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:

Python3

# 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 

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 -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]