GaussianDropout layer
GaussianDropout class
tf_keras.layers.GaussianDropout(rate, seed=None, **kwargs)
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)). - seed: Integer, optional random seed to enable deterministic behavior.
Call arguments
- 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).
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.