The gradient norm and the noise

autograd
1

I am training ResNet101 on tiny-imagenet. How to compute gradient l2 norm and the noise of the gradient (see the last two strings) per epoch? L is a classic loss function.
norm and noise

Firstly I need to compute them per iteration and then average. Is it OK for gradient norm?

for batch_num, (X, y) in enumerate(train_loader):
        X = X.to(device)
        y = y.to(device)
        optimizer.zero_grad()
        y_pred = model(X)
        loss = loss_fn(y_pred, y)
        loss_value += loss.item()
        loss.backward()
        optimizer.step()
        
        # Compute gradients for batch
        all_batch_gradients = []
        for param in model.parameters():
            all_batch_gradients.append(param.grad.view(-1,1).to("cpu"))
       all_batch_gradients = torch.concat(all_batch_gradients)