Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer, it is only active at training time.
rate: Float, drop probability (as with
Dropout). The multiplicative noise will have standard deviation
sqrt(rate / (1 - rate)).
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).
Arbitrary. Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Same shape as input.
__init__( rate, **kwargs )