feat: Updated src/main.py

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, train_model
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -31,18 +32,12 @@ def forward(self, x):
         return nn.functional.log_softmax(x, dim=1)
 
 # Step 3: Train the Model
-model = Net()
+model = CNN()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
+criterion = nn.CrossEntropyLoss()
 
 # 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()
+model = train_model(model, trainloader, criterion, optimizer, epochs)
 
 torch.save(model.state_dict(), "mnist_model.pth")