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# Python | Tensorflow method ()

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] `

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