function alphaDropout(args) {
	  return new AlphaDropout(args);
	}
Applies Alpha Dropout to the input.

As it is a regularization layer, it is only active at training time.

Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
to their original values, in order to ensure the self-normalizing property
even after this dropout.
Alpha Dropout fits well to Scaled Exponential Linear Units
by randomly setting activations to the negative saturation value.

Arguments:
  - `rate`: float, drop probability (as with `Dropout`).
    The multiplicative noise will have
    standard deviation `sqrt(rate / (1 - rate))`.
  - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the
    shape for randomly generated keep/drop flags.

Input shape:
  Arbitrary. Use the keyword argument `inputShape`
  (tuple of integers, does not include the samples axis)
  when using this layer as the first layer in a model.

Output shape:
  Same shape as input.

References:
  - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)

@doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}