tensorflow::ops::SegmentProdV2

#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.

Note: That this op is currently only supported with jit_compile=True.

The only difference with SegmentProd is the additional input num_segments. This helps in evaluating the output shape in compile time. num_segments should be consistent with segment_ids. e.g. Max(segment_ids) - 1 should be equal to num_segments for a 1-d segment_ids With inconsistent num_segments, the op still runs. only difference is, the output takes the size of num_segments irrespective of size of segment_ids and data. for num_segments less than expected output size, the last elements are ignored for num_segments more than the expected output size, last elements are assigned 1.

For example:

.function(jit_compile=True) ... def test(c): ... return tf.raw_ops.SegmentProdV2(data=c, segment_ids=tf.constant([0, 0, 1]), num_segments=2) c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) test(c).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. The values must be less than num_segments.

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

Returns:

• Output: Has same shape as data, except for the first segment_ids.rank dimensions, which are replaced with a single dimensionw which has size num_segments.

Public attributes

Operation operation

::tensorflow::Output output

Public functions

 SegmentProdV2(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input data,
  ::tensorflow::Input segment_ids,
  ::tensorflow::Input num_segments
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const