As it is a regularization layer, it is only active at training time.
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.
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.
Apply additive zero-centered Gaussian noise.
As it is a regularization layer, it is only active at training time.
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.
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.