AlphaDropout
class torch.nn.AlphaDropout(p=0.5, inplace=False)
Applies Alpha Dropout over the input.
Alpha Dropout is a type of Dropout that maintains the self-
   normalizing property. For an input with zero mean and unit standard
   deviation, the output of Alpha Dropout maintains the original mean
   and standard deviation of the input. Alpha Dropout goes hand-in-
   hand with SELU activation function, which ensures that the outputs
   have zero mean and unit standard deviation.
During training, it randomly masks some of the elements of the
   input tensor with probability p using samples from a bernoulli
   distribution. The elements to masked are randomized on every
   forward call, and scaled and shifted to maintain zero mean and unit
   standard deviation.
During evaluation the module simply computes an identity function.
More details can be found in the paper Self-Normalizing Neural
   Networks .
Parameters: