Add CNN class for handling MNIST dataset

src/cnn.py

@@ -0,0 +1,28 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNN(nn.Module):
+    """
+    A Convolutional Neural Network model for handling MNIST dataset.
+    """
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(7*7*64, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        """
+        Forward pass of the CNN.
+        """
+        x = F.relu(self.conv1(x))
+        x = self.pool(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool(x)
+        x = x.view(-1, 7*7*64)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)

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 from cnn.py
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -16,22 +17,10 @@
 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)
+# We are using the CNN class defined in cnn.py as our model
+model = CNN()  # Instantiate the CNN class
 
 # Step 3: Train the Model
-model = Net()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()