### GaussianNoise

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

Apply additive zero-centered Gaussian noise.

This is useful to mitigate overfitting (you could see it as a form 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 multiplicative 1-centered Gaussian noise.

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

**Arguments**

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

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

.

**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**