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  • 整体思路以及计算方式
  • 时间复杂度
  • 训练以及loss
  • 代码
  • 实验以及适用场景
  • 细节
  • 简评
  1. Normalize_And_Residual

ReZero is All You Need Fast Convergence at Large Depth

PreviousNormalize_And_ResidualNextBatch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks

Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/2003.04887

整体思路以及计算方式

作者提出了一种使得深度网络更容易训练的方式，比较新颖的是，该方法没有使用normalize。

该方法非常简单， 作为对比，给出常见一些常见的normalize方式：

  • Deep Network: xi+1=F(xi)\mathbf {x}_{i+1}=F\left(\mathbf {x}_{i}\right)xi+1​=F(xi​)

  • Residual Network: xi+1=xi+F(xi)\mathbf {x}_{i+1}=\mathbf {x}_{i}+F\left(\mathbf {x}_{i}\right)xi+1​=xi​+F(xi​)

  • Deep Network + Norm: xi+1=Norm⁡(F(xi))\mathbf {x}_{i+1}=\operatorname{Norm}\left(F\left(\mathbf {x}_{i}\right)\right)xi+1​=Norm(F(xi​))

  • Residual Network + Pre-Norm: xi+1=xi+F(Norm⁡(xi))\mathbf {x}_{i+1}=\mathbf {x}_{i}+F\left(\operatorname{Norm}\left(\mathbf {x}_{i}\right)\right)xi+1​=xi​+F(Norm(xi​))

  • Residual Network + Post-Norm: xi+1=Norm⁡(xi+F(xi))\mathbf {x}_{i+1}=\operatorname{Norm}\left(\mathbf {x}_{i}+F\left(\mathbf {x}_{i}\right)\right)xi+1​=Norm(xi​+F(xi​))

  • ReZero: xi+1=xi+αiF(xi)\mathbf {x}_{i+1}=\mathbf {x}_{i}+\alpha_{i} F\left(\mathbf {x}_{i}\right)xi+1​=xi​+αi​F(xi​)

注意αi\alpha_iαi​需要初始化为0。

时间复杂度

不考虑。

训练以及loss

不变。

代码

  • https://github.com/majumderb/rezero

实验以及适用场景

适用于所有场景；从实验中可以看出确实提升了网络的收敛速度。

细节

主要就是初始化为0。

简评

第一印象会感觉该方法不会work，但是结果非常反直觉，由于该方法应该会提升不少速度，所以非常值得复现。