Deep Learning, Regularization Techniques, Python Programming, Data Science, Technology.
mlp.parameters(): parameters.append(parameter.view(-1)) l1 = l1_weight * mlp.compute_l1_loss(torch.cat(parameters)) l2 = l2_weight * mlp.compute_l2_loss(torch.cat(parameters)) # Add L1 and L2 loss components loss += l1 loss += l2 # Perform backward pass loss.backward() # Perform optimization