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
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))
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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()
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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]