Add CNN class for training MNIST model and import it into main.py

src/cnn.py

@@ -0,0 +1,46 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import transforms
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network for MNIST classification.
+    """
+    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.relu3 = nn.ReLU()
+        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.relu3(self.fc1(x))
+        return self.fc2(x)
+
+    def train(self, trainloader, lr, epochs):
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(self.parameters(), lr=lr)
+
+        for epoch in range(epochs):
+            running_loss = 0.0
+            for i, data in enumerate(trainloader, 0):
+                inputs, labels = data
+                optimizer.zero_grad()
+                outputs = self(inputs)
+                loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+                running_loss += loss.item()
+            print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader)}")
+
+    def save_model(self, file_path):
+        torch.save(self.state_dict(), file_path)

src/main.py

@@ -5,6 +5,7 @@
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
 import numpy as np
+from cnn import CNN  # Import the CNN class
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -16,19 +17,14 @@
 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)
+# Create an instance of the CNN class
+cnn = CNN()
+
+# Train the CNN
+cnn.train(trainloader, lr=0.001, epochs=10)
+
+# Save the trained model
+cnn.save_model("mnist_model.pth")
 
 # Step 3: Train the Model
 model = Net()