tf.raw_ops.SparseSegmentSum
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Computes the sum along sparse segments of a tensor.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.SparseSegmentSum
tf.raw_ops.SparseSegmentSum(
data, indices, segment_ids, sparse_gradient=False, name=None
)
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 |
|---|
data
|
A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
|
indices
|
A Tensor. Must be one of the following types: int32, int64.
A 1-D tensor. Has same rank as segment_ids.
|
segment_ids
|
A Tensor. Must be one of the following types: int32, int64.
A 1-D tensor. Values should be sorted and can be repeated.
|
sparse_gradient
|
An optional bool. Defaults to False.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as data.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.SparseSegmentSum\n\n\u003cbr /\u003e\n\nComputes the sum along sparse segments of a tensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.SparseSegmentSum`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseSegmentSum)\n\n\u003cbr /\u003e\n\n tf.raw_ops.SparseSegmentSum(\n data, indices, segment_ids, sparse_gradient=False, name=None\n )\n\nRead\n[the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)\nfor an explanation of segments.\n\nLike `SegmentSum`, but `segment_ids` can have rank less than `data`'s first\ndimension, selecting a subset of dimension 0, specified by `indices`.\n\n#### For example:\n\n c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])\n\n # Select two rows, one segment.\n tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))\n # =\u003e [[0 0 0 0]]\n\n # Select two rows, two segment.\n tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))\n # =\u003e [[ 1 2 3 4]\n # [-1 -2 -3 -4]]\n\n # Select all rows, two segments.\n tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))\n # =\u003e [[0 0 0 0]\n # [5 6 7 8]]\n\n # Which is equivalent to:\n tf.segment_sum(c, tf.constant([0, 0, 1]))\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. |\n| `indices` | A `Tensor`. Must be one of the following types: `int32`, `int64`. A 1-D tensor. Has same rank as `segment_ids`. |\n| `segment_ids` | A `Tensor`. Must be one of the following types: `int32`, `int64`. A 1-D tensor. Values should be sorted and can be repeated. |\n| `sparse_gradient` | An optional `bool`. Defaults to `False`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `data`. ||\n\n\u003cbr /\u003e"]]
