Apply additive zero-centered Gaussian noise.
Inherits From: Layer, Module
tf.keras.layers.GaussianNoise(
stddev, seed=None, **kwargs
)
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.
Args |
|---|
stddev
|
Float, standard deviation of the noise distribution.
|
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 noise) or in inference mode (doing nothing).
|
|
|---|
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.
|
View source on GitHub