# Python | Tensorflow atanh() method

• Last Updated : 06 Jan, 2022

Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks.
The module tensorflow.math provides support for many basic mathematical operations. Function tf.atanh() [alias tf.math.atanh] provides support for the inverse hyperbolic tangent function in Tensorflow. Its domain is in the range [-1, 1] and it returns nan for any input outside this range. The input type is tensor and if the input contains more than one element, element-wise inverse hyperbolic tangent is computed.

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:

## Python3

 `# Importing the Tensorflow library``import` `tensorflow as tf``  ` `# A constant vector of size 6``a ``=` `tf.constant([``1.0``, ``-``0.5``, ``-``1``, ``2.4``, ``0.0``, ``-``6.5``], dtype ``=` `tf.float32)``  ` `# Applying the atanh function and``# storing the result in 'b'``b ``=` `tf.atanh(a, name ``=``'atanh'``)``  ` `# 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_3:0", shape=(6, ), dtype=float32)
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: Visualization

## Python3

 `# Importing the Tensorflow library``import` `tensorflow as tf`` ` `# Importing the NumPy library``import` `numpy as np`` ` `# Importing the matplotlib.pyplot function``import` `matplotlib.pyplot as plt`` ` `# A vector of size 15 with values from -1 to 1``a ``=` `np.linspace(``-``1``, ``1``, ``15``)`` ` `# Applying the inverse hyperbolic tangent``# function and storing the result in 'b'``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]```

My Personal Notes arrow_drop_up