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How to do gradient clipping in pytorch?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to do gradient clipping in pytorch in Python. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

How to do gradient clipping in pytorch?

  1. How to do gradient clipping in pytorch?

    From your example it looks like that you want clip_grad_value_ instead which has a similar syntax and also modifies the gradients in-place:

  2. do gradient clipping in pytorch

    From your example it looks like that you want clip_grad_value_ instead which has a similar syntax and also modifies the gradients in-place:

Method 1

A more complete example

optimizer.zero_grad()        
loss, hidden = model(data, hidden, targets)
loss.backward()

torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()

Method 2

clip_grad_norm (which is actually deprecated in favor of clip_grad_norm_ following the more consistent syntax of a trailing _ when in-place modification is performed) clips the norm of the overall gradient by concatenating all parameters passed to the function, as can be seen from the documentation:

The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place.

From your example it looks like that you want clip_grad_value_ instead which has a similar syntax and also modifies the gradients in-place:

clip_grad_value_(model.parameters(), clip_value)

Another option is to register a backward hook. This takes the current gradient as an input and may return a tensor which will be used in-place of the previous gradient, i.e. modifying it. This hook is called each time after a gradient has been computed, i.e. there’s no need for manually clipping once the hook has been registered:

for p in model.parameters():
    p.register_hook(lambda grad: torch.clamp(grad, -clip_value, clip_value))

Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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