Untitled

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    
    model.train()
    train_loss, correct = 0, 0
    
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(DEVICE), y.to(DEVICE)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        train_loss += loss.item()
        correct += (pred.round() == y).type(torch.float).sum().item()
        
        # Backpropagation
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

    train_loss /= num_batches
    correct /= size
    print(f"Train Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {train_loss:>8f} \n")