Python | Tensorflow method ()

Michael Zippo 18.07.2021

The tensorflow.math module provides support for many basic mathematical operations. The tf.cosh () [alias tf.math.cosh ] function provides support for the hyperbolic cosine function in Tensorflow. Input in radians is expected. Input type — tensor, and if the input contains more than one element, the element-wise hyperbolic cosine is calculated.

Syntax : tf.cosh (x, name = None) or tf.math.cosh (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:

       # Tensorflow library import 
   import   tensorflow as tf 
   
  # 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 Kosh function and  
  # saving the result to & # 39; b & # 39;  
   b   =   tf.cosh (a, name   =   ’cosh’  )  
   
  # 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))

Exit:

Input type: Tensor ("Const_2: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 -2.1 0. -6.5] Return type: Tensor ("cosh_1: 0", shape = (6,), dtype = float32) Output: [1.5430806 1.127626 14.998738 4.144313 1. 332.5716]

Code # 2: Rendering

# Tensorflow library import

import tensorflow as tf

# Import NumPy library

import numpy as np

# Import of the matplotlib.pylot function

import matplotlib.pyplot as plt

# Vector size 15 with values from -1 to 1

a = np.linspace ( - 1 , 1 , 15 )

# Applying the hyperbolic cosine function and
# save the result to & # 39; b & # 39;

b = tf.cosh (a, name = ’cosh’ )

# 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.cosh"  ) 
   plt.xlabel ( " X " )

plt.ylabel ( < code class = "string"> "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: [1.54308063 1.39039564 1.26613436 1.16775654 1.09325103 1.04109475 1.01022145 1. 1.01022145 1.04109475 1.09325103 1.16775654 1.26613436 1.39039564 1.54308063]