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

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

expint() function

expint() is used to compute element wise Exponential integral of x. It is defines as the integral of exp(t) / t from -inf to x, with the domain of definition all positive real numbers.

Sytnax: tensorflow.math.special.expint( 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

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

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Output:

a:  tf.Tensor(
[[-5. -7.]
 [ 2.  0.]], shape=(2, 2), dtype=float64)
Result:  tf.Tensor(
[[       nan        nan]
 [4.95423436       -inf]], shape=(2, 2), dtype=float64)

Example 2:

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

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Output:

a:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5,), dtype=float64)
Result:  tf.Tensor([ 1.89511782  4.95423436  9.93383257 19.63087447 40.18527536], shape=(5,), dtype=float64)

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