Add CNN class for handling MNIST dataset

src/cnn.py

@@ -0,0 +1,43 @@
+import torch.nn as nn
+import torch.optim as optim
+
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network (CNN) class.
+    """
+
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(7 * 7 * 64, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = x.view(-1, 7 * 7 * 64)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+
+def train_cnn(model, dataloader, epochs):
+    """
+    Function to train the CNN model.
+    """
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.CrossEntropyLoss()
+
+    for _epoch in range(epochs):
+        for images, labels in dataloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()

src/main.py

@@ -1,48 +1,25 @@
-from PIL import Image
+import numpy as np
 import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from cnn import CNN, train_cnn
 
 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
-# Step 2: Define the PyTorch Model
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(28 * 28, 128)
-        self.fc2 = nn.Linear(128, 64)
-        self.fc3 = nn.Linear(64, 10)
-
-    def forward(self, x):
-        x = x.view(-1, 28 * 28)
-        x = nn.functional.relu(self.fc1(x))
-        x = nn.functional.relu(self.fc2(x))
-        x = self.fc3(x)
-        return nn.functional.log_softmax(x, dim=1)
-
-# Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
+# Step 2: Define the CNN Model
+model = CNN()
+print("CNN model defined.")
 
-# Training loop
-epochs = 3
-for epoch in range(epochs):
-    for images, labels in trainloader:
-        optimizer.zero_grad()
-        output = model(images)
-        loss = criterion(output, labels)
-        loss.backward()
-        optimizer.step()
+# Step 3: Train the CNN Model
+epochs = 10
+train_cnn(model, trainloader, epochs)
+print("CNN model trained.")
 
-torch.save(model.state_dict(), "mnist_model.pth")
+torch.save(model.state_dict(), "mnist_model.pth")