feat: add CNN class for MNIST classification

src/cnn.py

@@ -0,0 +1,46 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import transforms
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network for MNIST classification.
+    """
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(7*7*64, 128)
+        self.relu3 = nn.ReLU()
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = x.view(-1, 7*7*64)
+        x = self.relu3(self.fc1(x))
+        return self.fc2(x)
+
+    def train(self, trainloader, lr, epochs):
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(self.parameters(), lr=lr)
+
+        for epoch in range(epochs):
+            running_loss = 0.0
+            for i, data in enumerate(trainloader, 0):
+                inputs, labels = data
+                optimizer.zero_grad()
+                outputs = self(inputs)
+                loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+                running_loss += loss.item()
+            print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader)}")
+
+    def save_model(self, file_path):
+        torch.save(self.state_dict(), file_path)