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 defined as the integral of exp(t) / t from -inf to x, with the domain of definition all positive real numbers.
Syntax: 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
import tensorflow as tf
a = tf.constant([ [ - 5 , - 7 ],[ 2 , 0 ]], dtype = tf.float64)
print ( 'a: ' , a)
res = tf.math.special.expint(a)
print ( 'Result: ' , res)
|
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:
Python3
import tensorflow as tf
a = tf.constant([ 1 , 2 , 3 , 4 , 5 ], dtype = tf.float64)
print ( 'a: ' , a)
res = tf.math.special.expint(a)
print ( 'Result: ' , res)
|
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)
Last Updated :
14 Mar, 2023
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