convolutional neural networks forward pass outputs model(imagesloss criterion(outputslabelsbackward and optimize optimizer zero_grad(loss backward(optimizer step(#after each epoch print train loss and validation loss accuracy print ('epoch [{}/{}]loss{ }format(epoch+ num_epochsloss item())evaluate(val_loaderoutput[epoch [ / ]loss average val loss val accuracy( %epoch [ / ]loss average val loss val accuracy( %epoch [ / ]loss average val loss val accuracy( %epoch [ / ]loss average val loss val accuracy( %epoch [ / ]loss average val loss val accuracy( %we can see that the model has achieved fairly positive results on the validation dataset with accuracy (within five epochs)we can conclude our model has good performance