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.

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).

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.

Same shape as input.