convolutional neural networks for epoch in range(num_epochs)model train(train_loss for (imageslabelsin enumerate(train_loader)images images to(devicelabels labels to(deviceforward pass outputs model(imagesloss loss_function(outputs float()labels float(view(- , )backward and optimize adam_optimizer zero_grad(loss backward(adam_optimizer step(train_loss +loss item()labels size( #after each epoch print train loss and validation loss accuracy print ('epoch [{}/{}]loss{ }format(epoch+ num_epochsloss item())#evaluate model after each training epoch evaluate(model,val_loaderoutput[total batches epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %