Thanks for tuning in to Google I/O. View all sessions on demand

# tensorflow::ops::SparseSegmentSum

`#include <math_ops.h>`

Computes the sum along sparse segments of a tensor.

## Summary

Read the section on segmentation for an explanation of segments.

Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first dimension, selecting a subset of dimension 0, specified by `indices`.

For example:

`c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])`

```# Select two rows, one segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
# => [[0 0 0 0]]```

```# Select two rows, two segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
# => [[ 1  2  3  4]
#     [-1 -2 -3 -4]]```

```# Select all rows, two segments.
tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
# => [[0 0 0 0]
#     [5 6 7 8]]```

```# Which is equivalent to:
tf.segment_sum(c, tf.constant([0, 0, 1]))
```

Args:

• scope: A Scope object
• indices: A 1-D tensor. Has same rank as `segment_ids`.
• segment_ids: A 1-D tensor. 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

`SparseSegmentSum(const ::tensorflow::Scope & scope, ::tensorflow::Input data, ::tensorflow::Input indices, ::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

### SparseSegmentSum

``` SparseSegmentSum(
const ::tensorflow::Scope & scope,
::tensorflow::Input data,
::tensorflow::Input indices,
::tensorflow::Input segment_ids
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `
[]
[]