Python | Tensorflow exp() 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.exp() [alias tf.math.exp] provides support for the exponential function in Tensorflow. It expects the input in form of complex numbers as $a+bi$  or floating point numbers. The input type is tensor and if the input contains more than one element, an element-wise exponential value is computed, y=e^x$  .

Syntax: tf.exp(x, name=None) or tf.math.exp(x, name=None)
Parameters: 
x: A Tensor of type bfloat16, half, float32, float64, complex64 or complex128. 
name (optional): The name for the operation.
Return type: A Tensor with the same size and type as that of x. 
 

Code #1: 

# Importing the Tensorflow library
import tensorflow as tf

# A constant vector of size 5
a = tf.constant([-0.5, -0.1, 0, 0.1, 0.5], dtype = tf.float32)

# Applying the exp function and
# storing the result in 'b'
b = tf.exp(a, name ='exp')

# 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=(5, ), dtype=float32)
Input: [-0.5 -0.1  0.   0.1  0.5]
Return type: Tensor("exp:0", shape=(5, ), dtype=float32)
Output: [0.60653067 0.9048374  1.         1.105171   1.6487212 ]

Code #2: Visualization 

# 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 21 with values from -10 to 10
a = np.linspace(-10, 10, 21)

# Applying the exponential function and
# storing the result in 'b'
b = tf.exp(a, name ='exp')

# 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.abs") 
    plt.xlabel("X") 
    plt.ylabel("Y") 

    plt.show()

Output: 
 

Input: [-10.  -9.  -8.  -7.  -6.  -5.  -4.  -3.  -2.  -1.   0.   1.   2.   3.
   4.   5.   6.   7.   8.   9.  10.]
Output: [4.53999298e-05 1.23409804e-04 3.35462628e-04 9.11881966e-04
 2.47875218e-03 6.73794700e-03 1.83156389e-02 4.97870684e-02
 1.35335283e-01 3.67879441e-01 1.00000000e+00 2.71828183e+00
 7.38905610e+00 2.00855369e+01 5.45981500e+01 1.48413159e+02
 4.03428793e+02 1.09663316e+03 2.98095799e+03 8.10308393e+03
 2.20264658e+04]