ResNet50.ipynb

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    "language_info": {
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  "cells": [
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      "source": [
        "<a href=\"https://colab.research.google.com/github/eshal26/Leukemia-classification/blob/main/ResNet50.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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          "text": [
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            "  inflating: Original/Pro/WBC-Malignant-Pro-804.jpg  \n"
          ]
        }
      ],
      "source": [
        "!unzip Original.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import os\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "fyrMoIi4eVg4"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\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": "fjCO0Sl_fJ91"
      },
      "execution_count": 3,
      "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": "HUPx3QlmfjV9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define paths to your dataset directories\n",
        "train_dir = 'train_dataset'\n",
        "val_dir = 'validation_dataset'\n",
        "test_dir = 'test_dataset'\n",
        "\n",
        "# Define transformations for training, validation, and testing data\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "val_test_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Create datasets\n",
        "train_dataset = torchvision.datasets.ImageFolder(root=train_dir, transform=train_transform)\n",
        "val_dataset = torchvision.datasets.ImageFolder(root=val_dir, transform=val_test_transform)\n",
        "test_dataset = torchvision.datasets.ImageFolder(root=test_dir, transform=val_test_transform)\n",
        "\n",
        "# Create data loaders\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",
        "\n",
        "# Define the pre-trained ResNet-50 model\n",
        "model = torchvision.models.resnet50(pretrained=True)\n",
        "\n",
        "# Freeze parameters of the pre-trained layers\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# Modify the final fully connected layer for binary classification\n",
        "num_features = model.fc.in_features\n",
        "model.fc = torch.nn.Linear(num_features, 2)  # Binary classification with 2 output units\n",
        "\n",
        "# Move model to GPU if available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = model.to(device)\n",
        "\n",
        "# Define loss function and optimizer\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "6GOoXijIgcBX",
        "outputId": "825f7278-66ae-4526-9c5f-d7c540caed58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
            "100%|██████████| 97.8M/97.8M [00:00<00:00, 133MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "# Initialize lists to store metrics\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",
        "num_epochs = 20\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    all_train_labels = []\n",
        "    all_train_preds = []\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() * inputs.size(0)\n",
        "\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        all_train_labels.extend(labels.cpu().numpy())\n",
        "        all_train_preds.extend(predicted.cpu().numpy())\n",
        "\n",
        "    epoch_loss = running_loss / len(train_loader.dataset)\n",
        "    train_accuracy = sum(np.array(all_train_preds) == np.array(all_train_labels)) / len(all_train_labels)\n",
        "    train_precision = precision_score(all_train_labels, all_train_preds, average='weighted')\n",
        "    train_recall = recall_score(all_train_labels, all_train_preds, average='weighted')\n",
        "    train_f1 = f1_score(all_train_labels, all_train_preds, average='weighted')\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: {epoch_loss:.4f}, Accuracy: {train_accuracy:.4f}, Precision: {train_precision:.4f}, Recall: {train_recall:.4f}, F1-score: {train_f1:.4f}\")\n",
        "\n",
        "    # Validation loop\n",
        "    model.eval()\n",
        "    val_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    all_val_labels = []\n",
        "    all_val_preds = []\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",
        "            loss = criterion(outputs, labels)\n",
        "            val_loss += loss.item() * inputs.size(0)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            all_val_labels.extend(labels.cpu().numpy())\n",
        "            all_val_preds.extend(predicted.cpu().numpy())\n",
        "\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    val_accuracy = correct / total\n",
        "    val_precision = precision_score(all_val_labels, all_val_preds, average='weighted')\n",
        "    val_recall = recall_score(all_val_labels, all_val_preds, average='weighted')\n",
        "    val_f1 = f1_score(all_val_labels, all_val_preds, average='weighted')\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 Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.4f}, Precision: {val_precision:.4f}, Recall: {val_recall:.4f}, F1-score: {val_f1:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDGh_OwB4-59",
        "outputId": "90d7d714-d8ce-4d29-e585-8b3c1e89a16d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Training Loss: 0.2777, Accuracy: 0.8917, Precision: 0.8820, Recall: 0.8917, F1-score: 0.8802\n",
            "Epoch 1, Validation Loss: 0.1371, Accuracy: 0.9723, Precision: 0.9742, Recall: 0.9723, F1-score: 0.9728\n",
            "Epoch 2, Training Loss: 0.1077, Accuracy: 0.9700, Precision: 0.9696, Recall: 0.9700, F1-score: 0.9695\n",
            "Epoch 2, Validation Loss: 0.0891, Accuracy: 0.9692, Precision: 0.9689, Recall: 0.9692, F1-score: 0.9690\n",
            "Epoch 3, Training Loss: 0.0721, Accuracy: 0.9816, Precision: 0.9814, Recall: 0.9816, F1-score: 0.9813\n",
            "Epoch 3, Validation Loss: 0.0761, Accuracy: 0.9723, Precision: 0.9733, Recall: 0.9723, F1-score: 0.9726\n",
            "Epoch 4, Training Loss: 0.0655, Accuracy: 0.9789, Precision: 0.9787, Recall: 0.9789, F1-score: 0.9787\n",
            "Epoch 4, Validation Loss: 0.0665, Accuracy: 0.9785, Precision: 0.9793, Recall: 0.9785, F1-score: 0.9787\n",
            "Epoch 5, Training Loss: 0.0529, Accuracy: 0.9846, Precision: 0.9845, Recall: 0.9846, F1-score: 0.9845\n",
            "Epoch 5, Validation Loss: 0.0703, Accuracy: 0.9785, Precision: 0.9801, Recall: 0.9785, F1-score: 0.9789\n",
            "Epoch 6, Training Loss: 0.0522, Accuracy: 0.9831, Precision: 0.9830, Recall: 0.9831, F1-score: 0.9829\n",
            "Epoch 6, Validation Loss: 0.0527, Accuracy: 0.9754, Precision: 0.9754, Recall: 0.9754, F1-score: 0.9754\n",
            "Epoch 7, Training Loss: 0.0453, Accuracy: 0.9839, Precision: 0.9838, Recall: 0.9839, F1-score: 0.9838\n",
            "Epoch 7, Validation Loss: 0.0531, Accuracy: 0.9815, Precision: 0.9815, Recall: 0.9815, F1-score: 0.9812\n",
            "Epoch 8, Training Loss: 0.0390, Accuracy: 0.9889, Precision: 0.9888, Recall: 0.9889, F1-score: 0.9888\n",
            "Epoch 8, Validation Loss: 0.0486, Accuracy: 0.9785, Precision: 0.9787, Recall: 0.9785, F1-score: 0.9785\n",
            "Epoch 9, Training Loss: 0.0322, Accuracy: 0.9923, Precision: 0.9923, Recall: 0.9923, F1-score: 0.9923\n",
            "Epoch 9, Validation Loss: 0.0509, Accuracy: 0.9815, Precision: 0.9826, Recall: 0.9815, F1-score: 0.9818\n",
            "Epoch 10, Training Loss: 0.0363, Accuracy: 0.9885, Precision: 0.9885, Recall: 0.9885, F1-score: 0.9885\n",
            "Epoch 10, Validation Loss: 0.0555, Accuracy: 0.9815, Precision: 0.9819, Recall: 0.9815, F1-score: 0.9810\n",
            "Epoch 11, Training Loss: 0.0455, Accuracy: 0.9827, Precision: 0.9826, Recall: 0.9827, F1-score: 0.9826\n",
            "Epoch 11, Validation Loss: 0.0656, Accuracy: 0.9754, Precision: 0.9776, Recall: 0.9754, F1-score: 0.9759\n",
            "Epoch 12, Training Loss: 0.0317, Accuracy: 0.9900, Precision: 0.9900, Recall: 0.9900, F1-score: 0.9900\n",
            "Epoch 12, Validation Loss: 0.0456, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n",
            "Epoch 13, Training Loss: 0.0286, Accuracy: 0.9931, Precision: 0.9931, Recall: 0.9931, F1-score: 0.9931\n",
            "Epoch 13, Validation Loss: 0.0442, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n",
            "Epoch 14, Training Loss: 0.0234, Accuracy: 0.9935, Precision: 0.9935, Recall: 0.9935, F1-score: 0.9935\n",
            "Epoch 14, Validation Loss: 0.0418, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n",
            "Epoch 15, Training Loss: 0.0242, Accuracy: 0.9919, Precision: 0.9919, Recall: 0.9919, F1-score: 0.9919\n",
            "Epoch 15, Validation Loss: 0.0442, Accuracy: 0.9785, Precision: 0.9793, Recall: 0.9785, F1-score: 0.9787\n",
            "Epoch 16, Training Loss: 0.0259, Accuracy: 0.9912, Precision: 0.9911, Recall: 0.9912, F1-score: 0.9912\n",
            "Epoch 16, Validation Loss: 0.0458, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n",
            "Epoch 17, Training Loss: 0.0225, Accuracy: 0.9939, Precision: 0.9938, Recall: 0.9939, F1-score: 0.9938\n",
            "Epoch 17, Validation Loss: 0.0463, Accuracy: 0.9754, Precision: 0.9759, Recall: 0.9754, F1-score: 0.9756\n",
            "Epoch 18, Training Loss: 0.0311, Accuracy: 0.9885, Precision: 0.9885, Recall: 0.9885, F1-score: 0.9885\n",
            "Epoch 18, Validation Loss: 0.0443, Accuracy: 0.9815, Precision: 0.9820, Recall: 0.9815, F1-score: 0.9817\n",
            "Epoch 19, Training Loss: 0.0216, Accuracy: 0.9946, Precision: 0.9946, Recall: 0.9946, F1-score: 0.9946\n",
            "Epoch 19, Validation Loss: 0.0422, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n",
            "Epoch 20, Training Loss: 0.0189, Accuracy: 0.9923, Precision: 0.9923, Recall: 0.9923, F1-score: 0.9923\n",
            "Epoch 20, Validation Loss: 0.0468, Accuracy: 0.9877, Precision: 0.9876, Recall: 0.9877, F1-score: 0.9876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(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 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",
        "    return test_loss, accuracy, precision, recall, f1\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "test_loss, test_accuracy, test_precision, test_recall, test_f1 = evaluate_model(model, test_loader, criterion, device)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLHg-ZZxq5ii",
        "outputId": "7e0d3df2-85c8-43e0-9009-e3dea979076e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.9908\n",
            "Test Precision: 0.9909\n",
            "Test Recall: 0.9908\n",
            "Test F1-score: 0.9907\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_metrics(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=(12, 10))\n",
        "\n",
        "    # Plot accuracy\n",
        "    plt.subplot(2, 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()\n",
        "\n",
        "    # Plot precision\n",
        "    plt.subplot(2, 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()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Plot the metrics\n",
        "plot_metrics(train_acc_history, val_acc_history, train_precision_history, val_precision_history)\n"
      ],
      "metadata": {
        "id": "6sX8TsOu_kW9",
        "outputId": "441fd17a-90ed-4c70-cd23-c965795aa1f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\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, test_loader, criterion, device)\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "KzpP2JXN1GtE",
        "outputId": "79d826f3-e6aa-4edc-a8cb-3fdee33302d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0234\n",
            "Test Accuracy: 0.9908\n",
            "Test Precision: 0.9909\n",
            "Test Recall: 0.9908\n",
            "Test F1-score: 0.9907\n",
            "Confusion Matrix:\n",
            " [[ 48   3]\n",
            " [  0 276]]\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       1.00      0.94      0.97        51\n",
            "           1       0.99      1.00      0.99       276\n",
            "\n",
            "    accuracy                           0.99       327\n",
            "   macro avg       0.99      0.97      0.98       327\n",
            "weighted avg       0.99      0.99      0.99       327\n",
            "\n"
          ]
        }
      ]
    }
  ]
}