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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).