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Apply multiplicative 1-centered Gaussian noise.

Inherits From: Layer

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)).

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.