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GaussianNoise

keras.layers.GaussianNoise(stddev)

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 processfor 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.

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.GaussianDropout(rate)

Apply multiplicative 1-centered Gaussian noise.

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

Arguments

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

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

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AlphaDropout

keras.layers.AlphaDropout(rate, noise_shape=None, seed=None)

Applies Alpha Dropout to the input.

Alpha Dropout is a Dropout that keeps mean and variance of inputsto their original values, in order to ensure the self-normalizing propertyeven after this dropout.Alpha Dropout fits well to Scaled Exponential Linear Unitsby randomly setting activations to the negative saturation value.

Arguments

• rate: float, drop probability (as with Dropout). The multiplicative noise will have standard deviation sqrt(rate / (1 - rate)).
• noise_shape: A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.
• seed: A Python integer to use as random seed.

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

• Self-Normalizing Neural Networks