Python | Tensorflow log() 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.log() [alias tf.math.log] provides support for the natural logarithmic 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 logarithm is computed,  y=\log_e x}$ .

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

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# 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 log function and
# storing the result in 'b'
b = tf.log(a, name ='log')
  
# 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("log:0", shape=(5, ), dtype=float32)
Output: [       nan        nan       -inf -2.3025851 -0.6931472]

 

 $ nan $ denotes that natural logarithm dosen’t exist for negative values and  $ -inf $ denotes that it approaches to negative infinity as the input approaches zero.

Code #2: Visualization

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# 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 20 with values from 0 to 1 and 1 to 10
a = np.append(np.linspace(0, 1, 10), np.linspace(1, 10, 10))
  
# Applying the logarithmic function and
# storing the result in 'b'
b = tf.log(a, name ='log')
  
# 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.grid()
  
    plt.show()

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Output:

Input: [ 0.          0.11111111  0.22222222  0.33333333  0.44444444  0.55555556
  0.66666667  0.77777778  0.88888889  1.          1.          2.
  3.          4.          5.          6.          7.          8.
  9.         10.        ]
Output: [       -inf -2.19722458 -1.5040774  -1.09861229 -0.81093022 -0.58778666
 -0.40546511 -0.25131443 -0.11778304  0.          0.          0.69314718
  1.09861229  1.38629436  1.60943791  1.79175947  1.94591015  2.07944154
  2.19722458  2.30258509]



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