GaussianDropout layer

[source]

GaussianDropout class

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 deviation sqrt(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).