layer_alpha_dropout {keras} | R Documentation

## 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

### See Also

Other noise layers: `layer_gaussian_dropout`, `layer_gaussian_noise`

[Package keras version 2.2.4 Index]