    test_loop(validate_loader, resnet18_model, loss_fn)\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyIrJCdPTyNc",
        "outputId": "fdd2bf73-260c-4fa4-8bcd-b6362a6d3290"
      },
      "outputs": [],
      "source": [
        "# compute model accuracy and Ave loss on train, valid and test data.\n",
        "resnet18_model.eval()\n",
        "# Define cross entropy loss function\n",
        "loss_fn_eval = nn.CrossEntropyLoss()\n",
        "# resnet18_model evaluation\n",
        "print(\"Evaluation results for train data:\")\n",
        "test_loop(train_loader, resnet18_model, loss_fn_eval)\n",
        "print(\"Evaluation results for validate data:\")\n",
        "test_loop(validate_loader, resnet18_model, loss_fn_eval)\n",
        "print(\"Evaluation results for test data:\")\n",
        "test_loop(test_loader, resnet18_model, loss_fn_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0SUowFiAzX_"
      },
      "source": [
        "## Knowledge Distillation: [Source](https://intellabs.github.io/distiller/knowledge_distillation.html)\n",
        "\n",
        "\n",
        " Sometiems it is not efficient to utilize high parameter networks for minor applications moreover, the small networks might not be as powerfull as the bigger ones so one way to increase the accuracy of the model is to use a technique called knowledge distillation. Training the resnet18 model according to the **Knowledge Distillation** from a teacher (resnet50):\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ddtTfKIAtnx"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-64mdLbEZwf"
      },
      "source": [
        "In distillation, knowledge is transferred from the teacher model to the student by minimizing a loss function in which the target is the distribution of class probabilities predicted by the teacher model. That is - the output of a softmax function on the teacher model's logits. However, in many cases, this probability distribution has the correct class at a very high probability, with all other class probabilities very close to 0. As such, it doesn't provide much information beyond the ground truth labels already provided in the dataset. To tackle this issue, [Hinton et al](Geoffrey Hinton, Oriol Vinyals and Jeff Dean. Distilling the Knowledge in a Neural Network. arxiv:1503.02531)., 2015 introduced the concept of \"softmax temperature\". The probability pi\n",
        " of class i\n",
        " is calculated from the logits z\n",
        " as:\n",
        "\n",
        "![3.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJ6-HDwKEP42"
      },
      "source": [
        "The overall loss function, incorporating both distillation and student losses, is calculated as:\n",
        "\n",
        "![2.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIciSgopD8cg"
      },
      "outputs": [],
      "source": [
        "# calculate softmax temperature\n",
        "def softmax_temperature(Z, Temp=2):\n",
        "    return torch.exp(Z/Temp)/(torch.unsqueeze(torch.sum(torch.exp(Z/Temp),dim=1),1))\n",
        "\n",
        "# # calculate cross entropy\n",
        "def cross_entropy(y_true,y_pred):\n",
        "    y_true=y_true.to(device)\n",
        "    y_pred=y_pred.to(device)\n",
        "    N_batch=len(y_true)\n",
        "    return -((y_true* torch.log(y_pred)).sum() /N_batch)\n",
        "\n",
        "# Calculate distiller_loss \n",
        "def distiller_loss(Z_t, Z_s, y_true, Temp=2, alpha=0.1):\n",
        "    return (1-alpha)*cross_entropy(y_true,softmax_temperature(Z_s, Temp=1))+ alpha*(Temp**2)*cross_entropy(softmax_temperature(Z_t, Temp=Temp),softmax_temperature(Z_s, Temp=Temp))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QpMLhkuAHkxm"
      },
      "outputs": [],
      "source": [
        "def teacher_train_loop(dataloader, model, loss_fn, optimizer):\n",
        "    model.train()\n",
        "    size = len(dataloader.dataset)\n",
        "    for batch, (img, y) in enumerate(dataloader):\n",
        "        img=img.to(device)\n",
        "        y=y.to(device)\n",
        "        # Compute prediction and loss\n",
        "        pred = model(img)\n",
        "        pred= softmax_temperature(pred, Temp=2)\n",
        "        loss = loss_fn(pred, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch % 100 == 0:\n",
        "            loss, current = loss.item(), batch * len(img)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
        "\n",
        "\n",
        "def teachear_test_loop(dataloader, model, loss_fn):\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    valid_loss, correct = 0, 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for img, y in dataloader:\n",
        "            img=img.to(device)\n",
        "            y=y.to(device)\n",
        "            pred = model(img)\n",
        "            pred= softmax_temperature(pred, Temp=2)\n",
        "            valid_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
        "\n",
        "    valid_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {valid_loss:>8f} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXTJnAbfXtAi"
      },
      "source": [
        "Teacher Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HKJhuAKBGga2"
      },
      "outputs": [],
      "source": [
        "# Load pre-trained ResNet50 on imageNet\n",
        "teacher_model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).to(device)\n",
        "# Freeze all parameters    \n",
        "for param in teacher_model.parameters():\n",
        "    param.requires_grad = False   \n",
        "# Modified FC layers\n",
        "teacher_model.fc = nn.Sequential(\n",
        "               nn.BatchNorm1d(teacher_model.fc.in_features),\n",
        "               nn.Linear(teacher_model.fc.in_features, 10)).to(device)\n",
        "# Set all parameters in FC layer requires grad to update it's weights.\n",
        "for param in teacher_model.fc.parameters():\n",
        "    param.requires_grad = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjGZpSymXvEa"
      },
      "source": [
        "Student Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PX08wrvCJfOz"
      },
      "outputs": [],
      "source": [
        "# Load pre-trained ResNet18 on imageNet\n",
        "student_model = models.resnet18(weights=None).to(device)\n",
        "# Modified FC layers\n",
        "student_model.fc = nn.Sequential(\n",
        "               nn.BatchNorm1d(student_model.fc.in_features),\n",
        "               nn.Linear(student_model.fc.in_features, 10)).to(device)\n",
        "# Set all parameters in resnet18 requires grad to update it's weights.\n",
        "for param in student_model.parameters():\n",
        "    param.requires_grad = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngi-yns1HZ1i",
        "outputId": "02e35583-15bb-421d-956d-dda2d8dc1390"
      },
      "outputs": [],
      "source": [
        "# Defining the model hyper parameters:\n",
        "lr = 1e-4\n",
        "epochs = 5\n",
        "weight_decay = 0.01\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(filter(lambda param: param.requires_grad, teacher_model.parameters()), lr=lr, weight_decay = weight_decay)\n",
        "\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    teacher_train_loop(train_loader, teacher_model, loss_fn, optimizer)\n",
        "    teachear_test_loop(validate_loader, teacher_model, loss_fn)\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i4ygXSnbFqK9"
      },
      "outputs": [],
      "source": [
        "def distiller_training_loop(train_loader, validate_loader, test_loader, teacher_model, student_model, epoch=5, Temp=2, alpha=0.25):\n",
        "    teacher_model.eval()\n",
        "    student_model.train()\n",
        "    size_train = len(train_loader.dataset)\n",
        "    size_valid = len(validate_loader.dataset)\n",
        "    num_batches_valid = len(validate_loader)\n",
        "    size_test = len(test_loader.dataset)\n",
        "    num_batches_test = len(test_loader)\n",
        "\n",
        "    # Set adam optimizer to train student model\n",
        "    optimizer_st = torch.optim.Adam(filter(lambda param: param.requires_grad, student_model.parameters()), lr=1e-4)\n",
        "\n",
        "    # training and validating process \n",
        "    val_acc=[]\n",
        "    val_loss=[]\n",
        "    for Epoch in range(epoch):\n",
        "        print(f\"Epoch {Epoch+1}\\n-------------------------------\")\n",
        "        # compute distiller-loss over epoch and update student model weights\n",
        "        for batch, (X, y) in enumerate(train_loader):\n",
        "            X=X.to(device)\n",
        "            y=y.to(device)\n",
        "            # Compute logits of teacher and student model  \n",
        "            Z_t = teacher_model(X)\n",
        "            Z_s = student_model(X)\n",
        "\n",
        "            loss = distiller_loss(Z_t, Z_s, y_true=y, Temp=Temp, alpha=alpha)\n",
        "\n",
        "            # Backpropagation\n",
        "            optimizer_st.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer_st.step()\n",
        "\n",
        "            if batch % 100 == 0:\n",
        "                loss, current = loss.item(), batch * len(X)\n",
        "                print(f\"loss: {loss:>7f}  [{current:>5d}/{size_train:>5d}]\")\n",
        "\n",
        "        # validation:\n",
        "        loss_fn = nn.CrossEntropyLoss()\n",
        "        valid_loss, correct = 0, 0\n",
        "        with torch.no_grad():\n",
        "            for X, y in validate_loader:\n",
        "                X=X.to(device)\n",
        "                y=y.to(device)\n",
        "                pred_st = student_model(X)\n",
        "                pred_st =  softmax_temperature(pred_st, Temp=Temp)\n",
        "                valid_loss += loss_fn(pred_st, y).item()\n",
        "                correct += (pred_st.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
        "\n",
        "        valid_loss /= num_batches_valid\n",
        "        correct /= size_valid\n",
        "        print(f\"Validation Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {valid_loss:>8f} \\n\")\n",
        "        val_acc.append(100*correct)\n",
        "        val_loss.append(valid_loss)\n",
        "\n",
        "\n",
        "    # Testing process:\n",
        "    te_acc=[]\n",
        "    te_loss=[]\n",
        "    # test:\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    test_loss, correct = 0, 0\n",
        "    with torch.no_grad():\n",
        "        for X, y in test_loader:\n",
        "            X=X.to(device)\n",
        "            y=y.to(device)\n",
        "            pred_st = student_model(X)\n",
        "            pred_st =  softmax_temperature(pred_st, Temp=Temp)\n",
        "            test_loss += loss_fn(pred_st, y).item()\n",
        "            correct += (pred_st.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
        "\n",
        "    test_loss /= num_batches_test\n",
        "    correct /= size_test\n",
        "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
        "    te_acc=100*correct\n",
        "    te_loss=test_loss\n",
        "\n",
        "    return val_acc,val_loss,te_acc,te_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9_EX8xHNZ5n",
        "outputId": "482dba4d-9122-4ce8-957f-c2a77af741ae"
      },
      "outputs": [],
      "source": [
        "# trainig of student model according to knowledge distillation from a teacher with best hyperparameters:\n",
        "training = distiller_training_loop(train_loader, validate_loader, test_loader,teacher_model= teacher_model, student_model=student_model, epoch=5, Temp=2, alpha=0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3a2wAdsWvq1",
        "outputId": "152b0f4f-b817-4dea-b734-07590c6c06c3"
      },
      "outputs": [],
      "source": [
        "# compute model accuracy and Ave loss on train, valid and test data.\n",
        "student_model.eval()\n",
        "# Define cross entropy loss function\n",
        "loss_fn_eval = nn.CrossEntropyLoss()\n",
        "# student_model evaluation\n",
        "print(\"Evaluation results for train data:\")\n",
        "teachear_test_loop(train_loader, student_model, loss_fn_eval)\n",
        "print(\"Evaluation results for validate data:\")\n",
        "teachear_test_loop(validate_loader, student_model, loss_fn_eval)\n",
        "print(\"Evaluation results for test data:\")\n",
        "teachear_test_loop(test_loader, student_model, loss_fn_eval)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.18"
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  "nbformat": 4,
  "nbformat_minor": 0
}