### GaussianNoise

```
keras.layers.noise.GaussianNoise(sigma)
```

Apply to the input an additive zero-centered Gaussian noise with
standard deviation `sigma`

. This is useful to mitigate overfitting
(you could see it as a kind of random data augmentation).
Gaussian Noise (GS) is a natural choice as corruption process
for real valued inputs.

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

**Arguments**

**sigma**: float, standard deviation of the noise distribution.

**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.

### GaussianDropout

```
keras.layers.noise.GaussianDropout(p)
```

Apply to the input an multiplicative one-centered Gaussian noise
with standard deviation `sqrt(p/(1-p))`

.

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

**Arguments**

**p**: float, drop probability (as with`Dropout`

).

**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.

**References**