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 
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, 
.
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:
Python3
# Importing the Tensorflow libraryimport tensorflow as tf# A constant vector of size 5a = 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 sessionwith 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
Python3
# Importing the Tensorflow libraryimport tensorflow as tf# Importing the NumPy libraryimport numpy as np# Importing the matplotlib.pyplot functionimport matplotlib.pyplot as plt# A vector of size 21 with values from -10 to 10a = np.linspace(-10, 10, 21)# Applying the exponential function and# storing the result in 'b'b = tf.exp(a, name ='exp')# Initiating a Tensorflow sessionwith 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]
