tf.keras.layers.GaussianNoise

Apply additive zero-centered Gaussian noise.

Inherits From: Layer, Module

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Compat aliases for migration

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`tf.compat.v1.keras.layers.GaussianNoise`

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).

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.