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# tensorflow::ops::SegmentSum

#include <math_ops.h>

Computes the sum along segments of a tensor.

## Summary

Read the section on segmentation for an explanation of segments.

Computes a tensor such that $$output_i = data_j$$ where sum is over j such that segment_ids[j] == i.

If the sum is empty for a given segment ID i, output[i] = 0.

For example:

c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_sum(c, tf.constant([0, 0, 1]))
# ==> [[5, 5, 5, 5],
#      [5, 6, 7, 8]]


Args:

• scope: A Scope object
• segment_ids: A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.

Returns:

• Output: Has same shape as data, except for dimension 0 which has size k, the number of segments.

### Constructors and Destructors

SegmentSum(const ::tensorflow::Scope & scope, ::tensorflow::Input data, ::tensorflow::Input segment_ids)

### Public attributes

operation
Operation
output
::tensorflow::Output

### Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

## Public attributes

### operation

Operation operation

### output

::tensorflow::Output output

## Public functions

### SegmentSum

 SegmentSum(
const ::tensorflow::Scope & scope,
::tensorflow::Input data,
::tensorflow::Input segment_ids
)

### node

::tensorflow::Node * node() const

### operator::tensorflow::Input

 operator::tensorflow::Input() const

### operator::tensorflow::Output

 operator::tensorflow::Output() const
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[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]