convolutional neural networks 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())#after each epoch evaluate model evaluate(new_model,val_loaderoutput[total batches epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %