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

## Arguments

Arguments | Description |
---|---|

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))` . |

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. |

## Section

## 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

## See Also

Other noise layers: `layer_alpha_dropout()`

, `layer_gaussian_noise()`