tf.sparse.reshape
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Reshapes a SparseTensor to represent values in a new dense shape.
tf.sparse.reshape(
sp_input, shape, name=None
)
This operation has the same semantics as reshape on the represented dense
tensor. The indices of non-empty values in sp_input are recomputed based
on the new dense shape, and a new SparseTensor is returned containing the
new indices and new shape. The order of non-empty values in sp_input is
unchanged.
If one component of shape is the special value -1, the size of that
dimension is computed so that the total dense size remains constant. At
most one component of shape can be -1. The number of dense elements
implied by shape must be the same as the number of dense elements
originally represented by sp_input.
For example, if sp_input has shape [2, 3, 6] and indices / values:
[0, 0, 0]: a
[0, 0, 1]: b
[0, 1, 0]: c
[1, 0, 0]: d
[1, 2, 3]: e
and shape is [9, -1], then the output will be a SparseTensor of
shape [9, 4] and indices / values:
[0, 0]: a
[0, 1]: b
[1, 2]: c
[4, 2]: d
[8, 1]: e
Args |
|---|
sp_input
|
The input SparseTensor.
|
shape
|
A 1-D (vector) int64 Tensor specifying the new dense shape of the
represented SparseTensor.
|
name
|
A name prefix for the returned tensors (optional)
|
Returns |
|---|
A SparseTensor with the same non-empty values but with indices calculated
by the new dense shape.
|
Raises |
|---|
TypeError
|
If sp_input is not a SparseTensor.
|
ValueError
|
If argument shape requests a SparseTensor with a different
number of elements than sp_input.
|
ValueError
|
If shape has more than one inferred (== -1) dimension.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.reshape\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#L876-L980) |\n\nReshapes a `SparseTensor` to represent values in a new dense shape.\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.reshape`](https://www.tensorflow.org/api_docs/python/tf/sparse/reshape), [`tf.compat.v1.sparse_reshape`](https://www.tensorflow.org/api_docs/python/tf/sparse/reshape)\n\n\u003cbr /\u003e\n\n tf.sparse.reshape(\n sp_input, shape, name=None\n )\n\nThis operation has the same semantics as `reshape` on the represented dense\ntensor. The indices of non-empty values in `sp_input` are recomputed based\non the new dense shape, and a new `SparseTensor` is returned containing the\nnew indices and new shape. The order of non-empty values in `sp_input` is\nunchanged.\n\nIf one component of `shape` is the special value -1, the size of that\ndimension is computed so that the total dense size remains constant. At\nmost one component of `shape` can be -1. The number of dense elements\nimplied by `shape` must be the same as the number of dense elements\noriginally represented by `sp_input`.\n\nFor example, if `sp_input` has shape `[2, 3, 6]` and `indices` / `values`: \n\n [0, 0, 0]: a\n [0, 0, 1]: b\n [0, 1, 0]: c\n [1, 0, 0]: d\n [1, 2, 3]: e\n\nand `shape` is `[9, -1]`, then the output will be a `SparseTensor` of\nshape `[9, 4]` and `indices` / `values`: \n\n [0, 0]: a\n [0, 1]: b\n [1, 2]: c\n [4, 2]: d\n [8, 1]: e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|-------------------------------------------------------------------------------------------------|\n| `sp_input` | The input `SparseTensor`. |\n| `shape` | A 1-D (vector) int64 `Tensor` specifying the new dense shape of the represented `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 non-empty values but with indices calculated by the new dense shape. ||\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| `ValueError` | If argument `shape` requests a `SparseTensor` with a different number of elements than `sp_input`. |\n| `ValueError` | If `shape` has more than one inferred (== -1) dimension. |\n\n\u003cbr /\u003e"]]
View source on GitHub