Transformer-Evolution-Paper
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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

Transformers without Tears: Improving the Normalization of Self-Attention

PreviousOn Layer Normalizations and Residual Connections in TransformersNextQuery-Key Normalization for Transformers

Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/1910.05895

整体思路以及计算方式

对layernorm的改进：

ScaleNorm⁡(x;g)=gx∥x∥\operatorname{ScaleNorm}(\mathbf{x} ; g)=g \frac{\mathbf{x}}{\|\mathbf{x}\|}ScaleNorm(x;g)=g∥x∥x​

时间复杂度

不考虑。

训练以及loss

不变。

代码

  • https://github.com/tnq177/transformers_without_tears

实验以及适用场景

适用于所有场景，作者测试了机器翻译，获得了一定的提升。

细节

暂无。

简评

值得实现。