TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
lbeta() is used to compute ln(|Beta(x)|). It reduces the tensor along the last dimension. If one-dimensional z is [z1, …, zk], then Beta(z) is defined as
If x is n+1 dimensional tensor with shape [N1 , . . ., Nn , k], last dimension is treated as z vector and,
If z = [u, v] then traditional bivariate beta function is defined as
Syntax: tensorflow.math.lbeta( x, name)
Parameters:
- x: It’s the input tensor with rank n+1 where n>=0. Allowed dtypes are float, or double.
- name(optional): It defines the name for the operation.
Returns:
It returns the logarithm of |Beta(x)| reducing along the last dimension.
Example 1:
Python3
# Importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([[7, 8], [13, 11]], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating the resultres = tf.math.lbeta(x = a)# Printing the resultprint('Result: ', res) |
Output:
a: tf.Tensor( [[ 7. 8.] [13. 11.]], shape=(2, 2), dtype=float64) Result: tf.Tensor([-10.08680861 -16.5150485 ], shape=(2, ), dtype=float64)
Example 2:
Python3
# Importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([7, 8, 13, 11], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating the resultres = tf.math.lbeta(x = a)# Printing the resultprint('Result: ', res) |
Output:
a: tf.Tensor([ 7. 8. 13. 11.], shape=(4, ), dtype=float64) Result: tf.Tensor(-52.77215897270088, shape=(), dtype=float64)
