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View Code? Open in Web Editor NEWQuasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
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What do you think about diffgrad?
This is a greate repo, my respect.
OverflowError: (34, 'Numerical result out of range')
I think DEMON based optimizers have some issue. Line 375 momentum.div_(1 - (beta1 ** state['step'])).mul_(nu1).add_(1-nu1, grad) gives the following error after few iterations of training.
OverflowError: (34, 'Numerical result out of range')
It seems to be a general issue:
https://discuss.pytorch.org/t/overflowerror-34-numerical-result-out-of-range/1907
I tried their recommendation and it still didn't work. My value of beta1 was around -3 when this overflow error was raised. I believe beta1 should stay in [0,1] ?
This problem occurs in other optimizers also. Removing DEMON option seems to get rid of this issue.
Comparison of optimizers on some standard benckmark
Thanks for your contribution. Is there a guideline/comparison you provide for these optimizers? Which one would be best for standard tasks like image classification?
HyperRangerMod do not work
/content/DemonRangerOptimizer/optimizers.py in step(self, display, activate_IA, closure)
883 denom = vt.pow_(group['p']).add_(group['eps'])
884
--> 885 n = state['lr']/denom
886
887 if beta3 > 0.0: # apply AdaMod
KeyError: 'lr'
Recent pytorch's adam changes
https://github.com/pytorch/pytorch/commits/master/torch/optim/adam.py
Where is a lot of changes like no grads cope decorator.
Not sure if it affects math/performance.
For your consideration.
Instability with HyperProp
I tried it in two tasks, but got nans during training, any suggestions?
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