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Apply additive zero-centered Gaussian noise.
Inherits From: Layer
, Operation
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). |
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)