tf.keras.layers.Softmax
Stay organized with collections
Save and categorize content based on your preferences.
Softmax activation layer.
Inherits From: Layer, Operation
tf.keras.layers.Softmax(
axis=-1, **kwargs
)
Used in the notebooks
exp_x = exp(x - max(x))
f(x) = exp_x / sum(exp_x)
Example:
oftmax_layer = keras.layers.activations.Softmax()
nput = np.array([1.0, 2.0, 1.0])
esult = softmax_layer(input)
[0.21194157, 0.5761169, 0.21194157]
Args |
|---|
axis
|
Integer, or list of Integers, axis along which the softmax
normalization is applied.
|
**kwargs
|
Base layer keyword arguments, such as name and dtype.
|
Call arguments |
|---|
inputs
|
The inputs (logits) to the softmax layer.
|
mask
|
A boolean mask of the same shape as inputs. The mask
specifies 1 to keep and 0 to mask. Defaults to None.
|
Returns |
|---|
Softmaxed output with the same shape as inputs.
|
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
)
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. Some content is licensed under the numpy license.
Last updated 2024-06-07 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.layers.Softmax\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/softmax.py#L14-L75) |\n\nSoftmax activation layer.\n\nInherits From: [`Layer`](../../../tf/keras/Layer), [`Operation`](../../../tf/keras/Operation) \n\n tf.keras.layers.Softmax(\n axis=-1, **kwargs\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Basic classification: Classify images of clothing](https://www.tensorflow.org/tutorials/keras/classification) - [TensorFlow 2 quickstart for beginners](https://www.tensorflow.org/tutorials/quickstart/beginner) - [Building Your Own Federated Learning Algorithm](https://www.tensorflow.org/federated/tutorials/building_your_own_federated_learning_algorithm) - [Composing Learning Algorithms](https://www.tensorflow.org/federated/tutorials/composing_learning_algorithms) - [Federated Learning for Image Classification](https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification) |\n\n#### Formula:\n\n exp_x = exp(x - max(x))\n f(x) = exp_x / sum(exp_x)\n\n#### Example:\n\n oftmax_layer = keras.layers.activations.Softmax()\n nput = np.array([1.0, 2.0, 1.0])\n esult = softmax_layer(input)\n [0.21194157, 0.5761169, 0.21194157]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|--------------------------------------------------------------------------------------|\n| `axis` | Integer, or list of Integers, axis along which the softmax normalization is applied. |\n| `**kwargs` | Base layer keyword arguments, such as `name` and `dtype`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Call arguments -------------- ||\n|----------|---------------------------------------------------------------------------------------------------------------|\n| `inputs` | The inputs (logits) to the softmax layer. |\n| `mask` | A boolean mask of the same shape as `inputs`. The mask specifies 1 to keep and 0 to mask. Defaults to `None`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Softmaxed output with the same shape as `inputs`. ||\n\n\u003cbr /\u003e\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