train.py

for epoch in range(num_epochs):
    train_running_loss = 0.0
    train_acc = 0.0

    model = model.train()

    ## training step
    for i, (images, labels) in enumerate(trainloader):
        
        images = images.to(device)
        labels = labels.to(device)

        ## forward + backprop + loss
        logits = model(images)
        loss = criterion(logits, labels)
        optimizer.zero_grad()
        loss.backward()

        ## update model params
        optimizer.step()

        train_running_loss += loss.detach().item()
        train_acc += get_accuracy(logits, labels, BATCH_SIZE)
    
    model.eval()
    print('Epoch: %d | Loss: %.4f | Train Accuracy: %.2f' \
          %(epoch, train_running_loss / i, train_acc/i))