tf.keras.layers.GaussianNoise
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.
|
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Last updated 2022-10-27 UTC.
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