Python | Tensorflow atanh() method

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:

# 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

# Importing the Tensorflow library 
import tensorflow as tf 
   
# Importing the NumPy library 
import numpy as np 
   
# Importing the matplotlib.pylot 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]

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