# Python – tensorflow.math.unsorted_segment_sum()

• Last Updated : 16 Jun, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

unsorted_segment_sum() is used to find the sum of segments.

Syntax: tensorflow.math.unsorted_segment_sum( data, segment_ids, num_segments, name )

Parameter:

• data: It is a tensor. Allowed dtypes are floating point or complex.
• segment_ids: It’s 1-D tensor with sorted values. It’s size should be equal to  size of first dimension of data. It represents number of distinct segment IDs. Allowed dtypes are int32 and int64.
• num_segments: It is a Tensor. Allowed dtypes are int32 and int64.
• name(optional): It defines the name for the operation.

Return: It returns a tensor of dtype as x.

Example 1:

## Python3

 # importing the library import tensorflow as tf    # Initializing the input tensor data = tf.constant([1, 2, 3], dtype = tf.float64) segment_ids = tf.constant([2, 2, 2])    # Printing the input tensor print('data: ', data) print('segment_ids: ', segment_ids)    # Calculating result res = tf.math.unsorted_segment_sum(data, segment_ids, tf.constant(3))    # Printing the result print('Result: ', res)

Output:

data:  tf.Tensor([1. 2. 3.], shape=(3, ), dtype=float64)
segment_ids:  tf.Tensor([2 2 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor([0. 0. 6.], shape=(3, ), dtype=float64)

Example 2:

## Python3

 # importing the library import tensorflow as tf    # Initializing the input tensor data = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype = tf.float64) segment_ids = tf.constant([0, 0, 2])    # Printing the input tensor print('data: ', data) print('segment_ids: ', segment_ids)    # Calculating result res = tf.math.unsorted_segment_sum(data, segment_ids, tf.constant(3))    # Printing the result print('Result: ', res)

Output:

data:  tf.Tensor(
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]], shape=(3, 3), dtype=float64)
segment_ids:  tf.Tensor([0 0 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor(
[[5. 7. 9.]
[0. 0. 0.]
[7. 8. 9.]], shape=(3, 3), dtype=float64)

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