A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

Related tags

Deep Learning SMU_pytorch
Overview

SMU_pytorch

A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

arXiv

https://arxiv.org/abs/2111.04682

Requirements

Pytorch 1.7

Tensorflow version of SMU activation

Please check https://github.com/iFe1er/SMU for tensorflow 2.x implementation.

Reference:

@ARTICLE{2021arXiv211104682B, author = {{Biswas}, Koushik and {Kumar}, Sandeep and {Banerjee}, Shilpak and {Pandey}, Ashish Kumar}, title = "{SMU: smooth activation function for deep networks using smoothing maximum technique}", journal = {arXiv e-prints}, keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing}, year = 2021, month = nov, eid = {arXiv:2111.04682}, pages = {arXiv:2111.04682}, archivePrefix = {arXiv}, eprint = {2111.04682}, primaryClass = {cs.LG}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv211104682B}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

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Comments
  • question

    question

    请问yolov5 使用smu激活函数为什么会有报这种错，UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skip？

    opened by weizhao-11 1
  • Update the parameters

    Update the parameters

    @iFe1er Hi, I am the author of the paper and I have updated the paper. Please update the parameters in your repository as well as people using your code. I will release an official code in the future. Here are the updated values of the parameters.

    SMU alpha=0.01, mu=2.5

    SMU-1 alpha=0.01, mu=4.332461424154261e-09

    Thank you.

    opened by koushik313 3
  • I use SMU instead of SILU in YoloV5, but loss shows up as nan

    I use SMU instead of SILU in YoloV5, but loss shows up as nan

    I use SMU instead of SILU in YoloV5, but loss shows up as nan.

    Could you please tell me the possible reason？Or maybe it's normal that this happened in previous epochs？

    image

    opened by mzzjuve 2
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