What Gradient Clipping Meaning, Applications & Example
Technique to prevent exploding gradients during training.
What is Gradient Clipping?
Gradient Clipping is a technique used in training deep learning models to prevent exploding gradients by limiting the size of gradients during backpropagation . This technique is especially helpful when training deep networks or models with long sequences, where gradients can become excessively large, leading to unstable learning and poor performance.
Methods of Gradient Clipping
- Norm-based Clipping: The gradients are scaled down to a predefined threshold if their norm exceeds that threshold.
- Value-based Clipping: Each gradient component is clipped individually if it exceeds a specified value.
- Global Gradient Clipping: All gradients in the model are clipped uniformly if the overall gradient norm exceeds the threshold.
Applications of Gradient Clipping
- Training Deep Neural Networks: Prevents instability in models like RNNs or LSTMs, where long sequences can lead to exploding gradients.
- Reinforcement Learning : Helps in stabilizing training when gradients can fluctuate wildly, ensuring consistent learning.
- Generative Models: Used in training generative adversarial networks (GANs) to stabilize the training process.
Example of Gradient Clipping
In a neural network with an exploding gradient problem, if the gradient norm exceeds a certain threshold, gradient clipping ensures that the gradients are scaled down, allowing the model to continue learning without the risk of overshooting during weight updates.