tensorflow::ops::SegmentMinV2

#include <math_ops.h>

Computes the minimum along segments of a tensor.

Summary

Read the section on segmentation for an explanation of segments.

Computes a tensor such that $$output_i = \min_j(data_j)$$ where min is over j such that segment_ids[j] == i.

If the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits::max().

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

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.

The only difference with SegmentMin 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) should be equal to num_segments - 1 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 the largest possible value for the specific numeric type.

For example:

.function(jit_compile=True) ... def test(c): ... return tf.raw_ops.SegmentMinV2(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([[1, 2, 2, 1], [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.

Constructors and Destructors

SegmentMinV2(const ::tensorflow::Scope & scope, ::tensorflow::Input data, ::tensorflow::Input segment_ids, ::tensorflow::Input num_segments)

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

SegmentMinV2

 SegmentMinV2(
const ::tensorflow::Scope & scope,
::tensorflow::Input data,
::tensorflow::Input segment_ids,
::tensorflow::Input num_segments
)

node

::tensorflow::Node * node() const

operator::tensorflow::Input

 operator::tensorflow::Input() const

operator::tensorflow::Output

 operator::tensorflow::Output() const
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "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" }]