Why do we use gradient clipping?
Gradient clipping ensures the gradient vector g has norm at most c. This helps gradient descent to have a reasonable behaviour even if the loss landscape of the model is irregular. The following figure shows an example with an extremely steep cliff in the loss landscape.
What is normalized gradient?
Compared to the regular gradient, normalized gradient only provides an updating direction but does not incorporate the local steepness of the objective through its magnitude, which helps to control the change of the solution through a well-designed step length.
Why are gradients important in a neural network?
Thus gradients are key to the power of a neural network. However, gradients often get smaller as the algorithm progresses down to the lower layers. So lower layer weights are unchanged, which leads the training to never converge to a good solution.
When do exploding gradients occur in gradient clipping?
As explained, exploding gradients can occur when the gradient becomes too large and error gradients accumulate, resulting in an unstable network. The aim of gradients clipping is to rescale gradients so that their norm is at a particular value.
Is the BN layer designed to prevent exploding gradients?
BN was not designed specially to prevent exploding gradients. BN layer controls the norm of activations on the forward pass, and then it uses the same scaling for the backward pass. But it does not help to control the norm of gradients. Therefore, it is still useful to know about gradient clipping.
What’s the purpose of gradient clipping in Excel?
The aim of gradients clipping is to rescale gradients so that their norm is at a particular value. With gradient clipping, a pre-determined gradient threshold is introduced, and the gradient norms that exceed this threshold are scaled down to match the norm. The clipping_value is an Hyperparameter that we can tune.