tf.keras.activations.exponential
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Exponential activation function.
tf.keras.activations.exponential(
x
)
For example:
a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
b = tf.keras.activations.exponential(a)
b.numpy()
array([ 0.04978707, 0.36787945, 1. , 2.7182817 , 20.085537 ],
dtype=float32)
Arguments |
|---|
x
|
Input tensor.
|
Returns |
|---|
Tensor with exponential activation: exp(x). Tensor will be of same
shape and dtype of input x.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.activations.exponential\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/activations/exponential) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.1.0/tensorflow/python/keras/activations.py#L276-L295) |\n\nExponential activation function.\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.exponential`](/api_docs/python/tf/keras/activations/exponential)\n\n\u003cbr /\u003e\n\n tf.keras.activations.exponential(\n x\n )\n\n#### For example:\n\n a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)\n b = tf.keras.activations.exponential(a)\n b.numpy()\n array([ 0.04978707, 0.36787945, 1. , 2.7182817 , 20.085537 ],\n dtype=float32)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|-----|---------------|\n| `x` | Input tensor. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Tensor with exponential activation: `exp(x)`. Tensor will be of same shape and dtype of input `x`. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub