tf_agents.keras_layers.permanent_variable_rate_dropout.PermanentVariableRateDropout
Stay organized with collections
Save and categorize content based on your preferences.
Applies dropout both in training and serving, with variable dropout rate.
tf_agents.keras_layers.permanent_variable_rate_dropout.PermanentVariableRateDropout(
rate, permanent=False, **kwargs
)
Initialize this layer the same was as keras.layers.Dropout, with two notable
differences:
--The parameter rate can also be a callable.
--The extra boolean parameter permanent. If set to true, dropout will be
applied both in training and inference.
Args |
|---|
seed
|
optional integer, used to create RandomGenerator.
|
force_generator
|
boolean, default to False, whether to force the
RandomGenerator to use the code branch of tf.random.Generator.
|
rng_type
|
string, the rng type that will be passed to backend
RandomGenerator. None will allow RandomGenerator to choose
types by itself. Valid values are "stateful", "stateless",
"legacy_stateful". Defaults to None.
|
**kwargs
|
other keyword arguments that will be passed to the parent
*class
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf_agents.keras_layers.permanent_variable_rate_dropout.PermanentVariableRateDropout\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/agents/blob/v0.19.0/tf_agents/keras_layers/permanent_variable_rate_dropout.py#L25-L61) |\n\nApplies dropout both in training and serving, with variable dropout rate. \n\n tf_agents.keras_layers.permanent_variable_rate_dropout.PermanentVariableRateDropout(\n rate, permanent=False, **kwargs\n )\n\nInitialize this layer the same was as [`keras.layers.Dropout`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout), with two notable\ndifferences:\n--The parameter `rate` can also be a callable.\n--The extra boolean parameter `permanent`. If set to true, dropout will be\napplied both in training and inference.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `seed` | optional integer, used to create RandomGenerator. |\n| `force_generator` | boolean, default to False, whether to force the RandomGenerator to use the code branch of tf.random.Generator. |\n| `rng_type` | string, the rng type that will be passed to backend RandomGenerator. `None` will allow RandomGenerator to choose types by itself. Valid values are \"stateful\", \"stateless\", \"legacy_stateful\". Defaults to `None`. |\n| `**kwargs` | other keyword arguments that will be passed to the parent \\*class |\n\n\u003cbr /\u003e"]]
View source on GitHub