# Python | Tensorflow log () method

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.log () ` [alias ` tf.math.log `] function provides support for the natural logarithmic function in Tensorflow. Expected to be input as complex numbers in the form or floating point. Input type — tensor, and if the input contains more than one element, the element-wise logarithm is calculated, ,

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:

 ` # Import Tensorflow library ` ` import ` ` tensorflow as tf `   ` # Constant vector of size 5 ` ` a = tf.constant ([ - 0.5 , - 0.1 , 0 , 0.1 , 0.5 ], dtype = tf.float32) ``   # Using the log function and # saving the result to & # 39; b & # 39; b = tf.log (a, name = ’log’ )    # Initiating a Tensorfl session ow with tf.Session () as sess: print ( ’Input type:’ , a) print ( ’Input:’ , sess.run (a)) print ( ’Return type:’ , b) print ( ’Output:’ , sess.run (b)) `

Exit:

` 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] ` means that natural logarithm does not exist for negative values ​​and means that it is approaching negative infinity when the input approaches zero.

Code # 2: Rendering

` `

` # Import Tensorflow library import tensorflow as tf   # Importing the NumPy library import numpy as np   # Import ma function tplotlib.pylot import matplotlib.pyplot as plt   # Vector size 20 with values ​​0 to 1 and 1 to 10 a = np.append (np.linspace ( 0 , 1 , 10 ), np.linspace ( 1 , 10 , 10 ))   # Applying a logarithmic function and # save the result to & # 39; b & # 39; b = tf.log (a, name = ’log’ )    # Initiate 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 () `

` `

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 1.609437 91 1.79175947 1.94591015 2.07944154 2.19722458 2.30258509] ` ## Shop Learn programming in R: courses

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