Proper way to do gradient clipping?
Is there a proper way to do gradient clipping, for example, with Adam? It seems like that the value of Variable.data.grad should be manipulated (clipped) before calling optimizer.step() method. I...
View ArticleProper way to do gradient clipping?
You can safely modify Variable.grad.data in-place after the backward pass finishes. For example see how it’s done in the language modelling example. The reason for that is that it has a nice user...
View ArticleProper way to do gradient clipping?
I have tested nn.LSTM against simple LSTM implementation and found almost no difference in the performance. Maybe I overestimated the overhead of the additional addition with simple guess. Thank you!...
View ArticleProper way to do gradient clipping?
If you’re running on GPU you’ll also likely see great speedups from using cuDNN LSTM implementation. Read full topic
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Proper way to do gradient clipping?
I have tested in CPU and got no better results than just few milliseconds. (for someone who may try to implement LSTM for benchmarking ) I think some more addition is insignificant than another...
View ArticleProper way to do gradient clipping?
Quick question about this @apaszke , are the Variable.grad.data that we should pass to our clip function a part of the model object or (if we use a different optimizer) - the optimizer object? In the...
View ArticleProper way to do gradient clipping?
I’m sorry but I don’t understand the question. Optimizer never calls backward() itself, unless you give it a callable argument (see torch.optim docs for more details on that). BTW you might want to...
View ArticleProper way to do gradient clipping?
Maybe I’m doing something wrong here, but using gradient clipping like nn.utils.clip_grad_norm(model.parameters(), clip) for p in model.parameters(): p.data.add_(-lr, p.grad.data) makes my network...
View ArticleProper way to do gradient clipping?
Maybe you’re clipping them to very small values. It’s a possible effect Read full topic
View ArticleProper way to do gradient clipping?
The one comes with nn.util clips in proportional to the magnitude of the gradients. Thus you’d like to make sure it is not too small for your particular model as Adam said (I think :p). The...
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Proper way to do gradient clipping?
for people trying to just get an answer quickly: torch.nn.utils.clip_grad_norm(mdl_sgd.parameters(),clip) or with in-place clamp: W.grad.data.clamp_(-clip,clip) also similar Q: Gradient clipping Hi...
View ArticleProper way to do gradient clipping?
I thought nn.utils.clip_grad_norm(model.parameters(), clip) is supposed to finish the job. What is: for p in model.parameters(): p.data.add_(-lr, p.grad.data) for? Can someone give a more explicit...
View ArticleProper way to do gradient clipping?
@ntubertchen Hi, Use torch.nn.utils.clip_grad_norm to keep the gradients within a specific range (clip). In RNNs the gradients tend to grow very large (this is called ‘the exploding gradient...
View ArticleProper way to do gradient clipping?
Note that clip_grad_norm_ modifies the gradient after the entire backpropagation has taken place. In the RNN context it is common to restrict the gradient that is being backpropagated during the...
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Proper way to do gradient clipping?
Brando_Miranda: torch.nn.utils.clip_grad_norm(mdl_sgd.parameters(),clip) Thanks. Where does this go in relation to forward and backward propagation? Read full topic
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Proper way to do gradient clipping?
Neta_Zmora: In other words, this performs a similar function as optimizer.step(), using the gradients to updates the model parameters, but without the extra sophistication of a torch.optim.Optimizer....
View ArticleProper way to do gradient clipping?
No reason: you can certainly use optimizer.step() and it will most likely lead to a better solution since the optimizer will update the parameters in a more sophisticated way (e.g. using momentum)....
View ArticleProper way to do gradient clipping?
My bad, I thought what you suggest is that if you do gradient clipping, then you should (for some reason) use custom updates instead of optimizer.step(). Now I got it, you meant that if you use custom...
View ArticleProper way to do gradient clipping?
You need to use both optimizer.step and clip right? Because optimizer.step calcultes the gradient and then you want to clip those gradients to prevent vanishing on the next training step? Read full...
View ArticleProper way to do gradient clipping?
No, loss.backward() calculates the gradient, clip_grad_norm_ limits it’s norm and optimizer.step() updates the parameters. But yes, you need the first and last. Best regards Thomas Read full topic
View ArticleProper way to do gradient clipping?
Does Variable.grad.data gives access to normalized gradients per batch? If yes, how can I have access to unnormalized gradients? Read full topic
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