What is the pytorch gradient clipping method?

This article introduces the relevant knowledge of "what is the gradient tailoring method of pytorch". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

I won't say much nonsense. Let's look at the example.

Import torch.nn as nnoutputs = model (data) loss= loss_fn (outputs, target) optimizer.zero_grad () loss.backward () nn.utils.clip_grad_norm_ (model.parameters (), max_norm=20, norm_type=2) optimizer.step ()

Parameters of nn.utils.clip_grad_norm_:

Parameters-A variable-based iterator that normalizes gradients

Maximum norm of max_norm-gradient

Norm_type-specifies the type of norm, default to L2

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