Python | Tensorflow tan() method

Last Updated : 10 Jan, 2022

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.tan() [alias tf.math.tan] provides support for the tangent function in Tensorflow. It expects the input in radian form. The input type is tensor and if the input contains more than one element, element-wise tangent is computed.

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:

Python3

# 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 tan function and 
# storing the result in 'b' 
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: Tensor("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: 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 -1 to 1 
a = np.linspace(-1, 1, 15) 
  
# Applying the tangent function and 
# storing the result in 'b' 
b = tf.tan(a, name ='tan') 
  
# 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.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]