Function gaussianDropout

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)).

Input shape: Arbitrary. Use the keyword argument inputShape (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.

References:

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting

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