layer_alpha_dropout: Applies Alpha Dropout to the input.
In dfalbel/keras: R Interface to 'Keras'

Description Usage Arguments Details Input shape Output shape References See Also

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

`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.

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

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.