|TensorFlow 2.0 version||View source on GitHub|
Applies Alpha Dropout to the input.
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.
rate: float, drop probability (as with
Dropout). The multiplicative noise will have standard deviation
sqrt(rate / (1 - rate)).
seed: A Python integer to use as random seed.
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).
Arbitrary. Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Same shape as input.
__init__( rate, noise_shape=None, seed=None, **kwargs )