sam

SAM: Sharpness-Aware Minimization (PyTorch) (by davda54)

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sam reviews and mentions

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  • What is the correct way to sum loss into a total loss and then to backprop?
    1 project | /r/MLQuestions | 24 Nov 2021
    Which from here I understand that I shouldn't use the same loss variable for both forward passes but I'm not sure how else to do this. I thought that I could maybe create a variable called total_loss and add the loss to it and then after the iterations to backprop over it, but I'm not sure if that's the correct approach.
  • [R] Sharpness-Aware Minimization for Efficiently Improving Generalization
    1 project | /r/MachineLearning | 30 Apr 2021
    They reached sota on a few tasks. Do you really believe that the entire community missed the magic hyperparameters of batch size 128 and Adam to beat SOTA? I think getting SOTA really solidifies the approach, albeit the 2x speed cost seems heavy. As for implementation, it looks fairly trivial to adapt to all optimizers, at least from this random github https://github.com/davda54/sam
  • Help me implement this paper expanding on Google's SAM optimizer
    1 project | /r/MLQuestions | 12 Apr 2021
    Here is the code for SAM. SAM isn't too complicated. There are two forward and backward passes, a gradient accent after the first one, and the gradient decent after the second. The gradient accent is to get the noised SAM model which is calculated as for each p in param group add epsilon, which is rho * (p.grad / grad_norm), with rho being SAM's only hyperparameter.
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Stats

Basic sam repo stats
3
1,572
0.0
about 1 year ago

davda54/sam is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of sam is Python.


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