Python | Tensorflow tan () method

Michael Zippo 18.07.2021

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

Syntax : tf.tan (x, name = None) or tf.math.tan ( 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 tan function and  
  # save the result to & # 39; b & # 39;  
   b   =   tf.tan (a, name   =  ’ tan’  ) 
    
  # 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: T ensor ("Const: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 -2.1 0. -6.5] Return type: Tensor ("tan: 0", shape = (6, ), dtype = float32) Output: [1.5574077 -0.5463025 0.264317 1.7098469 0. -0.2202772]

Code # 2: Rendering

# Tensorflow library import

import tensorflow as tf

# Import NumPy library

import numpy as np

# Import matplotlib.pylot function

import matplotlib. pyplot as plt

# Vector p size 15 with values from -1 to 1

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

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

b = tf.tan (a, name = ’tan’ )

# Initiate a session Tensorflow
with tf.Session () as sess:

print < code class = "plain"> ( ’Input:’ , a)

print ( ’Output: ’ , sess.run (b))

plt.plot (a, sess.run (b), color = ’ red’ , marker = "o" )

plt.title ( "tensorflow .tan " )

plt. xlabel ( "X" )

plt.ylabel ( "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: [-1.55740772 -1.15486601 -0.86700822 -0.64298589 -0.45689311 -0.29375136 -0.14383696 0. 0.14383696 0.29375136 0.45689311 0.64298589 0.86700822 1.15486601 1.55740772]