[source]

GaussianNoise

keras.layers.noise.GaussianNoise(sigma)

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

• 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.

---

[source]

GaussianDropout

keras.layers.noise.GaussianDropout(p)

Apply multiplicative 1-centered Gaussian noise.

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

Arguments

• p: float, drop probability (as with Dropout). The multiplicative noise will have standard deviation sqrt(p / (1 - p)).

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

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014