# GaussianDropout layer

[source]

`GaussianDropout`

class

```
tf.keras.layers.GaussianDropout(rate, seed=None, **kwargs)
```

Apply multiplicative 1-centered Gaussian noise.

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

**Arguments**

**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.

**Call arguments**

**inputs**: Input tensor (of any rank).
**training**: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (doing nothing).

**Input shape**

Arbitrary. Use the keyword argument `input_shape`

(tuple 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.