train.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms


# ----------------- TRANSFORMS -----------------
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])


# ----------------- LOAD DATASET -----------------
train_set = torchvision.datasets.CIFAR10(
    root="./data", train=True, download=True, transform=transform
)

test_set = torchvision.datasets.CIFAR10(
    root="./data", train=False, download=True, transform=transform
)

train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)


# ----------------- BUILD CNN MODEL -----------------
class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv_layer = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),  
            nn.ReLU(),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )

        self.fc_layer = nn.Sequential(
            nn.Linear(64 * 8 * 8, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        )

    def forward(self, x):
        x = self.conv_layer(x)
        x = x.view(x.size(0), -1)
        x = self.fc_layer(x)
        return x


model = CNN()

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)


# ----------------- TRAIN LOOP -----------------
for epoch in range(5):
    running_loss = 0.0
    for images, labels in train_loader:
        optimizer.zero_grad()

        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f"Epoch {epoch+1}, Loss: {running_loss/len(train_loader)}")


# ----------------- SAVE MODEL -----------------
torch.save(model.state_dict(), "model.pth")
print("Model saved as model.pth")