TensorFlow 1 version
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View source on GitHub
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Applies Alpha Dropout to the input.
tf.keras.layers.AlphaDropout(
rate, noise_shape=None, seed=None, **kwargs
)
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
by randomly setting activations to the negative saturation value.
Args | |
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rate
|
float, drop probability (as with Dropout).
The multiplicative noise will have
standard deviation sqrt(rate / (1 - rate)).
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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 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.