Add CNN class for MNIST handling and import it to main.py

src/api.py

@@ -2,10 +2,10 @@
 from PIL import Image
 import torch
 from torchvision import transforms
-from main import Net  # Importing Net class from main.py
+from main import CNN  # Importing CNN class from main.py
 
 # Load the model
-model = Net()
+model = CNN()
 model.load_state_dict(torch.load("mnist_model.pth"))
 model.eval()
 

src/cnn.py

@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network (CNN) class for handling 28x28 grayscale images.
+    """
+    def __init__(self):
+        """
+        Initialize the CNN with convolutional, pooling, and fully connected layers.
+        """
+        super().__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(64 * 7 * 7, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        """
+        Implement the forward pass of the network.
+        """
+        x = F.relu(self.conv1(x))
+        x = self.pool(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool(x)
+        x = x.view(-1, 64 * 7 * 7)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x

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([
@@ -16,29 +17,16 @@
 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()  # Instantiate the CNN class
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()
 
+# Step 3: Train the Model
 # Training loop
 epochs = 3
 for epoch in range(epochs):
     for images, labels in trainloader:
+        images = images.view(-1, 1, 28, 28)  # Reshape the input data for the CNN
         optimizer.zero_grad()
         output = model(images)
         loss = criterion(output, labels)