Change language

# Python | Tensorflow acosh () method

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.acosh () ` [alias ` tf.math.acosh `] function provides support for the inverse hyperbolic cosine function in Tensorflow ... It expects input in the range [1, ∞) and returns nan for any input outside this range. Input type — tensor, and if the input contains more than one element, the element-wise inverse hyperbolic cosine is calculated.

Syntax : tf.acosh (x, name = None) or tf.math. acosh (x, name = None)

Parameters :
x : A tensor of any of the following types: float16, float32, float64, complex64, or complex128.
name (optional): The name for the operation.

Return type : A tensor with the same type as that of x.

Code # 1:

 ` # Library import Tensorflow ` ` import ` ` tensorflow as tf `   ` # Constant vector of size 6 ` ` a ` ` = ` ` tf.constant ([` ` 1.0 ` `, ` ` 0.5 ` `, ` ` 3.4 ` `, ` ` - ` ` 2.1 ` `, ` ` 0.0 ` `, ` ` 6.5 ` `], ` ` ` ` dtype ` ` = ` ` tf.float32) `   ` # Applying acosh and ` ` # save the result to & # 39; b & # 39; ` ` b ` ` = ` ` tf.acosh (a, name ` ` = ` ` ’acosh’ ` `) ` `   # Initiating a Tensorflow session with tf.Session () as sess: `` print ( ’Input type:’ , a) print ( ’ Input: ’ , sess.run ( a)) print ( ’Return type:’ , b)   print ( ’Output:’ , sess.run (b)) `

Output :

` Input type: Tensor ("Const: 0", shape = (6,) , dtype = float32) Input: [1. 0.5 3.4 -2.1 0. 6.5] Return type: Tensor ("acosh: 0", shape = (6,), dtype = float32) Output: [0. nan 1.894559 nan nan 2.558979] `

Code # 2: Visualization

 ` # Library import Tensorflow ` ` import ` ` tensorflow as tf `   ` # Importing the NumPy library ` ` import ` ` numpy as np `   ` # Import function matplotlib.pylot ` ` import ` ` matplotlib.pyplot as plt `   ` # Vector size 15 with values ​​from 1 to 10 ` ` a ` ` = ` ` np.linspace (` ` 1 , 10 , 15 ) ``   # Applying inverse hyperbolic cosine # function and saving the result to & # 39; b & # 39; b = tf.acosh (a, name = ’acosh’ )    # Initiating a Tensorflow session with tf.Session () as sess: print ( ’Input:’ , a) print ( ’Output:’ , sess.run (b)) plt.plot (a, sess.run (b), color = ’red’ , marker = "o" )  plt.title ( "tensorflow.acosh" )  plt.xlabel ( "X" )  plt.ylabel ( "Y" )    plt.show () `

Exit:

` Input: [1. 1.64285714 2.28571429 2.92857143 3.57142857 4.21428571 4.85714286 5.5 6.14285714 6.78571429 7.42857143 8.07142857 8.71428571 9.35714286 10.] Output: [0. 1.08055227 1.46812101 1.73714862 1.94591015 2.11724401 2.26282815 2.38952643 2.50174512 2.60249262 2.69391933 2.77761797 2.85480239 2.92641956 2.99322285] `

## Shop

Learn programming in R: courses

\$FREE

Best Python online courses for 2022

\$FREE

Best laptop for Fortnite

\$399+

Best laptop for Excel

\$

Best laptop for Solidworks

\$399+

Best laptop for Roblox

\$399+

Best computer for crypto mining

\$499+

Best laptop for Sims 4

\$

Latest questions

PythonStackOverflow

Common xlabel/ylabel for matplotlib subplots

PythonStackOverflow

Check if one list is a subset of another in Python

PythonStackOverflow

How to specify multiple return types using type-hints

PythonStackOverflow

Printing words vertically in Python

PythonStackOverflow

Python Extract words from a given string

PythonStackOverflow

Why do I get "Pickle - EOFError: Ran out of input" reading an empty file?

PythonStackOverflow

Python os.path.join () method

PythonStackOverflow

Flake8: Ignore specific warning for entire file

## Wiki

Python | How to copy data from one Excel sheet to another

Common xlabel/ylabel for matplotlib subplots

Check if one list is a subset of another in Python

How to specify multiple return types using type-hints

Printing words vertically in Python

Python Extract words from a given string

Cyclic redundancy check in Python

Finding mean, median, mode in Python without libraries

Python add suffix / add prefix to strings in a list

Why do I get "Pickle - EOFError: Ran out of input" reading an empty file?

Python - Move item to the end of the list

Python - Print list vertically