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
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([ [-5, -7],[ 2, 0]], dtype=tf.float64)# Printing the input tensorprint('a: ', a)# Calculating resultres = tf.math.special.expint(a)# Printing the resultprint('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
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([1, 2, 3, 4, 5], dtype=tf.float64)# Printing the input tensorprint('a: ', a)# Calculating resultres = tf.math.special.expint(a)# Printing the resultprint('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)
