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 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) |
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 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) |
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)
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.