Python | Tensorflow asinh() method

Namachila October 16, 2025

0 1 1 minute read

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.asinh() [alias tf.math.asinh] provides support for the inverse hyperbolic sine function in Tensorflow. The input type is tensor and if the input contains more than one element, element-wise inverse hyperbolic sine is computed.

Syntax: tf.asinh(x, name=None) or tf.math.asinh(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, 3.4, 22.1, 0.0, -6.5], 
                               dtype = tf.float32) 
    
# Applying the asinh function and 
# storing the result in 'b' 
b = tf.asinh(a, name ='asinh') 
    
# 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_1:0", shape=(6, ), dtype=float32)
Input: [ 1.  -0.5  3.4 22.1  0.  -6.5]
Return type: Tensor("asinh:0", shape=(6, ), dtype=float32)
Output: [ 0.8813736  -0.48121184  1.9378793   3.7892363   0.         -2.5708146 ]

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 -10 to 10 
a = np.linspace(-10, 10, 15) 
   
# Applying the inverse hyperbolic sine 
# function and storing the result in 'b' 
b = tf.asinh(a, name ='asinh') 
   
# 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.asinh")  
    plt.xlabel("X")  
    plt.ylabel("Y")  
   
    plt.show() 

Output:

Input: [-10.          -8.57142857  -7.14285714  -5.71428571  -4.28571429
  -2.85714286  -1.42857143   0.           1.42857143   2.85714286
   4.28571429   5.71428571   7.14285714   8.57142857  10.        ]
Output: [-2.99822295 -2.84496713 -2.66412441 -2.44368627 -2.16177575 -1.77227614
 -1.15447739  0.          1.15447739  1.77227614  2.16177575  2.44368627
  2.66412441  2.84496713  2.99822295]