convolutional neural networks output model(imagespredicted output correct +(labels reshape(- , =predicted reshape(- , )float(sum(#clear memory del([images,labels]if device ="cuda"torch cuda empty_cache(print('\nval accuracy{}/{({ }%)\nformatcorrectlen(data_loader dataset) correct len(data_loader dataset))with that being in placelet' define and create model instance and train our network for epochs listing - demonstrates defining the loss function and optimizercreating model instanceand training for defined number of epochs listing - defining the loss function and optimizercreating the model instanceand training for defined number of epochs num_epochs loss_function nn bceloss(#binary crosss entropy loss model convnet(model cuda(adam_optimizer torch optim adam(model parameters()lr train the model total_step len(train_loaderprint("total batches:",total_step