resnet101.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyN7PzsOvXTxSXHLWYXylr+k",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/eshal26/Leukemia-classification/blob/main/resnet101.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": {
        "id": "ZpigzU7Y6CHG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "482cd60b-f5eb-4976-f44b-4fb2aafd122c"
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Archive:  train_dataset.zip\n",
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            "   creating: test_dataset/Malignant/\n",
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          ]
        }
      ],
      "source": [
        "!unzip train_dataset.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": "7kfta4m8vN-R"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix,  ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "47-uNVXEw4ZU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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)"
      ],
      "metadata": {
        "id": "RX-sZN4UwcW7"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the pre-trained ResNet101 model\n",
        "model = torchvision.models.resnet101(pretrained=True)\n",
        "\n",
        "# Freezing parameters of the pre-trained layers\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# Modifying 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",
        "# Moving model to GPU if available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = model.to(device)\n",
        "\n",
        "# Defining loss function and optimizer\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.fc.parameters(), lr=0.001)  # Only train the final layer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Md6HXOwwjp0",
        "outputId": "7dc9667c-cf86-4c7a-887b-780876c0ddc8"
      },
      "execution_count": 9,
      "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=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import matplotlib.pyplot as plt\n",
        "# Function to plot training and validation accuracy\n",
        "def plot_training_results(train_acc_history, val_acc_history):\n",
        "    epochs = range(1, len(train_acc_history) + 1)\n",
        "    plt.figure(figsize=(10, 5))\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",
        "    plt.show()\n",
        "\n",
        "# Training loop\n",
        "num_epochs = 15\n",
        "train_acc_history = []\n",
        "val_acc_history = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    correct_train = 0\n",
        "    total_train = 0\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",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        total_train += labels.size(0)\n",
        "        correct_train += (predicted == labels).sum().item()\n",
        "\n",
        "    epoch_loss = running_loss / len(train_loader.dataset)\n",
        "    train_accuracy = correct_train / total_train\n",
        "    train_acc_history.append(train_accuracy)\n",
        "\n",
        "    # Validation\n",
        "    model.eval()\n",
        "    val_loss = 0.0\n",
        "    correct_val = 0\n",
        "    total_val = 0\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",
        "            loss = criterion(outputs, labels)\n",
        "            val_loss += loss.item() * inputs.size(0)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            total_val += labels.size(0)\n",
        "            correct_val += (predicted == labels).sum().item()\n",
        "\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    val_accuracy = correct_val / total_val\n",
        "    val_acc_history.append(val_accuracy)\n",
        "\n",
        "    print(f\"Epoch {epoch + 1}/{num_epochs}, Training Loss: {epoch_loss:.4f}, Training Accuracy: {train_accuracy:.4f}, Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_accuracy:.4f}\")\n",
        "\n",
        "# Ploting training results\n",
        "plot_training_results(train_acc_history, val_acc_history)\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "fgsNJEubxQjj",
        "outputId": "2c86cc56-45bc-4ff7-ccad-d0fc595cc62b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15, Training Loss: 0.2067, Training Accuracy: 0.9205, Validation Loss: 0.0957, Validation Accuracy: 0.9796\n",
            "Epoch 2/15, Training Loss: 0.0870, Training Accuracy: 0.9750, Validation Loss: 0.0614, Validation Accuracy: 0.9877\n",
            "Epoch 3/15, Training Loss: 0.0753, Training Accuracy: 0.9759, Validation Loss: 0.0514, Validation Accuracy: 0.9918\n",
            "Epoch 4/15, Training Loss: 0.0605, Training Accuracy: 0.9794, Validation Loss: 0.0526, Validation Accuracy: 0.9898\n",
            "Epoch 5/15, Training Loss: 0.0426, Training Accuracy: 0.9890, Validation Loss: 0.0468, Validation Accuracy: 0.9857\n",
            "Epoch 6/15, Training Loss: 0.0434, Training Accuracy: 0.9877, Validation Loss: 0.0364, Validation Accuracy: 0.9877\n",
            "Epoch 7/15, Training Loss: 0.0423, Training Accuracy: 0.9873, Validation Loss: 0.0478, Validation Accuracy: 0.9857\n",
            "Epoch 8/15, Training Loss: 0.0344, Training Accuracy: 0.9877, Validation Loss: 0.0432, Validation Accuracy: 0.9898\n",
            "Epoch 9/15, Training Loss: 0.0370, Training Accuracy: 0.9903, Validation Loss: 0.0534, Validation Accuracy: 0.9816\n",
            "Epoch 10/15, Training Loss: 0.0290, Training Accuracy: 0.9903, Validation Loss: 0.0357, Validation Accuracy: 0.9877\n",
            "Epoch 11/15, Training Loss: 0.0270, Training Accuracy: 0.9921, Validation Loss: 0.0327, Validation Accuracy: 0.9857\n",
            "Epoch 12/15, Training Loss: 0.0323, Training Accuracy: 0.9895, Validation Loss: 0.0381, Validation Accuracy: 0.9898\n",
            "Epoch 13/15, Training Loss: 0.0223, Training Accuracy: 0.9960, Validation Loss: 0.0340, Validation Accuracy: 0.9898\n",
            "Epoch 14/15, Training Loss: 0.0262, Training Accuracy: 0.9912, Validation Loss: 0.0274, Validation Accuracy: 0.9898\n",
            "Epoch 15/15, Training Loss: 0.0219, Training Accuracy: 0.9934, Validation Loss: 0.0299, Validation Accuracy: 0.9918\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, dataloader, device):\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 dataloader:\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            total += labels.size(0)\n",
        "            correct += (preds == labels).sum().item()\n",
        "\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "    accuracy = correct / total\n",
        "    precision = precision_score(all_labels, all_preds, average='macro')\n",
        "    recall = recall_score(all_labels, all_preds, average='macro')\n",
        "    f1 = f1_score(all_labels, all_preds, average='macro')\n",
        "\n",
        "    print(f'Accuracy: {accuracy:.4f}')\n",
        "    print(f'Precision: {precision:.4f}')\n",
        "    print(f'Recall: {recall:.4f}')\n",
        "    print(f'F1-score: {f1:.4f}')\n",
        "\n",
        "# Ensuring to call the function with the test loader\n",
        "evaluate_model(model, test_loader, device)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaU6o_UHxb1V",
        "outputId": "ec9a4dac-f227-465d-dd64-9bfe1eb0c996"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9836\n",
            "Precision: 0.9789\n",
            "Recall: 0.9581\n",
            "F1-score: 0.9682\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EvKAEtjBxmyW",
        "outputId": "5db453a8-9562-426e-97be-a65471a6a17c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_path = \"/content/drive/MyDrive/resnet101.pth\"\n",
        "torch.save(model, model_path)"
      ],
      "metadata": {
        "id": "8xGgRAodxrbR"
      },
      "execution_count": 13,
      "outputs": []
    }
  ]
}