tensorflow.math.special.spence() function in Python

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

special_spence() method

special_spence() is used to compute element wise Spence’s integral of x. It is defined as the integral of log(t) / (1 – t) from 1 to x, with the domain of definition all positive real numbers.

Syntax: tensorflow.math.special.spence( x, name)

Parameter:

• x: It’s a Tensor or Sparse Tensor. Allowed dtypes are float32 and float64.
• name(optional): It defines name for the operation.

Returns: It returns a Tensor of same dtype as x.

Example 1:

Python3

# importing the library
import tensorflow as tf

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

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

# Calculating result
res = tf.math.special.spence(a)

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

Output:

a:  tf.Tensor(
[[-5. -7.]
 [ 2.  0.]], shape=(2, 2), dtype=float64)
Result:  tf.Tensor(
[[        nan         nan]
 [-0.82246703  1.64493407]], shape=(2, 2), dtype=float64)

Example 2:

Python3

# importing the library
import tensorflow as tf

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

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

# Calculating result
res = tf.math.special.spence(a)

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

Output:

a:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5,), dtype=float64) 
Result:  tf.Tensor([ 0.         -0.82246703 -1.43674637 -1.93937542 -2.3699398 ], shape=(5,), dtype=float64)