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.

`layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)`

- object
What to compose the new

`Layer`

instance with. Typically a Sequential model or a Tensor (e.g., as returned by`layer_input()`

). The return value depends on`object`

. If`object`

is:missing or

`NULL`

, the`Layer`

instance is returned.a

`Sequential`

model, the model with an additional layer is returned.a Tensor, the output tensor from

`layer_instance(object)`

is returned.

- rate
float, drop probability (as with

`layer_dropout()`

). The multiplicative noise will have standard deviation`sqrt(rate / (1 - rate))`

.- noise_shape
Noise shape

- seed
An integer to use as random seed.

- ...
standard layer arguments.

Arbitrary. Use the keyword argument `input_shape`

(list
of integers, does not include the samples axis) when using this layer as
the first layer in a model.

Same shape as input.

Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.

https://www.tensorflow.org/api_docs/python/tf/keras/layers/AlphaDropout

Other noise layers:
`layer_gaussian_dropout()`

,
`layer_gaussian_noise()`