layer_alpha_dropout {keras}

Applies Alpha Dropout to the input.

Description

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.

Usage

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

Arguments

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.

Details

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

Input shape

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.

Output shape

Same shape as input.

References

• Self-Normalizing Neural Networks

See Also

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

Other noise layers: layer_gaussian_dropout(), layer_gaussian_noise()