GaussianNoise layer

• Original Link : https://keras.io/api/layers/regularization_layers/gaussian_noise/
• Last Checked at : 2024-11-25

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GaussianNoise class

keras.layers.GaussianNoise(stddev, seed=None, **kwargs)

Apply additive zero-centered Gaussian noise.

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.

Arguments

• 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).