tf.keras.layers.GaussianNoise

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.

stddev Float, standard deviation of the noise distribution.
seed Integer, optional random seed to enable deterministic behavior.

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).

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@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

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symbolic_call( *args, **kwargs )