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)

## 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.

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

## See also