Learning to Initialize Neural Networks for Stable and Efficient Training

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Deep Learning gradinit
Overview

GradInit

This repository hosts the code for experiments in the paper, GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training.

Scripts for experiments on CIFAR-10 is currently available. Please refer to launch/run_gradinit_densenet.sh for DenseNet-100, launch/run_gradinit_wrn.sh for WRN-28-10, and launch/run_gradinit.sh for other networks shown in the paper. We will release the code for ImageNet and IWSLT experiments soon.

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Comments
  • division of the loss value by eta

    division of the loss value by eta

    Hello! Thank you for a great paper. Could you please explain the idea behind the division of the loss value by eta? I didn't see any information related to this in the paper. In my experiments, I haven't observed any significant difference between the code with/without this division, but maybe I am missing something important.

    https://github.com/zhuchen03/gradinit/blob/cb4685348b2a6d216f35c16cbf9c2450af062d6f/gradinit_utils.py#L231

    opened by DenisKoposov 1
  • Code to run fairseq IWSLT experiments?

    Code to run fairseq IWSLT experiments?

    I would love to test your method out on language modeling tasks in fairseq.

    Do you have the code to make table 2 (or just the GradInit rows in Table 2) handy?

    opened by sshleifer 2
  • Great Stuff but Needs Better Usability

    Great Stuff but Needs Better Usability

    Hello,

    Thanks for such a great work. Auto-initializing a DNN in a proper way definetely sounds amazing.

    Yet, the usability needs to be significantly improved so that I can plug this in my existing networks.

    It would be great if that could be as easy as installing and then importing an additional package.

    We should maybe open a feature request in PyTorch so that they integrate this into the framework.

    opened by kayuksel 12
Owner
Chen Zhu
Chen Zhu
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