Skip to content
Related Articles

Related Articles

Improve Article

Python | Tensorflow acosh() method

  • Last Updated : 10 Dec, 2018

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.pylot 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]

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course




My Personal Notes arrow_drop_up
Recommended Articles
Page :