What is the purpose of gradient clipping in PyTorch?

Gradient clipping is a technique used to control the size of gradients in neural network models. During training, gradient clipping can help prevent problems such as exploding or vanishing gradients, ultimately improving the stability and convergence speed of training.

In PyTorch, you can use the torch.nn.utils.clip_grad_norm_() function to clip the gradients of a model. By specifying a clipping threshold, gradients with a norm exceeding this threshold will be rescaled to ensure they do not become too large.

The main function of gradient clipping includes:

  1. Preventing gradient explosion: When the values of gradients are too large, it may cause excessive updates in model parameters, preventing the model from converging or leading to numerical instability.
  2. Prevent gradient vanishing: When the value of the gradient becomes too small, it may result in difficulties in updating the model parameters, thereby affecting the effectiveness of the model training.

Overall, gradient clipping can help improve the stability and training effectiveness of neural network models, especially when dealing with long sequence data or deep networks.

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