Add new CNN class for handling MNIST dataset and import it into main.py

src/cnn.py

@@ -0,0 +1,57 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network (CNN) class.
+    """
+
+    def __init__(self):
+        """
+        Initialize the CNN model.
+        """
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        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 = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+    def train(self, trainloader):
+        """
+        Train the CNN model.
+        """
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.SGD(self.parameters(), lr=0.01)
+
+        for _epoch in range(10):  # loop over the dataset multiple times
+            for _i, data in enumerate(trainloader, 0):
+                inputs, labels = data
+                optimizer.zero_grad()
+                outputs = self(inputs)
+                loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+
+        return self
+
+    def save_model(self, path):
+        """
+        Save the trained model.
+        """
+        torch.save(self.state_dict(), path)

src/main.py

@@ -1,48 +1,22 @@
 from PIL import Image
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from cnn import CNN
 
 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+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)
+model = CNN()
 
 # Step 3: Train the Model
-model = Net()
-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()
+model.train(trainloader)
 
-torch.save(model.state_dict(), "mnist_model.pth")
+# Save the trained model
+model.save_model("mnist_model.pth")