Implementation of VGG 19 using Transfer Learning.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNOnMZo+ff7t7sCNLTzmXSM"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"W4E4gWGvGmnj"},"outputs":[],"source":["# Import required modules\n","import torch\n","import torch.optim as optim\n","import torchvision.transforms as transforms\n","from torchvision.datasets import ImageFolder\n","import torchvision.models as models\n","\n","from google.colab import drive\n","\n","import seaborn as sn\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc"]},{"cell_type":"code","source":["# Mount drive and retrieve path\n","drive.mount('/content/drive')\n","\n","train_path = '/content/drive/MyDrive/Parkinsons-DaTscan-master/Implementation/TRAIN_CROP/'\n","test_path = '/content/drive/MyDrive/Parkinsons-DaTscan-master/Implementation/TEST_CROP/'\n","val_path = '/content/drive/MyDrive/Parkinsons-DaTscan-master/Implementation/VAL_CROP/'"],"metadata":{"id":"YK7HxDtIGszo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Define preprocessing transforms\n","transform = transforms.Compose([\n","    transforms.Resize((224, 224)),\n","    transforms.ToTensor(),\n","    transforms.Normalize(mean=[0.5], std=[0.5])\n","])"],"metadata":{"id":"KbbbiP005BaM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Create train and test data loaders\n","batch_size = 30\n","num_epochs = 30\n","\n","train_loader = torch.utils.data.DataLoader(ImageFolder(train_path, transform=transform_train), batch_size=batch_size, shuffle=True)\n","test_loader = torch.utils.data.DataLoader(ImageFolder(test_path, transform=transform_test), batch_size=batch_size, shuffle=True)\n","val_loader = torch.utils.data.DataLoader(ImageFolder(val_path, transform=transform_test), batch_size=batch_size, shuffle=True)"],"metadata":{"id":"TW8Rgv3GG1iJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load pre-trained vgg16 model, Freeze pre-trained layers and Modify last layer\n","vgg19 = models.vgg19(pretrained=True)\n","\n","for param in vgg19.parameters():\n","    param.requires_grad = False\n","\n","num_classes = 2\n","vgg19.classifier[-1] = torch.nn.Linear(4096, num_classes)"],"metadata":{"id":"VhP69eykG3zP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Define loss function and optimizer\n","criterion = torch.nn.CrossEntropyLoss()\n","optimizer = optim.Adam(vgg19.parameters(), lr=0.001)\n","\n","accuracy_list = []\n","loss_list = []\n","\n","# Train the model\n","for epoch in range(num_epochs):\n","    running_loss = 0.0\n","    total_correct = 0\n","    total_samples = 0\n","    for i, (images, labels) in enumerate(train_loader, 0):\n","        # Zero the parameter gradients\n","        optimizer.zero_grad()\n","\n","        # Forward + backward + optimize\n","        outputs = vgg19(images)\n","        _, predicted = torch.max(outputs.data, 1)\n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        optimizer.step()\n","\n","        # Update metrics utilities\n","        total_samples += labels.size(0)\n","        total_correct += (predicted == labels).sum().item()\n","\n","        # Print statistics\n","        running_loss += loss.item()\n","        if i % 10 == 9:\n","            print('[Epoch %d, Batch %d] Loss: %.3f' %(epoch + 1, batch_size, running_loss / 10))\n","            running_loss = 0.0\n","\n","    accuracy = 100 * total_correct / total_samples\n","    accuracy_list.append(accuracy)\n","\n","    epoch_loss = running_loss / len(train_loader)\n","    loss_list.append(epoch_loss)"],"metadata":{"id":"DPqiKejbG-NP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plot the accuracy versus epoch\n","plt.plot(range(1, num_epochs + 1), accuracy_list)\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy (%)')\n","plt.title('Accuracy vs. Epoch')\n","plt.show()"],"metadata":{"id":"-7IDA4355V8R"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plot the loss versus epoch\n","plt.plot(range(1, num_epochs + 1), loss_list)\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.title('Loss vs. Epoch')\n","plt.show()"],"metadata":{"id":"7KwFqVoC5YGY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate on validation set\n","correct = 0\n","total = 0\n","with torch.no_grad():\n","    for data in val_loader:\n","        images, labels = data\n","        outputs = vgg19(images)\n","        _, predicted = torch.max(outputs.data, 1)\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","print('Accuracy on validation set: %d %%' % (100 * correct / total))"],"metadata":{"id":"Oqrup2K9HATP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate on test set\n","correct = 0\n","total = 0\n","actual = []\n","prediction = []\n","\n","with torch.no_grad():\n","    for data in test_loader:\n","        images, labels = data\n","        outputs = vgg19(images)\n","        _, predicted = torch.max(outputs.data, 1)\n","        actual.extend(labels.tolist())\n","        prediction.extend(predicted.tolist())\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","print('Accuracy on test set: %d %%' % (100 * correct / total))"],"metadata":{"id":"GryglLANHCGO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Create confusion matrix on test set\n","confusion_mtx = confusion_matrix(actual, prediction)\n","ax = plt.axes()\n","sn.heatmap(confusion_mtx, annot=True,annot_kws={\"size\": 25}, cmap=\"Blues\", ax = ax)\n","ax.set_title('Test Accuracy', size=14)\n","plt.show()"],"metadata":{"id":"a6NukAXO5hB2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Calculate scores\n","TN, FP, FN, TP = confusion_mtx.ravel()\n","\n","precision = TP / (TP + FP)\n","recall = TP / (TP + FN)\n","specificity = TN / (TN + FP)\n","f1 = 2 * (precision * recall) / (precision + recall)\n","\n","print(\"Precision:\", precision)\n","print(\"Recall (Sensitivity):\", recall)\n","print(\"Specificity:\", specificity)\n","print(\"F1 Score:\", f1)"],"metadata":{"id":"DgKVrhOD5jbS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"Classification Report:\")\n","report = classification_report(actual, prediction, target_names=[\"Yes\", \"No\"])\n","print(report)"],"metadata":{"id":"8c3NSSXLIIc0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Calculate ROC curve and AUC\n","fpr, tpr, _ = roc_curve(actual, prediction)\n","roc_auc = auc(fpr, tpr)\n","\n","# Plot ROC curve\n","plt.figure(figsize=(8, 6))\n","plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\n","plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver Operating Characteristic (ROC)')\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"metadata":{"id":"CjgVwLnvIJ2X"},"execution_count":null,"outputs":[]}]}