keras: layer_alpha_dropout – R documentation

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layer_alpha_dropout

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,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

`object`	Model or layer object
`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.
`input_shape`	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
`batch_input_shape`	Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors.
`batch_size`	Fixed batch size for layer
`dtype`	The data type expected by the input, as a string (`float32`, `float64`, `int32`...)
`name`	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
`trainable`	Whether the layer weights will be updated during training.
`weights`	Initial weights for layer.

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