Attend the Women in ML Symposium on December 7

# tensorflow::ops::SegmentProd

#include <math_ops.h>

Computes the product along segments of a tensor.

## Summary

Read the section on segmentation for an explanation of segments.

Computes a tensor such that $$output_i = \prod_j data_j$$ where the product is over j such that segment_ids[j] == i.

If the product is empty for a given segment ID i, output[i] = 1.

Caution: On CPU, values in segment_ids are always validated to be sorted, and an error is thrown for indices that are not increasing. On GPU, this does not throw an error for unsorted indices. On GPU, out-of-order indices result in safe but unspecified behavior, which may include treating out-of-order indices as the same as a smaller following index.

For example:

c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) tf.math.segment_prod(c, tf.constant([0, 0, 1])).numpy() array([[4, 6, 6, 4], [5, 6, 7, 8]], dtype=int32)

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.

Caution: The values are always validated to be sorted on CPU, never validated on GPU.

Returns:

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

### Constructors and Destructors

SegmentProd(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

### SegmentProd

 SegmentProd(
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
[]
[]