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<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L14)</span>
### GaussianNoise
```python
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.
----
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L58)</span>
### GaussianDropout
```python
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__
- [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](
http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
----
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L106)</span>
### AlphaDropout
```python
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__
- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
|
