tf.compat.v1.sparse_segment_mean
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Computes the mean along sparse segments of a tensor.
tf.compat.v1.sparse_segment_mean(
data,
indices,
segment_ids,
name=None,
num_segments=None,
sparse_gradient=False
)
Read the section on
segmentation
for an explanation of segments.
Like tf.math.segment_mean, but segment_ids can have rank less than
data's first dimension, selecting a subset of dimension 0, specified by
indices.
segment_ids is allowed to have missing ids, in which case the output will
be zeros at those indices. In those cases num_segments is used to determine
the size of the output.
Args |
|---|
data
|
A Tensor with data that will be assembled in the output.
|
indices
|
A 1-D Tensor with indices into data. Has same rank as
segment_ids.
|
segment_ids
|
A 1-D Tensor with indices into the output Tensor. Values
should be sorted and can be repeated.
|
name
|
A name for the operation (optional).
|
num_segments
|
An optional int32 scalar. Indicates the size of the output
Tensor.
|
sparse_gradient
|
An optional bool. Defaults to False. If True, the
gradient of this function will be sparse (IndexedSlices) instead of
dense (Tensor). The sparse gradient will contain one non-zero row for
each unique index in indices.
|
Returns |
|---|
A tensor of the shape as data, except for dimension 0 which
has size k, the number of segments specified via num_segments or
inferred for the last element in segments_ids.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.compat.v1.sparse_segment_mean\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/math_ops.py#L4757-L4815) |\n\nComputes the mean 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.sparse.segment_mean`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/sparse_segment_mean)\n\n\u003cbr /\u003e\n\n tf.compat.v1.sparse_segment_mean(\n data,\n indices,\n segment_ids,\n name=None,\n num_segments=None,\n sparse_gradient=False\n )\n\nRead [the section on\nsegmentation](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/math#about_segmentation)\nfor an explanation of segments.\n\nLike [`tf.math.segment_mean`](../../../tf/math/segment_mean), but `segment_ids` can have rank less than\n`data`'s first dimension, selecting a subset of dimension 0, specified by\n`indices`.\n`segment_ids` is allowed to have missing ids, in which case the output will\nbe zeros at those indices. In those cases `num_segments` is used to determine\nthe size of the output.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | A `Tensor` with data that will be assembled in the output. |\n| `indices` | A 1-D `Tensor` with indices into `data`. Has same rank as `segment_ids`. |\n| `segment_ids` | A 1-D `Tensor` with indices into the output `Tensor`. Values should be sorted and can be repeated. |\n| `name` | A name for the operation (optional). |\n| `num_segments` | An optional int32 scalar. Indicates the size of the output `Tensor`. |\n| `sparse_gradient` | An optional `bool`. Defaults to `False`. If `True`, the gradient of this function will be sparse (`IndexedSlices`) instead of dense (`Tensor`). The sparse gradient will contain one non-zero row for each unique index in `indices`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `tensor` of the shape as data, except for dimension 0 which has size `k`, the number of segments specified via `num_segments` or inferred for the last element in `segments_ids`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub