Is this code an effective way to compute the global L2 gradient norm of the model after each training epoch : -
current_gradient_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=float('inf'), norm_type=2.0)
I do not want to clip the gradients, I only want to find and store the entire model’s gradient norms after each epoch