Python | Tensorflow acosh() 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.acosh() [alias tf.math.acosh] provides support for the inverse hyperbolic cosine function in Tensorflow. It expects the input in the range [1, ∞) and 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 cosine is computed.
 

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: 
 

# Importing the Tensorflow library
import tensorflow as tf
 
# A constant vector of size 6
a = tf.constant([1.0, 0.5, 3.4, -2.1, 0.0, 6.5],
                             dtype = tf.float32)
 
# Applying the acosh function and
# storing the result in 'b'
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 
 

# 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 10
a = np.linspace(1, 10, 15)
 
# Applying the inverse hyperbolic cosine
# function and storing the result in 'b'
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()

Output: 
 

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]

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