MNIST_CNN_rajas_daryapurkar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn  # nn module\n",
    "import torch.optim as optim  # optimizers\n",
    "import torch.nn.functional as F  #activation functions\n",
    "from torch.utils.data import DataLoader  #create mini batches\n",
    "import torchvision.datasets as datasets  # MNIST\n",
    "import torchvision.transforms as transforms  # transformations on datasets\n",
    "import matplotlib.pyplot as plt  # for visualization\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, in_channels=1, num_classes=10):  # in_channels = 1 because grayscale\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        self.bn1 = nn.BatchNorm2d(16)  # Batch Normalization\n",
    "        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
    "        \n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        self.bn2 = nn.BatchNorm2d(32)  # Batch Normalization\n",
    "        \n",
    "        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        self.bn3 = nn.BatchNorm2d(64)  # Batch Normalization\n",
    "        \n",
    "        # Dropout layer\n",
    "        self.dropout = nn.Dropout(p=0.5)  # Increased dropout rate\n",
    "\n",
    "        # Fully connected layers\n",
    "        self.fc1 = nn.Linear(64 * 3 * 3, 256)  # Increased number of neurons\n",
    "        self.fc2 = nn.Linear(256, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.bn1(self.conv1(x)))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.bn2(self.conv2(x)))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.bn3(self.conv3(x)))\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Flatten the tensor for the fully connected layers\n",
    "        x = x.view(x.shape[0], -1)  # Flatten the output\n",
    "\n",
    "        x = self.dropout(F.relu(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CNN().to(device)\n",
    "\n",
    "# Hyperparameters\n",
    "in_channels = 1\n",
    "learning_rate = 0.0006\n",
    "batch_size = 128\n",
    "num_epochs = 10  # Increased for better training\n",
    "\n",
    "# Data transformations with augmentation\n",
    "train_transforms = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),  # Randomly change brightness, contrast, saturation, and hue\n",
    "    transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),  # Randomly translate the image\n",
    "    transforms.RandomPerspective(distortion_scale=0.2, p=0.5, interpolation=3),  # Apply random perspective transformation\n",
    "    transforms.RandomResizedCrop(size=28, scale=(0.8, 1.0)),  # Randomly resize and crop\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "\n",
    "\n",
    "train_dataset = datasets.MNIST(root='dataset/', train=True, download=True, transform=train_transforms)\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='dataset/', train=False, download=True, transform=transforms.ToTensor())\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(loader, model):\n",
    "    model.eval()  #Set the model to evaluation mode implies no dropout or batch norm\n",
    "    num_correct = 0\n",
    "    num_samples = 0\n",
    "\n",
    "    with torch.no_grad():  # No gradient calculation for evaluation\n",
    "        for x, y in loader:\n",
    "            x, y = x.to(device), y.to(device)  #Move data to the device\n",
    "            scores = model(x) #scores.shape = (batch_size, num_classes)\n",
    "            _, predictions = scores.max(1)  #Gets the index of the  class with the highest score and we ignore the maximum value \n",
    "            num_correct += (predictions == y).sum().item()#correct predictions\n",
    "            num_samples += predictions.size(0)#total predictions\n",
    "\n",
    "    accuracy = float(num_correct) / num_samples * 100\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/20], Loss: 0.5590, Train Acc: 88.42%, Test Acc: 92.21%\n",
      "Epoch [2/20], Loss: 0.2411, Train Acc: 95.28%, Test Acc: 97.10%\n",
      "Epoch [3/20], Loss: 0.1889, Train Acc: 95.79%, Test Acc: 97.77%\n",
      "Epoch [4/20], Loss: 0.1640, Train Acc: 96.41%, Test Acc: 97.94%\n",
      "Epoch [5/20], Loss: 0.1467, Train Acc: 96.79%, Test Acc: 98.18%\n",
      "Epoch [6/20], Loss: 0.1372, Train Acc: 96.93%, Test Acc: 98.36%\n",
      "Epoch [7/20], Loss: 0.1276, Train Acc: 96.98%, Test Acc: 98.33%\n",
      "Epoch [8/20], Loss: 0.1218, Train Acc: 97.32%, Test Acc: 98.38%\n",
      "Epoch [9/20], Loss: 0.1120, Train Acc: 97.42%, Test Acc: 98.57%\n",
      "Epoch [10/20], Loss: 0.1113, Train Acc: 97.44%, Test Acc: 98.57%\n",
      "Epoch [11/20], Loss: 0.1038, Train Acc: 97.60%, Test Acc: 98.83%\n",
      "Epoch [12/20], Loss: 0.1028, Train Acc: 97.78%, Test Acc: 98.78%\n",
      "Epoch [13/20], Loss: 0.0952, Train Acc: 97.61%, Test Acc: 98.62%\n",
      "Epoch [14/20], Loss: 0.0987, Train Acc: 97.88%, Test Acc: 98.82%\n",
      "Epoch [15/20], Loss: 0.0936, Train Acc: 97.71%, Test Acc: 98.81%\n",
      "Epoch [16/20], Loss: 0.0884, Train Acc: 97.61%, Test Acc: 98.62%\n",
      "Epoch [17/20], Loss: 0.0884, Train Acc: 98.04%, Test Acc: 98.80%\n",
      "Epoch [18/20], Loss: 0.0863, Train Acc: 97.91%, Test Acc: 98.82%\n",
      "Epoch [19/20], Loss: 0.0855, Train Acc: 98.03%, Test Acc: 98.62%\n",
      "Epoch [20/20], Loss: 0.0830, Train Acc: 98.18%, Test Acc: 98.87%\n"
     ]
    }
   ],
   "source": [
    "train_losses = []\n",
    "test_losses = []\n",
    "train_accuracies = []\n",
    "test_accuracies = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # Set model to training mode enables batch_norm \n",
    "    running_loss = 0.0\n",
    "\n",
    "    for batch_idx, (data, targets) in enumerate(train_loader):\n",
    "        data, targets = data.to(device), targets.to(device)  # Move data to the device\n",
    "        optimizer.zero_grad()\n",
    "        scores = model(data)\n",
    "        loss = criterion(scores, targets)\n",
    "        loss.backward() #computes the gradients \n",
    "        optimizer.step() #updates the model parameters \n",
    "\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    # Calculate average training loss\n",
    "    avg_train_loss = running_loss / len(train_loader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "\n",
    "    # Evaluate on training and test set\n",
    "    train_accuracy = accuracy(train_loader, model)\n",
    "    test_accuracy = accuracy(test_loader, model)\n",
    "\n",
    "    train_accuracies.append(train_accuracy)\n",
    "    test_accuracies.append(test_accuracy)\n",
    "\n",
    "    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {avg_train_loss:.4f}, Train Acc: {train_accuracy:.2f}%, Test Acc: {test_accuracy:.2f}%')\n",
    "    torch.save(model.state_dict(), \"digit_recognizer_model.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Loss vs. Epochs\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_losses, label='Training Loss')\n",
    "plt.plot(test_losses, label='Test Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Accuracy vs. Epochs\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_accuracies, label='Training Accuracy')\n",
    "plt.plot(test_accuracies, label='Test Accuracy')\n",
    "plt.title('Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_images(loader, model, num_images=5):\n",
    "    model.eval()\n",
    "    images, labels = next(iter(loader))\n",
    "\n",
    "    with torch.no_grad():\n",
    "        outputs = model(images)\n",
    "        _, preds = outputs.max(1)\n",
    "\n",
    "    # Set up the plot\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    for i in range(num_images):\n",
    "        plt.subplot(2, num_images, i + 1)\n",
    "        plt.imshow(images[i].squeeze(), cmap='gray')\n",
    "        plt.title(f'True: {labels[i]} Pred: {preds[i]}')\n",
    "        plt.axis('off')\n",
    "    plt.show()# %% [code]\n",
    "def visualize_images(loader, model, num_images=5):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    images, labels = next(iter(loader))\n",
    "    \n",
    "    images = images.to(device)  # Move images to the appropriate device (CPU or GPU)\n",
    "    labels = labels.to(device)  # Move labels to the appropriate device (CPU or GPU)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        outputs = model(images)\n",
    "        _, preds = outputs.max(1)\n",
    "\n",
    "    # Set up the plot\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(num_images):\n",
    "        plt.subplot(1, num_images, i + 1)\n",
    "        plt.imshow(images[i].cpu().numpy().squeeze(), cmap='gray')  # Move images back to CPU for plotting\n",
    "        plt.title(f'Pred: {preds[i].item()}, Label: {labels[i].item()}')\n",
    "        plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "# Call the visualization function after training to see the predictions\n",
    "visualize_images(test_loader, model)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}