train.py

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from model import SimpleCNN

transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)
train_data = datasets.MNIST(root="data", train=True, download=True, transform=transform)
test_data = datasets.MNIST(root="data", train=False, download=True, transform=transform)

train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64)

device = torch.device("cpu")
model = SimpleCNN().to(device)

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

for epoch in range(3):
    model.train()
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch + 1} complete")

model.eval()
correct, total = 0, 0
with torch.no_grad():
    for data, target in test_loader:
        data, target = data.to(device), target.to(device)
        output = model(data)
        preds = output.argmax(dim=1)
        correct += (preds == target).sum().item()
        total += target.size(0)

print(f"Test Accuracy: {correct / total:.4f}")