tf.sparse.reorder
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Reorders a SparseTensor into the canonical, row-major ordering.
tf.sparse.reorder(
sp_input, name=None
)
Used in the notebooks
Note that by convention, all sparse ops preserve the canonical ordering
along increasing dimension number. The only time ordering can be violated
is during manual manipulation of the indices and values to add entries.
Reordering does not affect the shape of the SparseTensor.
For example, if sp_input has shape [4, 5] and indices / values:
[0, 3]: b
[0, 1]: a
[3, 1]: d
[2, 0]: c
then the output will be a SparseTensor of shape [4, 5] and
indices / values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Args |
|---|
sp_input
|
The input SparseTensor.
|
name
|
A name prefix for the returned tensors (optional)
|
Returns |
|---|
A SparseTensor with the same shape and non-empty values, but in
canonical ordering.
|
Raises |
|---|
TypeError
|
If sp_input is not a SparseTensor.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.sparse.reorder\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/sparse_ops.py#L821-L873) |\n\nReorders a `SparseTensor` into the canonical, row-major ordering.\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.reorder`](https://www.tensorflow.org/api_docs/python/tf/sparse/reorder), [`tf.compat.v1.sparse_reorder`](https://www.tensorflow.org/api_docs/python/tf/sparse/reorder)\n\n\u003cbr /\u003e\n\n tf.sparse.reorder(\n sp_input, name=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Client-efficient large-model federated learning via \\`federated_select\\` and sparse aggregation](https://www.tensorflow.org/federated/tutorials/sparse_federated_learning) |\n\nNote that by convention, all sparse ops preserve the canonical ordering\nalong increasing dimension number. The only time ordering can be violated\nis during manual manipulation of the indices and values to add entries.\n\nReordering does not affect the shape of the `SparseTensor`.\n\nFor example, if `sp_input` has shape `[4, 5]` and `indices` / `values`: \n\n [0, 3]: b\n [0, 1]: a\n [3, 1]: d\n [2, 0]: c\n\nthen the output will be a `SparseTensor` of shape `[4, 5]` and\n`indices` / `values`: \n\n [0, 1]: a\n [0, 3]: b\n [2, 0]: c\n [3, 1]: d\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|---------------------------------------------------|\n| `sp_input` | The input `SparseTensor`. |\n| `name` | A name prefix for the returned tensors (optional) |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `SparseTensor` with the same shape and non-empty values, but in canonical ordering. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|-------------|----------------------------------------|\n| `TypeError` | If `sp_input` is not a `SparseTensor`. |\n\n\u003cbr /\u003e"]]
View source on GitHub