feat: Updated src/main.py

src/main.py

@@ -7,6 +7,7 @@
 import numpy as np
 
 # Step 1: Load MNIST Data and Preprocess
+# The MNIST dataset is loaded and preprocessed by transforming the images to tensors and normalizing them.
 transform = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))
@@ -15,6 +16,10 @@
 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):
+trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+
 # Step 2: Define the PyTorch Model
 class Net(nn.Module):
     def __init__(self):
@@ -37,6 +42,7 @@ def forward(self, x):
 
 # Training loop
 epochs = 3
+epochs = 3
 for epoch in range(epochs):
     for images, labels in trainloader:
         optimizer.zero_grad()
@@ -45,4 +51,11 @@ def forward(self, x):
         loss.backward()
         optimizer.step()
 
+torch.save(model.state_dict(), "mnist_model.pth")
+
+torch.save(model.state_dict(), "mnist_model.pth")
+        loss = criterion(output, labels)
+        loss.backward()
+        optimizer.step()
+
 torch.save(model.state_dict(), "mnist_model.pth")