AlphaDropout layer
AlphaDropout class
keras.layers.AlphaDropout(rate, noise_shape=None, seed=None, **kwargs)
Applies Alpha Dropout to the input.
Alpha Dropout is a Dropout that keeps mean and variance of inputs
to their original values, in order to ensure the self-normalizing property
even after this dropout.
Alpha Dropout fits well to Scaled Exponential Linear Units (SELU) by
randomly setting activations to the negative saturation value.
Arguments
- rate: Float between 0 and 1. The multiplicative noise will have
standard deviation
sqrt(rate / (1 - rate)). - noise_shape: 1D integer tensor representing the shape of the
binary alpha dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
(batch_size, timesteps, features)and you want the alpha dropout mask to be the same for all timesteps, you can usenoise_shape=(batch_size, 1, features). - seed: A Python integer to use as random seed.
Call arguments
- inputs: Input tensor (of any rank).
- training: Python boolean indicating whether the layer should behave in training mode (adding alpha dropout) or in inference mode (doing nothing).