  # Python | Tensorflow atanh () method

NumPy | Python Methods and Functions

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.atanh () ` [alias ` tf.math.atanh `] function provides support for the inverse hyperbolic tangent function in Tensorflow ... Its domain is in the range [-1, 1] and it returns nan for any entry outside this range. Input type — tensor, and if the input contains more than one element, the elementwise inverse hyperbolic tangent is calculated.

Syntax : tf.atanh (x, name = None) or tf.math. atanh (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 ` `, ` ` - ` ` 1 ` `, ` ` 2.4 ` `, ` ` 0.0 ` `, ` ` - ` ` 6.5 ` `], dtype ` ` = ` ` tf.float32) `   ` # Using the atanh function and ` ` # saving the result to & # 39; b & # 39; ` ` b ` ` = ` ` tf.atanh (a, name ` ` = ` ` `atanh` ` `) `   < code class = "comments"> # 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)) `

Exit:

` Input type: Tensor ("Const_3: 0", shape = (6,), dtype = f loat32) Input: [1. -0.5 -1. 2.4 0. -6.5] Return type: Tensor ("atanh_1: 0", shape = (6,), dtype = float32) Output: [inf -0.54930615 -inf nan 0. nan] `

Code # 2: Rendering

 ` # Import Tensorflow library ` ` import ` ` tensorflow as tf `   ` # Import NumPy library ` ` import ` ` numpy as np `   ` # Import matplotlib.pylot function ` ` import ` ` matplotlib.pyplot as plt `   ` # Vector size 15 with values ​​from -1 to 1 ` ` a ` ` = ` ` np.linspace (` ` - ` ` 1 ` `, ` ` 1 ` `, ` ` 15 ` `) `   ` # Applying inverse hyperbolic tangent ` ` # function and saving the result to & # 39; b & # 39; ` ` b ` ` = ` ` tf.atanh (a, name ` ` = ` ` `atanh` ` `) ` ` `  ` # 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.atanh" )  `` plt.xlabel ( "X" ) `` plt.ylabel ( "Y" )       plt.show () `

Output :

` Input: [-1. -0.85714286 -0.71428571 -0.57142857 -0.42857143 -0.28571429 -0.14285714 0. 0.14285714 0.28571429 0.42857143 0.57142857 0.71428571 0.85714286 1.] Output: [-inf -1.28247468 -0.89587973 -0.64964149 -0.45814537 -0.29389333 -0.14384104 0. 0.14384104 0.29389333 0.45814537 0.64964149 0.89587973 1.28247468 inf] `