AlexNet.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eshal26/Leukemia-classification/blob/main/AlexNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EfiOEuNUcr9",
        "outputId": "968ae8e8-799a-49ad-8367-1d0112cafe39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  Original.zip\n",
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          ]
        }
      ],
      "source": [
        "!unzip Original.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "# Define paths to your dataset directories\n",
        "dataset_dir = 'Original'\n",
        "benign_dir = os.path.join(dataset_dir, 'Benign')\n",
        "malignant_dir = os.path.join(dataset_dir, 'Malignant')  # Create this directory if it doesn't exist\n",
        "\n",
        "# Create the malignant directory if it doesn't exist\n",
        "if not os.path.exists(malignant_dir):\n",
        "    os.makedirs(malignant_dir)\n",
        "\n",
        "# Move images from early, pre, and pro directories to malignant directory\n",
        "for stage in ['Early', 'Pre', 'Pro']:\n",
        "    stage_dir = os.path.join(dataset_dir, stage)\n",
        "    for img_file in os.listdir(stage_dir):\n",
        "        img_path = os.path.join(stage_dir, img_file)\n",
        "        shutil.move(img_path, malignant_dir)\n",
        "\n",
        "# Update dataset directory structure and labels\n",
        "os.rmdir(os.path.join(dataset_dir, 'Early'))\n",
        "os.rmdir(os.path.join(dataset_dir, 'Pre'))\n",
        "os.rmdir(os.path.join(dataset_dir, 'Pro'))\n"
      ],
      "metadata": {
        "id": "radjOgRdpA7L"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Define paths to your original dataset directory\n",
        "original_data_dir = 'Original'\n",
        "benign_dir = os.path.join(original_data_dir, 'Benign')\n",
        "malignant_dir = os.path.join(original_data_dir, 'Malignant')\n",
        "\n",
        "# Define paths to create directories for the new dataset splits\n",
        "train_dir = 'train_dataset'\n",
        "val_dir = 'validation_dataset'\n",
        "test_dir = 'test_dataset'\n",
        "\n",
        "# Create directories for the new dataset splits if they don't exist\n",
        "for directory in [train_dir, val_dir, test_dir]:\n",
        "    os.makedirs(directory, exist_ok=True)\n",
        "    for category in ['Benign', 'Malignant']:\n",
        "        os.makedirs(os.path.join(directory, category), exist_ok=True)\n",
        "\n",
        "# Split dataset and copy images to appropriate directories\n",
        "for category_dir, category in [(benign_dir, 'Benign'), (malignant_dir, 'Malignant')]:\n",
        "    image_files = [os.path.join(category_dir, img) for img in os.listdir(category_dir)]\n",
        "    train_files, test_val_files = train_test_split(image_files, test_size=0.2, random_state=42)\n",
        "    val_files, test_files = train_test_split(test_val_files, test_size=0.5, random_state=42)\n",
        "\n",
        "    for file in train_files:\n",
        "        shutil.copy(file, os.path.join(train_dir, category))\n",
        "    for file in val_files:\n",
        "        shutil.copy(file, os.path.join(val_dir, category))\n",
        "    for file in test_files:\n",
        "        shutil.copy(file, os.path.join(test_dir, category))\n"
      ],
      "metadata": {
        "id": "XhptBTbepMjQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader"
      ],
      "metadata": {
        "id": "mxwaVHlwsNyJ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define transformations to apply to each image\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),  # Resize image to 224x224\n",
        "    transforms.ToTensor(),           # Convert image to tensor\n",
        "    transforms.Normalize(mean=[0.6490, 0.7092, 0.8777], std=[0.0151, 0.0215, 0.0138])  # Normalize pixel values\n",
        "])\n",
        "\n",
        "# Load the dataset using ImageFolder\n",
        "train_dataset = ImageFolder('train_dataset', transform=transform)\n",
        "val_dataset = ImageFolder('validation_dataset', transform=transform)\n",
        "test_dataset = ImageFolder('test_dataset', transform=transform)\n",
        "\n",
        "# Create DataLoader objects for batching and shuffling\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n"
      ],
      "metadata": {
        "id": "72yMsNgipxmk"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms, models\n",
        "import torchvision.models as models\n",
        "\n",
        "# Load pre-trained AlexNet\n",
        "alexnet = models.alexnet(pretrained=True)\n",
        "\n",
        "# Freeze early layers\n",
        "for param in alexnet.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# Modify classifier for binary classification\n",
        "num_classes = 2\n",
        "alexnet.classifier[6] = nn.Linear(4096, num_classes)\n",
        "\n",
        "# Move model to GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "alexnet = alexnet.to(device)\n",
        "\n",
        "# Define loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(alexnet.classifier.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "wEqtUbkMzX5W",
        "outputId": "4b31e00d-66cf-456f-f97f-972bbb193c0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n",
            "100%|██████████| 233M/233M [00:01<00:00, 168MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate mean and standard deviation of the dataset\n",
        "dataset_mean = torch.stack([img.mean(1).mean(1) for img, _ in train_dataset]).mean(0)\n",
        "dataset_std = torch.stack([img.std(1).std(1) for img, _ in train_dataset]).std(0)\n",
        "\n",
        "print(\"Mean:\", dataset_mean)\n",
        "print(\"Std Deviation:\", dataset_std)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqkN_-uWsoIK",
        "outputId": "91efbc9a-02ef-40da-aa43-e559a21632dc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: tensor([11.6762,  6.7745,  4.4288])\n",
            "Std Deviation: tensor([0.5128, 0.5111, 0.4982])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Training function\n",
        "def train_model(model, criterion, optimizer, train_loader, val_loader, num_epochs=30):\n",
        "    train_acc_history = []\n",
        "    val_acc_history = []\n",
        "    train_precision_history = []\n",
        "    val_precision_history = []\n",
        "    train_recall_history = []\n",
        "    val_recall_history = []\n",
        "    train_f1_history = []\n",
        "    val_f1_history = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch + 1}/{num_epochs}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Training phase\n",
        "        model.train()\n",
        "        running_loss = 0.0\n",
        "        all_labels = []\n",
        "        all_preds = []\n",
        "\n",
        "        for inputs, labels in train_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            running_loss += loss.item()\n",
        "\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "        train_accuracy = np.mean(np.array(all_preds) == np.array(all_labels))\n",
        "        train_precision = precision_score(all_labels, all_preds, average='binary')\n",
        "        train_recall = recall_score(all_labels, all_preds, average='binary')\n",
        "        train_f1 = f1_score(all_labels, all_preds, average='binary')\n",
        "\n",
        "        train_acc_history.append(train_accuracy)\n",
        "        train_precision_history.append(train_precision)\n",
        "        train_recall_history.append(train_recall)\n",
        "        train_f1_history.append(train_f1)\n",
        "\n",
        "        print(f\"Epoch {epoch + 1}, Training Loss: {running_loss / len(train_loader):.4f}\")\n",
        "        print(f\"Epoch {epoch + 1}, Training Accuracy: {train_accuracy:.4f}, Precision: {train_precision:.4f}, Recall: {train_recall:.4f}, F1-score: {train_f1:.4f}\")\n",
        "\n",
        "        # Validation phase\n",
        "        model.eval()\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        all_labels = []\n",
        "        all_preds = []\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for inputs, labels in val_loader:\n",
        "                inputs, labels = inputs.to(device), labels.to(device)\n",
        "                outputs = model(inputs)\n",
        "                _, preds = torch.max(outputs, 1)\n",
        "                total += labels.size(0)\n",
        "                correct += (preds == labels).sum().item()\n",
        "                all_labels.extend(labels.cpu().numpy())\n",
        "                all_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "        val_accuracy = correct / total\n",
        "        val_precision = precision_score(all_labels, all_preds, average='binary')\n",
        "        val_recall = recall_score(all_labels, all_preds, average='binary')\n",
        "        val_f1 = f1_score(all_labels, all_preds, average='binary')\n",
        "\n",
        "        val_acc_history.append(val_accuracy)\n",
        "        val_precision_history.append(val_precision)\n",
        "        val_recall_history.append(val_recall)\n",
        "        val_f1_history.append(val_f1)\n",
        "\n",
        "        print(f\"Epoch {epoch + 1}, Validation Accuracy: {val_accuracy:.4f}, Precision: {val_precision:.4f}, Recall: {val_recall:.4f}, F1-score: {val_f1:.4f}\")\n",
        "\n",
        "    return model, train_acc_history, val_acc_history, train_precision_history, val_precision_history, train_recall_history, val_recall_history, train_f1_history, val_f1_history\n",
        "\n",
        "\n",
        "\n",
        "# Train the model\n",
        "model_ft, train_acc_history, val_acc_history, train_precision_history, val_precision_history, train_recall_history, val_recall_history, train_f1_history, val_f1_history = train_model(alexnet, criterion, optimizer, train_loader, val_loader, num_epochs=20)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LraQJras6GB",
        "outputId": "44cfa866-4bf2-4d41-f7ca-b51ff1646be7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "----------\n",
            "Epoch 1, Training Loss: 0.3720\n",
            "Epoch 1, Training Accuracy: 0.9194, Precision: 0.9458, Recall: 0.9596, F1-score: 0.9526\n",
            "Epoch 1, Validation Accuracy: 0.9569, Precision: 0.9547, Recall: 0.9964, F1-score: 0.9751\n",
            "Epoch 2/20\n",
            "----------\n",
            "Epoch 2, Training Loss: 0.1691\n",
            "Epoch 2, Training Accuracy: 0.9485, Precision: 0.9649, Recall: 0.9746, F1-score: 0.9697\n",
            "Epoch 2, Validation Accuracy: 0.9662, Precision: 0.9648, Recall: 0.9964, F1-score: 0.9803\n",
            "Epoch 3/20\n",
            "----------\n",
            "Epoch 3, Training Loss: 0.1645\n",
            "Epoch 3, Training Accuracy: 0.9512, Precision: 0.9688, Recall: 0.9736, F1-score: 0.9712\n",
            "Epoch 3, Validation Accuracy: 0.9538, Precision: 0.9577, Recall: 0.9891, F1-score: 0.9732\n",
            "Epoch 4/20\n",
            "----------\n",
            "Epoch 4, Training Loss: 0.1277\n",
            "Epoch 4, Training Accuracy: 0.9604, Precision: 0.9742, Recall: 0.9791, F1-score: 0.9767\n",
            "Epoch 4, Validation Accuracy: 0.9569, Precision: 0.9516, Recall: 1.0000, F1-score: 0.9752\n",
            "Epoch 5/20\n",
            "----------\n",
            "Epoch 5, Training Loss: 0.1192\n",
            "Epoch 5, Training Accuracy: 0.9635, Precision: 0.9760, Recall: 0.9809, F1-score: 0.9785\n",
            "Epoch 5, Validation Accuracy: 0.9538, Precision: 0.9514, Recall: 0.9964, F1-score: 0.9734\n",
            "Epoch 6/20\n",
            "----------\n",
            "Epoch 6, Training Loss: 0.1834\n",
            "Epoch 6, Training Accuracy: 0.9528, Precision: 0.9727, Recall: 0.9714, F1-score: 0.9720\n",
            "Epoch 6, Validation Accuracy: 0.9508, Precision: 0.9609, Recall: 0.9818, F1-score: 0.9712\n",
            "Epoch 7/20\n",
            "----------\n",
            "Epoch 7, Training Loss: 0.1583\n",
            "Epoch 7, Training Accuracy: 0.9593, Precision: 0.9733, Recall: 0.9786, F1-score: 0.9760\n",
            "Epoch 7, Validation Accuracy: 0.9477, Precision: 0.9708, Recall: 0.9673, F1-score: 0.9690\n",
            "Epoch 8/20\n",
            "----------\n",
            "Epoch 8, Training Loss: 0.1594\n",
            "Epoch 8, Training Accuracy: 0.9601, Precision: 0.9746, Recall: 0.9782, F1-score: 0.9764\n",
            "Epoch 8, Validation Accuracy: 0.9446, Precision: 0.9416, Recall: 0.9964, F1-score: 0.9682\n",
            "Epoch 9/20\n",
            "----------\n",
            "Epoch 9, Training Loss: 0.1580\n",
            "Epoch 9, Training Accuracy: 0.9658, Precision: 0.9770, Recall: 0.9827, F1-score: 0.9798\n",
            "Epoch 9, Validation Accuracy: 0.9631, Precision: 0.9614, Recall: 0.9964, F1-score: 0.9786\n",
            "Epoch 10/20\n",
            "----------\n",
            "Epoch 10, Training Loss: 0.1975\n",
            "Epoch 10, Training Accuracy: 0.9597, Precision: 0.9759, Recall: 0.9764, F1-score: 0.9762\n",
            "Epoch 10, Validation Accuracy: 0.9631, Precision: 0.9582, Recall: 1.0000, F1-score: 0.9786\n",
            "Epoch 11/20\n",
            "----------\n",
            "Epoch 11, Training Loss: 0.1772\n",
            "Epoch 11, Training Accuracy: 0.9601, Precision: 0.9742, Recall: 0.9786, F1-score: 0.9764\n",
            "Epoch 11, Validation Accuracy: 0.9569, Precision: 0.9745, Recall: 0.9745, F1-score: 0.9745\n",
            "Epoch 12/20\n",
            "----------\n",
            "Epoch 12, Training Loss: 0.1725\n",
            "Epoch 12, Training Accuracy: 0.9597, Precision: 0.9755, Recall: 0.9768, F1-score: 0.9762\n",
            "Epoch 12, Validation Accuracy: 0.9631, Precision: 0.9647, Recall: 0.9927, F1-score: 0.9785\n",
            "Epoch 13/20\n",
            "----------\n",
            "Epoch 13, Training Loss: 0.1785\n",
            "Epoch 13, Training Accuracy: 0.9620, Precision: 0.9760, Recall: 0.9791, F1-score: 0.9775\n",
            "Epoch 13, Validation Accuracy: 0.9538, Precision: 0.9514, Recall: 0.9964, F1-score: 0.9734\n",
            "Epoch 14/20\n",
            "----------\n",
            "Epoch 14, Training Loss: 0.1458\n",
            "Epoch 14, Training Accuracy: 0.9662, Precision: 0.9800, Recall: 0.9800, F1-score: 0.9800\n",
            "Epoch 14, Validation Accuracy: 0.9600, Precision: 0.9580, Recall: 0.9964, F1-score: 0.9768\n",
            "Epoch 15/20\n",
            "----------\n",
            "Epoch 15, Training Loss: 0.1560\n",
            "Epoch 15, Training Accuracy: 0.9635, Precision: 0.9786, Recall: 0.9782, F1-score: 0.9784\n",
            "Epoch 15, Validation Accuracy: 0.9600, Precision: 0.9679, Recall: 0.9855, F1-score: 0.9766\n",
            "Epoch 16/20\n",
            "----------\n",
            "Epoch 16, Training Loss: 0.1556\n",
            "Epoch 16, Training Accuracy: 0.9639, Precision: 0.9778, Recall: 0.9796, F1-score: 0.9787\n",
            "Epoch 16, Validation Accuracy: 0.9292, Precision: 0.9228, Recall: 1.0000, F1-score: 0.9599\n",
            "Epoch 17/20\n",
            "----------\n",
            "Epoch 17, Training Loss: 0.1257\n",
            "Epoch 17, Training Accuracy: 0.9658, Precision: 0.9783, Recall: 0.9814, F1-score: 0.9798\n",
            "Epoch 17, Validation Accuracy: 0.9538, Precision: 0.9514, Recall: 0.9964, F1-score: 0.9734\n",
            "Epoch 18/20\n",
            "----------\n",
            "Epoch 18, Training Loss: 0.1976\n",
            "Epoch 18, Training Accuracy: 0.9616, Precision: 0.9760, Recall: 0.9786, F1-score: 0.9773\n",
            "Epoch 18, Validation Accuracy: 0.9723, Precision: 0.9819, Recall: 0.9855, F1-score: 0.9837\n",
            "Epoch 19/20\n",
            "----------\n",
            "Epoch 19, Training Loss: 0.1650\n",
            "Epoch 19, Training Accuracy: 0.9639, Precision: 0.9791, Recall: 0.9782, F1-score: 0.9786\n",
            "Epoch 19, Validation Accuracy: 0.9538, Precision: 0.9577, Recall: 0.9891, F1-score: 0.9732\n",
            "Epoch 20/20\n",
            "----------\n",
            "Epoch 20, Training Loss: 0.1734\n",
            "Epoch 20, Training Accuracy: 0.9681, Precision: 0.9801, Recall: 0.9823, F1-score: 0.9812\n",
            "Epoch 20, Validation Accuracy: 0.9538, Precision: 0.9815, Recall: 0.9636, F1-score: 0.9725\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_accuracy_precision(train_acc_history, val_acc_history, train_precision_history, val_precision_history):\n",
        "    epochs = range(1, len(train_acc_history) + 1)\n",
        "\n",
        "    plt.figure(figsize=(14, 6))\n",
        "\n",
        "    # Plot training and validation accuracy\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, train_acc_history, 'bo-', label='Training Accuracy')\n",
        "    plt.plot(epochs, val_acc_history, 'ro-', label='Validation Accuracy')\n",
        "    plt.title('Training and Validation Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    # Plot training and validation precision\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, train_precision_history, 'bo-', label='Training Precision')\n",
        "    plt.plot(epochs, val_precision_history, 'ro-', label='Validation Precision')\n",
        "    plt.title('Training and Validation Precision')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Precision')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "plot_accuracy_precision(train_acc_history, val_acc_history, train_precision_history, val_precision_history)\n"
      ],
      "metadata": {
        "id": "aYnKMvFl1Ikb",
        "outputId": "e6c3b8f1-e1bb-4d1b-e54c-25a96bc98dd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "def evaluate_model_with_detailed_metrics(model, dataloader, criterion, device):\n",
        "    model.eval()\n",
        "    test_loss = 0.0\n",
        "    all_labels = []\n",
        "    all_preds = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            test_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_preds.extend(predicted.cpu().numpy())\n",
        "\n",
        "    test_loss /= len(dataloader.dataset)\n",
        "    accuracy = accuracy_score(all_labels, all_preds)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "\n",
        "    print(f'Test Loss: {test_loss:.4f}')\n",
        "    print(f'Test Accuracy: {accuracy:.4f}')\n",
        "    print(f'Test Precision: {precision:.4f}')\n",
        "    print(f'Test Recall: {recall:.4f}')\n",
        "    print(f'Test F1-score: {f1:.4f}')\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(all_labels, all_preds)\n",
        "    print(\"Confusion Matrix:\\n\", cm)\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "    disp.plot(cmap=plt.cm.Blues)\n",
        "    plt.show()\n",
        "\n",
        "    # Print classification report\n",
        "    class_report = classification_report(all_labels, all_preds)\n",
        "    print(\"Classification Report:\\n\", class_report)\n",
        "\n",
        "    return test_loss, accuracy, precision, recall, f1, cm, class_report\n",
        "\n",
        "test_loss, test_accuracy, test_precision, test_recall, test_f1, cm, class_report = evaluate_model_with_detailed_metrics(model_ft, test_loader, criterion, device)\n"
      ],
      "metadata": {
        "id": "3Rh40pOwwJB5",
        "outputId": "01a0479e-b26b-40d3-8a25-b1d60d52e132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0888\n",
            "Test Accuracy: 0.9786\n",
            "Test Precision: 0.9794\n",
            "Test Recall: 0.9786\n",
            "Test F1-score: 0.9788\n",
            "Confusion Matrix:\n",
            " [[ 49   2]\n",
            " [  5 271]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.96      0.93        51\n",
            "           1       0.99      0.98      0.99       276\n",
            "\n",
            "    accuracy                           0.98       327\n",
            "   macro avg       0.95      0.97      0.96       327\n",
            "weighted avg       0.98      0.98      0.98       327\n",
            "\n"
          ]
        }
      ]
    }
  ]
}