### GaussianNoise

```
keras.layers.GaussianNoise(stddev)
```

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**

**stddev**: 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.GaussianDropout(rate)
```

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))`

.

**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**

### AlphaDropout

```
keras.layers.AlphaDropout(rate, noise_shape=None, seed=None)
```

Applies Alpha Dropout to the input.

Alpha Dropout is a `Dropout`

that keeps mean and variance of inputs
to their original values, in order to ensure the self-normalizing property
even after this dropout.
Alpha Dropout fits well to Scaled Exponential Linear Units
by randomly setting activations to the negative saturation value.

**Arguments**

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

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

.**noise_shape**: A 1-D`Tensor`

of type`int32`

, representing the shape for randomly generated keep/drop flags.**seed**: A Python integer to use as random seed.

**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**