# Python | Tensorflow atan() method

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

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

Parameters:
x: A tensor of any of the following types: bfloat16, half, float32, float64, int32, int64, complex64, or complex128.
name (optional): The name for the operation.

Return type: A tensor with the same type as that of x.

Code #1:

 # Importing the Tensorflow library import tensorflow as tf    # A constant vector of size 6 a = tf.constant([1.0, -0.5, 3.4, 0.2, 0.0, -2],                            dtype = tf.float32)    # Applying the atan function and # storing the result in 'b' b = tf.atan(a, name ='atan')    # 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_8:0", shape=(6, ), dtype=float32)
Input: [ 1.  -0.5  3.4  0.2  0.  -2. ]
Return type: Tensor("atan:0", shape=(6, ), dtype=float32)
Output: [ 0.7853982  -0.4636476   1.2847449   0.19739556  0.         -1.1071488 ]

Code #2: Visualization

 # Importing the Tensorflow library import tensorflow as tf    # Importing the NumPy library import numpy as np    # Importing the matplotlib.pylot function import matplotlib.pyplot as plt    # A vector of size 15 with values from -5 to 5 a = np.linspace(-5, 5, 15)    # Applying the inverse tangent function and # storing the result in 'b' b = tf.atan(a, name ='atan')    # 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.atan")      plt.xlabel("X")      plt.ylabel("Y")         plt.show()

Output:

Input: [-5.         -4.28571429 -3.57142857 -2.85714286 -2.14285714 -1.42857143
-0.71428571  0.          0.71428571  1.42857143  2.14285714  2.85714286
3.57142857  4.28571429  5.        ]
Output: [-1.37340077 -1.34156439 -1.29778762 -1.23412151 -1.13416917 -0.96007036
-0.62024949  0.          0.62024949  0.96007036  1.13416917  1.23412151
1.29778762  1.34156439  1.37340077]

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.

Improved By : sanskar27jain

Article Tags :

1

Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.