Gradient clipping

See also: Machine learning terms

Gradient Clipping in Machine Learning

Gradient clipping is a technique employed in machine learning, specifically during the training of deep neural networks, to mitigate the effect of exploding gradients. Exploding gradients occur when the gradients of the model parameters become excessively large, leading to instabilities and impairments in the learning process. Gradient clipping aids in the regularization of the learning process by limiting the magnitude of gradients during backpropagation, thus preventing the aforementioned issues.

Background and Motivation

In the training of deep neural networks, backpropagation is a widely used algorithm for minimizing the loss function by adjusting the model parameters (i.e., weights and biases). The learning process typically involves computing gradients of the loss function with respect to the model parameters, followed by updating the parameters using a gradient descent optimization algorithm.

However, in deep networks, gradients can sometimes grow exceedingly large, causing the model parameters to be updated with excessively large values, resulting in a phenomenon known as the exploding gradient problem. This can lead to instability in the learning process, as the model's parameters oscillate wildly, hindering convergence or causing the model to diverge entirely.

To overcome the exploding gradient problem, gradient clipping was introduced as a regularization technique that limits the magnitude of gradients during backpropagation.

Methods for Gradient Clipping

There are two primary methods for gradient clipping:

1. Global Norm Clipping: This method involves computing the global gradient norm, which is the aggregate measure of the gradients across all model parameters. If the global gradient norm exceeds a specified threshold, the gradients are scaled down, maintaining their relative direction, such that the global gradient norm is equal to the threshold value.

2. Individual Component Clipping: In this method, each component of the gradient vector is individually clipped if it exceeds a specified threshold value. This approach may lead to a distortion of the gradient's direction, but it effectively bounds the magnitude of individual gradient components.

Both methods can be utilized depending on the specific requirements of the problem and the optimization algorithm being employed.

Explain Like I'm 5 (ELI5)

Imagine you're learning how to ride a bike, and each time you make a mistake, your mom or dad gives you some advice on how to do better next time. Now, if they give you too much advice all at once, you might get overwhelmed and have a hard time learning. In machine learning, when a computer is learning something, it can also get "overwhelmed" if it receives too much advice (big gradients) at once.

Gradient clipping is like a helper who makes sure the computer doesn't get too much advice at once by limiting the size of the gradients. This way, the computer can learn more easily and avoid making big mistakes, just like you can learn better when your parents give you smaller pieces of advice.