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GaussianNoise

keras.layers.noise.GaussianNoise(sigma)

Apply to the input an additive zero-centered Gaussian noise with standard deviation sigma. This is useful to mitigate overfitting (you could see it as a kind 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

  • sigma: float, standard deviation of the noise distribution.

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.


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GaussianDropout

keras.layers.noise.GaussianDropout(p)

Apply to the input an multiplicative one-centered Gaussian noise with standard deviation sqrt(p/(1-p)).

As it is a regularization layer, it is only active at training time.

Arguments

  • p: float, drop probability (as with Dropout).

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.

References