Backpropagation of dropout wrapper does not apply the mask

[TzuChieh]

Hi, I recently discovered this excellent repository for learning basic concepts in ML and noticed a potential problem in the implementation of dropout wrapper. In particular, this is the line of code I am confused about:

numpy-ml/numpy_ml/neural_nets/wrappers/wrappers.py

Line 231 in b0359af

dLdy *= 1.0 / (1.0 - self.p)

Shouldn't the gradient from a later layer also apply the mask from the one used in forward()? Otherwise, dLdy will overestimate the true gradient after dividing by the probability 1.0 - self.p. Not sure this is an issue though as I am a beginner in ML.