The tensorflow.math
module provides support for many basic mathematical operations. The tf.log1p ()
[alias tf.math.log1p
] provides support for the natural logarithmic function in Tensorflow. Expected to be input as complex numbers in the form
or floating point numbers. Input type — tensor, and if the input contains more than one element, the element-wise logarithm
is calculated,
,
Syntax : tf.log1p (x, name = None) or tf.math.log1p (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 ([ - 1.5 , - 1 , - 0.5 , 0 , 0.5 , 1 , 1.5 ], dtype = tf.float32) # Using the log1p function and # save the result to & # 39; b & # 39; b = tf.log1p (a, name = ’log1p’ ) # 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 = (7,), dtype = float32) Input: [-1.5 -1. -0.5 0. 0.5 1. 1.5] Return type: Tensor ("log1p: 0", shape = (7,), dtype = float32) Output: [nan -inf -0.6931472 0. 0.4054651 0.6931472 0.91629076]
indicates that the natural logarithm 1 + x does not exist for negative values and
indicates that it approaches negative infinity when the input approaches -1.
Code # 2: Render
# Import Tensorflow library import tensorflow as tf # Import NumPy library import numpy as np # Import the matplotlib.pylot function import matplotlib.pyplot as plt # Vector size 20 with values from -1 to 0 and from 0 to 10 a = np.append (np.linspace ( - 1 , 0 , 10 ), np.linspace ( 0 , 10 , 10 )) # Applying a logarithmic function and # preservation result in & # 39; b & # 39; b = tf.log1p (a, name = ’log1p’ ) # 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 () |
Output :
Input: [-1. -0.88888889 -0.77777778 -0.66666667 -0.55555556 -0.44444444 -0.33333333 -0.22222222 -0.11111111 0. 0. 1.11111111 2.22222222 3.33333333 4.44444444 5.55555556 6.66666667 7.77777778 8.88888889 10.] Output: [-inf -2.19722458 -1.5040774 -1.09861229 -0.81093022 -0.58778666 -0.40546511 -0.25131443 -0.11778304 0. 0. 0.7472144 1.17007125 1.46633707 1.69459572 1.88031287 2.03688193 2.17222328 2.29141179 2.39789527]
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