feat: Updated src/main.py

src/main.py

@@ -1,48 +1,44 @@
-from PIL import Image
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torchvision import datasets, transforms
+from cnn import CNN, train
+from PIL import Image
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
-    transforms.ToTensor(),
+transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))
 ])
 
 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):
-    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)
-
-# Step 3: Train the Model
+
+
+
+
+
+
+
+
 model = Net()
+model = CNN()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()
 
 # 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()
+train(model, trainloader, optimizer)
+
 
 torch.save(model.state_dict(), "mnist_model.pth")