FeatureAlphaDropout
class torch.nn.FeatureAlphaDropout(p=0.5, inplace=False)
Randomly masks out entire channels (a channel is a feature map,
   e.g. the j-th channel of the i-th sample in the batch input is a
   tensor \text{input}[i, j]) of the input tensor). Instead of setting
   activations to zero, as in regular Dropout, the activations are set
   to the negative saturation value of the SELU activation function.
   More details can be found in the paper Self-Normalizing Neural
   Networks .
Each element will be masked independently for each sample on every
   forward call with probability "p" using samples from a Bernoulli
   distribution. The elements to be masked are randomized on every
   forward call, and scaled and shifted to maintain zero mean and unit
   variance.
Usually the input comes from "nn.AlphaDropout" modules.
As described in the paper Efficient Object Localization Using
   Convolutional Networks , if adjacent pixels within feature maps are