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Gradient Clipping

What is Gradient Clipping?

Gradient clipping is a technique to prevent exploding gradients in very deep networks, usually in recurrent neural networks. A neural network is a learning algorithm, also called neural network or neural net, that uses a network of functions to understand and translate data input into a specific output. This type of learning algorithm is designed based on the way neurons function in the human brain. There are many ways to compute gradient clipping, but a common one is to rescale gradients so that their norm is at most a particular value. With gradient clipping, pre-determined gradient threshold be introduced, and  then gradients norms that exceed this threshold are scaled down to match the norm.  This prevents any gradient to have norm greater than the threshold and thus the gradients are clipped.  There is an introduced bias in the resulting values from the gradient, but gradient clipping can keep things stable. 

Why is this Useful?

Training recurrent neural networks can be very difficult. Two common issues with training recurrent neural networks are vanishing gradients and exploding gradients. Exploding gradients can occur when the gradient becomes too large and error gradients accumulate, resulting in an unstable network. Vanishing gradients can happen when optimization gets stuck at a certain point because the gradient is too small to progress. Gradient clipping can prevent these issues in the gradients that mess up the parameters during training.

Practical Uses of Gradient Clipping

  • Deep Learning

    – Gradient clipping is a technique used in deep learning to optimize and solve problems. Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the human brain and neural networks.