tf.keras.layers.Activation
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Applies an activation function to an output.
Inherits From: Layer, Operation
tf.keras.layers.Activation(
activation, **kwargs
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
Args |
|---|
activation
|
Activation function. It could be a callable, or the name of
an activation from the keras.activations namespace.
|
**kwargs
|
Base layer keyword arguments, such as name and dtype.
|
Example:
layer = keras.layers.Activation('relu')
layer([-3.0, -1.0, 0.0, 2.0])
[0.0, 0.0, 0.0, 2.0]
layer = keras.layers.Activation(keras.activations.relu)
layer([-3.0, -1.0, 0.0, 2.0])
[0.0, 0.0, 0.0, 2.0]
Attributes |
|---|
input
|
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
output
|
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Creates a layer from its config.
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
| Args |
|---|
config
|
A Python dictionary, typically the
output of get_config.
|
| Returns |
|---|
|
A layer instance.
|
symbolic_call
View source
symbolic_call(
*args, **kwargs
)
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.layers.Activation\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/layers/activations/activation.py#L6-L39) |\n\nApplies an activation function to an output.\n\nInherits From: [`Layer`](../../../tf/keras/Layer), [`Operation`](../../../tf/keras/Operation) \n\n tf.keras.layers.Activation(\n activation, **kwargs\n )\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Mixed precision](https://www.tensorflow.org/guide/mixed_precision) - [Ragged tensors](https://www.tensorflow.org/guide/ragged_tensor) | - [Basic text classification](https://www.tensorflow.org/tutorials/keras/text_classification) - [Load text](https://www.tensorflow.org/tutorials/load_data/text) - [Classifying CIFAR-10 with XLA](https://www.tensorflow.org/xla/tf2xla/tutorials/autoclustering_xla) - [TFF simulations with accelerators](https://www.tensorflow.org/federated/tutorials/simulations_with_accelerators) - [Parametrized Quantum Circuits for Reinforcement Learning](https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning) |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|----------------------------------------------------------------------------------------------------------------------------------------------------|\n| `activation` | Activation function. It could be a callable, or the name of an activation from the [`keras.activations`](../../../tf/keras/activations) namespace. |\n| `**kwargs` | Base layer keyword arguments, such as `name` and `dtype`. |\n\n\u003cbr /\u003e\n\n#### Example:\n\n layer = keras.layers.Activation('relu')\n layer([-3.0, -1.0, 0.0, 2.0])\n [0.0, 0.0, 0.0, 2.0]\n layer = keras.layers.Activation(keras.activations.relu)\n layer([-3.0, -1.0, 0.0, 2.0])\n [0.0, 0.0, 0.0, 2.0]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | Retrieves the input tensor(s) of a symbolic operation. \u003cbr /\u003e Only returns the tensor(s) corresponding to the *first time* the operation was called. |\n| `output` | Retrieves the output tensor(s) of a layer. \u003cbr /\u003e Only returns the tensor(s) corresponding to the *first time* the operation was called. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/operation.py#L191-L213) \n\n @classmethod\n from_config(\n config\n )\n\nCreates a layer from its config.\n\nThis method is the reverse of `get_config`,\ncapable of instantiating the same layer from the config\ndictionary. It does not handle layer connectivity\n(handled by Network), nor weights (handled by `set_weights`).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|----------------------------------------------------------|\n| `config` | A Python dictionary, typically the output of get_config. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A layer instance. ||\n\n\u003cbr /\u003e\n\n### `symbolic_call`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/operation.py#L58-L70) \n\n symbolic_call(\n *args, **kwargs\n )"]]
View source on GitHub