Delete vggnet (1).ipynb

vggnet (1).ipynb

@@ -1,465 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6cb82750-e21c-4895-81fb-6b03a44595d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: torch in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (2.5.1)\n",
-      "Requirement already satisfied: torchvision in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (0.20.1)\n",
-      "Requirement already satisfied: datasets in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (4.1.1)\n",
-      "Requirement already satisfied: tqdm in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (4.67.1)\n",
-      "Requirement already satisfied: matplotlib in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (3.9.4)\n",
-      "Requirement already satisfied: filelock in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (from torch) (3.19.1)\n",
-      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (from torch) (4.14.1)\n",
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-      "Requirement already satisfied: numpy in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (from torchvision) (2.0.2)\n",
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-      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (from requests>=2.32.2->datasets) (2025.8.3)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\adity\\miniconda3\\envs\\dl_cpu\\lib\\site-packages (from jinja2->torch) (3.0.2)\n",
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-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install torch torchvision datasets tqdm matplotlib\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "eaba3244-e2d3-4d34-b44c-e94287ac08af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cpu\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torchvision import models, transforms\n",
-    "from torch.utils.data import DataLoader\n",
-    "from datasets import load_dataset\n",
-    "from tqdm import tqdm\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import clear_output\n",
-    "\n",
-    "# Set device\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(\"Using device:\", device)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ded6afcd-a0f0-437d-8ad1-f816650146de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision import transforms\n",
-    "\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],\n",
-    "                         std=[0.229, 0.224, 0.225])\n",
-    "])\n",
-    "\n",
-    "val_transform = transforms.Compose([\n",
-    "    transforms.Resize((224, 224)),\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
-    "                         std=[0.229, 0.224, 0.225])\n",
-    "])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b8cc0c13-a42a-405c-a003-af754e657de4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision.datasets import ImageFolder\n",
-    "\n",
-    "train_dataset = ImageFolder(root=\"sickle-cell-classification-dataset/train\", transform=train_transform)\n",
-    "val_dataset   = ImageFolder(root=\"sickle-cell-classification-dataset/val\", transform=val_transform)\n",
-    "test_dataset  = ImageFolder(root=\"sickle-cell-classification-dataset/test\", transform=val_transform)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a16b08c8-93fb-4cf3-9f1f-900508e1d109",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'AIN': 0, 'non_sickle': 1, 'sickle': 2}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(train_dataset.class_to_idx)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a4b8be88-5cb8-449b-9287-26b6a5151e79",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "batch_size = 32\n",
-    "num_workers = 0  # safe for Windows\n",
-    "\n",
-    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
-    "val_loader   = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
-    "test_loader  = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "dd1d9ad2-800c-4b87-a639-ad13158b69b5",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VGG(\n",
-      "  (features): Sequential(\n",
-      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (1): ReLU(inplace=True)\n",
-      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (3): ReLU(inplace=True)\n",
-      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (6): ReLU(inplace=True)\n",
-      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (8): ReLU(inplace=True)\n",
-      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (11): ReLU(inplace=True)\n",
-      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (13): ReLU(inplace=True)\n",
-      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (15): ReLU(inplace=True)\n",
-      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (18): ReLU(inplace=True)\n",
-      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (20): ReLU(inplace=True)\n",
-      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (22): ReLU(inplace=True)\n",
-      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (25): ReLU(inplace=True)\n",
-      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (27): ReLU(inplace=True)\n",
-      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "    (29): ReLU(inplace=True)\n",
-      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "  )\n",
-      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
-      "  (classifier): Sequential(\n",
-      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
-      "    (1): ReLU(inplace=True)\n",
-      "    (2): Dropout(p=0.5, inplace=False)\n",
-      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
-      "    (4): ReLU(inplace=True)\n",
-      "    (5): Dropout(p=0.5, inplace=False)\n",
-      "    (6): Linear(in_features=4096, out_features=3, bias=True)\n",
-      "  )\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torchvision.models import vgg16, VGG16_Weights\n",
-    "\n",
-    "# Number of classes\n",
-    "num_classes = len(train_dataset.classes)  # automatically from ImageFolder\n",
-    "\n",
-    "# Load pretrained VGG16\n",
-    "model = vgg16(weights=VGG16_Weights.DEFAULT)\n",
-    "\n",
-    "# Replace the last classifier layer\n",
-    "model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, num_classes)\n",
-    "\n",
-    "# Move to device\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = model.to(device)\n",
-    "\n",
-    "print(model)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "50f8f941-36a6-4986-8221-ce21dd4d1f66",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss, optimizer, and scheduler set up for VGGNet.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch.optim as optim\n",
-    "\n",
-    "criterion_vgg = nn.CrossEntropyLoss()\n",
-    "optimizer_vgg = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)\n",
-    "scheduler_vgg = optim.lr_scheduler.CosineAnnealingLR(optimizer_vgg, T_max=10)\n",
-    "\n",
-    "print(\"Loss, optimizer, and scheduler set up for VGGNet.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2cfb1d82-e0fd-4276-af78-b6497069a5d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10 [Train]:   7%|████▎                                                          | 6/88 [01:04<14:35, 10.67s/it]"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm\n",
-    "from IPython.display import clear_output\n",
-    "\n",
-    "num_epochs = 10\n",
-    "train_losses, val_losses = [], []\n",
-    "train_accs, val_accs = [], []\n",
-    "\n",
-    "for epoch in range(num_epochs):\n",
-    "    # --- Training ---\n",
-    "    model.train()\n",
-    "    running_loss, correct, total = 0, 0, 0\n",
-    "    for images, labels in tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Train]\"):\n",
-    "        images, labels = images.to(device), labels.to(device)\n",
-    "        optimizer_vgg.zero_grad()\n",
-    "        outputs = model(images)\n",
-    "        loss = criterion_vgg(outputs, labels)\n",
-    "        loss.backward()\n",
-    "        optimizer_vgg.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "        _, predicted = outputs.max(1)\n",
-    "        total += labels.size(0)\n",
-    "        correct += predicted.eq(labels).sum().item()\n",
-    "\n",
-    "    avg_train_loss = running_loss / len(train_loader)\n",
-    "    train_acc = 100. * correct / total\n",
-    "\n",
-    "    # --- Validation ---\n",
-    "    model.eval()\n",
-    "    val_loss, val_correct, val_total = 0, 0, 0\n",
-    "    with torch.no_grad():\n",
-    "        for images, labels in tqdm(val_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Val]\"):\n",
-    "            images, labels = images.to(device), labels.to(device)\n",
-    "            outputs = model(images)\n",
-    "            loss = criterion_vgg(outputs, labels)\n",
-    "            val_loss += loss.item()\n",
-    "            _, predicted = outputs.max(1)\n",
-    "            val_total += labels.size(0)\n",
-    "            val_correct += predicted.eq(labels).sum().item()\n",
-    "\n",
-    "    avg_val_loss = val_loss / len(val_loader)\n",
-    "    val_acc = 100. * val_correct / val_total\n",
-    "\n",
-    "    # --- Save metrics ---\n",
-    "    train_losses.append(avg_train_loss)\n",
-    "    val_losses.append(avg_val_loss)\n",
-    "    train_accs.append(train_acc)\n",
-    "    val_accs.append(val_acc)\n",
-    "\n",
-    "    # --- Update scheduler ---\n",
-    "    scheduler_vgg.step()\n",
-    "\n",
-    "    # --- Epoch summary ---\n",
-    "    clear_output(wait=True)\n",
-    "    print(f\"Epoch [{epoch+1}/{num_epochs}] \"\n",
-    "          f\"Train Loss: {avg_train_loss:.4f}, Train Acc: {train_acc:.2f}% | \"\n",
-    "          f\"Val Loss: {avg_val_loss:.4f}, Val Acc: {val_acc:.2f}%\")\n",
-    "\n",
-    "    # --- Live plots ---\n",
-    "    plt.figure(figsize=(10,4))\n",
-    "    plt.subplot(1,2,1)\n",
-    "    plt.plot(train_losses, label='Train Loss')\n",
-    "    plt.plot(val_losses, label='Val Loss')\n",
-    "    plt.title('Loss')\n",
-    "    plt.xlabel('Epoch')\n",
-    "    plt.ylabel('Loss')\n",
-    "    plt.legend()\n",
-    "\n",
-    "    plt.subplot(1,2,2)\n",
-    "    plt.plot(train_accs, label='Train Acc')\n",
-    "    plt.plot(val_accs, label='Val Acc')\n",
-    "    plt.title('Accuracy')\n",
-    "    plt.xlabel('Epoch')\n",
-    "    plt.ylabel('Accuracy (%)')\n",
-    "    plt.legend()\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69f51f76-a3dd-4a2b-9ec1-8ee77480f019",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import confusion_matrix, classification_report\n",
-    "from tqdm import tqdm\n",
-    "import numpy as np\n",
-    "\n",
-    "# --- 1. Save the trained model ---\n",
-    "torch.save(model_shufflenet.state_dict(), \"vggnet_sickle.pth\")\n",
-    "print(\"✅ Model saved as vggenet_sickle.pth\")\n",
-    "\n",
-    "\n",
-    "# --- 2. Evaluation function with predictions ---\n",
-    "def evaluate_with_preds(model, data_loader, device, split_name=\"Eval\"):\n",
-    "    model.eval()\n",
-    "    total, correct = 0, 0\n",
-    "    all_preds, all_labels = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for images, labels in tqdm(data_loader, desc=f\"Evaluating {split_name}\"):\n",
-    "            images, labels = images.to(device), labels.to(device)\n",
-    "            outputs = model(images)\n",
-    "            _, predicted = outputs.max(1)\n",
-    "\n",
-    "            total += labels.size(0)\n",
-    "            correct += predicted.eq(labels).sum().item()\n",
-    "\n",
-    "            all_preds.extend(predicted.cpu().numpy())\n",
-    "            all_labels.extend(labels.cpu().numpy())\n",
-    "\n",
-    "    accuracy = 100. * correct / total\n",
-    "    return accuracy, np.array(all_preds), np.array(all_labels)\n",
-    "\n",
-    "\n",
-    "# --- 3. Run evaluation on validation set ---\n",
-    "val_acc, preds, labels = evaluate_with_preds(model, val_loader, device, split_name=\"Val\")\n",
-    "\n",
-    "print(f\"\\n📊 Final Validation Accuracy: {val_acc:.2f}%\")\n",
-    "print(\"\\nClassification Report:\")\n",
-    "print(classification_report(labels, preds, target_names=dataset[\"train\"].features[\"label\"].names))\n",
-    "\n",
-    "\n",
-    "# --- 4. Confusion matrix heatmap ---\n",
-    "cm = confusion_matrix(labels, preds)\n",
-    "plt.figure(figsize=(6, 5))\n",
-    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
-    "            xticklabels=dataset[\"train\"].features[\"label\"].names,\n",
-    "            yticklabels=dataset[\"train\"].features[\"label\"].names)\n",
-    "plt.xlabel(\"Predicted\")\n",
-    "plt.ylabel(\"True\")\n",
-    "plt.title(\"ShuffleNet Confusion Matrix\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "854aedac-9b8d-4abc-843d-9b7ada1ee72f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
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