Deep Learning, Regularization Techniques, Python Programming, Data Science, Technology.
mlp.compute_l1_loss(torch.cat(l1_parameters)) # Add L1 loss component loss += l1 # Perform backward pass loss.backward() # Perform optimization optimizer.step() # Print statistics minibatch_loss = loss.item() if i % 500 == 499: print('Loss after mini-batch %5d: %.5f (of which %.5f L1 loss)' % (i +