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  # 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()