tf.keras.layers.AlphaDropout
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Applies Alpha Dropout to the input.
Inherits From: Layer
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.
Arguments |
|---|
rate
|
float, drop probability (as with Dropout).
The multiplicative noise will have
standard deviation sqrt(rate / (1 - rate)).
|
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).
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 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.AlphaDropout\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/keras/layers/AlphaDropout) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/noise.py#L134-L206) |\n\nApplies Alpha Dropout to the input.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer)\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`](/api_docs/python/tf/keras/layers/AlphaDropout), \\`tf.compat.v2.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| Arguments --------- ||\n|--------|-------------------------------------------------------------------------------------------------------------------------------|\n| `rate` | float, drop probability (as with `Dropout`). The multiplicative noise will have standard deviation `sqrt(rate / (1 - rate))`. |\n| `seed` | A Python integer to use as random seed. |\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#### Input shape:\n\nArbitrary. Use the keyword argument `input_shape`\n(tuple of integers, does not include the samples axis)\nwhen using this layer as the first layer in a model.\n\n#### Output shape:\n\nSame shape as input."]]
TensorFlow 2 version
View source on GitHub