Add CNN class for handling MNIST dataset

src/cnn.py

@@ -0,0 +1,38 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class CNN(nn.Module):
+    """
+    A Convolutional Neural Network (CNN) class that inherits from torch.nn.Module.
+    """
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(64 * 7 * 7, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool(nn.functional.relu(self.conv1(x)))
+        x = self.pool(nn.functional.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 7 * 7)
+        x = nn.functional.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+def train_cnn(model, dataloader, epochs):
+    """
+    Trains a CNN model on the data from the DataLoader for the specified number of epochs.
+    """
+    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

@@ -16,33 +16,13 @@
 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)
+from cnn import CNN, train_cnn
 
-# Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
+# Step 2: Define the PyTorch Model
+model = CNN()
 
-# Training loop
+# Step 3: Train the Model
 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()
+train_cnn(model, trainloader, epochs)
 
 torch.save(model.state_dict(), "mnist_model.pth")