# Python – tensorflow.math.lbeta()

• Last Updated : 14 Sep, 2021

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 library import tensorflow as tf   # Initializing the input tensor a = tf.constant([[7, 8], [13, 11]], dtype = tf.float64)   # Printing the input tensor print('a: ', a)   # Calculating the result res = tf.math.lbeta(x = a)   # Printing the result print('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 library import tensorflow as tf   # Initializing the input tensor a = tf.constant([7, 8, 13, 11], dtype = tf.float64)   # Printing the input tensor print('a: ', a)   # Calculating the result res = tf.math.lbeta(x = a)   # Printing the result print('Result: ', res)

Output:

a:  tf.Tensor([ 7.  8. 13. 11.], shape=(4, ), dtype=float64)
Result:  tf.Tensor(-52.77215897270088, shape=(), dtype=float64)

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