# layer_gaussian_dropout: Apply multiplicative 1-centered Gaussian noise.

## Description

As it is a regularization layer, it is only active at training time.

## Usage

layer_gaussian_dropout(object, rate, 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 `Dropout`

). The multiplicative
noise will have standard deviation `sqrt(rate / (1 - rate))`

.

- seed
Integer, optional random seed to enable deterministic behavior.

- ...
standard layer arguments.

## 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.

## See Also

Other noise layers:
`layer_alpha_dropout()`

,
`layer_gaussian_noise()`