GaussianNoise
keras.layers.noise.GaussianNoise(sigma)
Apply to the input an additive zero-centred 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.
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.
Arguments
- sigma: float, standard deviation of the noise distribution.
GaussianDropout
keras.layers.noise.GaussianDropout(p)
Apply to the input an multiplicative one-centred 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). -
_References_:
-
__Dropout__: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014