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Python | Tensorflow tan() method

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  • 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]


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