tf.keras.layers.AlphaDropout
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Applies Alpha Dropout to the input.
Inherits From: Layer, Module
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.layers.AlphaDropout`
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 |
|---|
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).
|
|
|---|
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.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.layers.AlphaDropout\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/regularization/alpha_dropout.py#L28-L104) |\n\nApplies Alpha Dropout to the input.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer), [`Module`](../../../tf/Module)\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n\\`tf.compat.v1.keras.layers.AlphaDropout\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.AlphaDropout(\n rate, noise_shape=None, seed=None, **kwargs\n )\n\nAlpha Dropout is a `Dropout` that keeps mean and variance of inputs\nto their original values, in order to ensure the self-normalizing property\neven after this dropout.\nAlpha Dropout fits well to Scaled Exponential Linear Units\nby randomly setting activations to the negative saturation value.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|-------------------------------------------------------------------------------------------------------------------------------|\n| `rate` | float, drop probability (as with `Dropout`). The multiplicative noise will have standard deviation `sqrt(rate / (1 - rate))`. |\n| `seed` | Integer, optional random seed to enable deterministic behavior. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Call arguments -------------- ||\n|------------|-----------------------------------------------------------------------------------------------------------------------------------|\n| `inputs` | Input tensor (of any rank). |\n| `training` | Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Input shape ----------- ||\n|---|---|\n| 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. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Output shape ------------ ||\n|---|---|\n| Same shape as input. ||\n\n\u003cbr /\u003e"]]
View source on GitHub