mxnet：具有共享掩码的多个辍学层

Question

I'd like to reproduce a recurrent neural network where each time layer is followed by a dropout layer, and these dropout layers share their masks. This structure was described in, among others, A Theoretically Grounded Application of Dropout in Recurrent Neural Networks .

As far as I understand the code, the recurrent network models implemented in MXNet do not have any dropout layers applied between time layers; the dropout parameter of functions such as lstm ( R API , Python API ) actually defines dropout on the input. Therefore I'd need to reimplement these functions from scratch.

However, the Dropout layer does not seem to take a variable that defines mask as a parameter.

Is it possible to make multiple dropout layers in different places of the computation graph, yet sharing their masks?

Answer 1

根据此处的讨论，不可能指定掩码，并且使用随机种子不会影响dropout的随机数生成器。