Python – tensorflow.math.xlogy()

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

xlogy() is used to compute element wise x * log(y).

Syntax: tensorflow.math.xlogy(x, y, name)

Parameters:

• x: It’s a tensor. Allowed dtypes are half, float32, float64, complex64, complex128.
• y: It’s a tensor of same dtype as x.
• name(optional): It defines the name for the operation.

Returns: It returns a tensor.

Example 1:

Python3

# importing the library
import tensorflow as tf

# Initializing the input tensor
a = tf.constant([ -5, -7, 2, 0, 7], dtype = tf.float64)
b = tf.constant([ 1, 3, 9, 4, 7], dtype = tf.float64)

# Printing the input tensor
print('a: ', a)
print('b: ', b)

# Calculating result
res = tf.math.xlogy(a, b)

# Printing the result
print('Result: ', res)

Output:

a:  tf.Tensor([-5. -7.  2.  0.  7.], shape=(5, ), dtype=float64)
b:  tf.Tensor([1. 3. 9. 4. 7.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([-0.         -7.69028602  4.39444915  0.         13.62137104], shape=(5, ), dtype=float64)

Example 2:

Python3

# importing the library
import tensorflow as tf

# Initializing the input tensor
a = tf.constant([ -5 + 2j, -7-5j, 2 + 2j, 5-3j, 7 + 6j], dtype = tf.complex128)
b = tf.constant([ 0 + 0j, 3-1j, 9 + 5j, 4-3j, -6-8j], dtype = tf.complex128)

# Printing the input tensor
print('a: ', a)
print('b: ', b)

# Calculating result
res = tf.math.xlogy(a, b)

# Printing the result
print('Result: ', res)

Output:

a:  tf.Tensor([-5.+2.j -7.-5.j  2.+2.j  5.-3.j  7.+6.j], shape=(5, ), dtype=complex128)
b:  tf.Tensor([ 0.+0.j  3.-1.j  9.+5.j  4.-3.j -6.-8.j], shape=(5, ), dtype=complex128)
Result:  tf.Tensor(
[        inf       -infj -9.6678006 -3.50420885j  3.64924209+5.6776361j
  6.11668624-8.04581928j 29.40388026-1.68457149j], shape=(5, ), dtype=complex128)