GaussianNoise
keras.layers.noise.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 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.
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.
GaussianDropout
keras.layers.noise.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 deviationsqrt(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