tf.keras.activations.softmax
Stay organized with collections
Save and categorize content based on your preferences.
Softmax converts a real vector to a vector of categorical probabilities.
tf.keras.activations.softmax(
x, axis=-1
)
The the elements of the output vector are in range (0, 1) and sum to 1.
Each vector is handled independently. The axis argument sets which axis
of the input the finction is applied along.
Softmax is often used as the activation for the last
layer of a classification network because the result could be interpreted as
a probability distribution.
The softmax of each vector x is calculated by exp(x)/tf.reduce_sum(exp(x)).
The input values in are the log-odds of the resulting probability.
Arguments |
|---|
x
|
Input tensor.
|
axis
|
Integer, axis along which the softmax normalization is applied.
|
Returns |
|---|
|
Tensor, output of softmax transformation (all values are non-negative
and sum to 1).
|
Raises |
|---|
ValueError
|
In case dim(x) == 1.
|
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 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.activations.softmax\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/activations/softmax) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.1.0/tensorflow/python/keras/activations.py#L43-L79) |\n\nSoftmax converts a real vector to a vector of categorical probabilities.\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.activations.softmax`](/api_docs/python/tf/keras/activations/softmax)\n\n\u003cbr /\u003e\n\n tf.keras.activations.softmax(\n x, axis=-1\n )\n\nThe the elements of the output vector are in range (0, 1) and sum to 1.\n\nEach vector is handled independently. The `axis` argument sets which axis\nof the input the finction is applied along.\n\nSoftmax is often used as the activation for the last\nlayer of a classification network because the result could be interpreted as\na probability distribution.\n\nThe softmax of each vector x is calculated by `exp(x)/tf.reduce_sum(exp(x))`.\nThe input values in are the log-odds of the resulting probability.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|--------|-----------------------------------------------------------------|\n| `x` | Input tensor. |\n| `axis` | Integer, axis along which the softmax normalization is applied. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Tensor, output of softmax transformation (all values are non-negative and sum to 1). ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|------------------------|\n| `ValueError` | In case `dim(x) == 1`. |\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub