Layer Normalization

Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. "Layer normalization." arXiv preprint arXiv:1607.06450 (2016).

Batch Normalization是对每个神经元做归一化(cnn是对每个feature map做归一化)，主要是为了解决internal covariate shift的问题。

作者提出，对于RNN这种没法用mini-batch的网络，没办法用BN，所以提出了Layer Normalization。

公式为：

两个repo:https://github.com/pbhatia243/tf-layer-norm 和 https://github.com/ryankiros/layer-norm

感觉不管BN还是LN，都是为了限制神经元的输出值，使其符合一定的分布。可以看出，为了得到均值和方差，BN采用了不同batch中不同的输入数据来计，LN采用了同一层上神经元来计算。那么，是否可以通过历史时间数据来计算呢，感兴趣的同学可以自己去实现一个TN(time normalization或者叫temporal normalization )，似乎也是支持RNN的(即独立于mini-batch的)。

时间： 2024-10-25 22:33:17

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