tf.sparse.transpose
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Transposes a SparseTensor
tf.sparse.transpose(
sp_input, perm=None, name=None
)
The returned tensor's dimension i will correspond to the input dimension
perm[i]. If perm is not given, it is set to (n-1...0), where n is
the rank of the input tensor. Hence by default, this operation performs a
regular matrix transpose on 2-D input Tensors.
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 [5, 4] and
indices / values:
[0, 2]: c
[1, 0]: a
[1, 3]: d
[3, 0]: b
Args |
|---|
sp_input
|
The input SparseTensor.
|
perm
|
A permutation of the dimensions of sp_input.
|
name
|
A name prefix for the returned tensors (optional)
|
Returns |
|---|
A transposed SparseTensor.
|
Raises |
|---|
TypeError
|
If sp_input is not a SparseTensor.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.sparse.transpose\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/sparse/transpose) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.1.0/tensorflow/python/ops/sparse_ops.py#L2540-L2599) |\n\nTransposes a `SparseTensor`\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.transpose`](/api_docs/python/tf/sparse/transpose), [`tf.compat.v1.sparse_transpose`](/api_docs/python/tf/sparse/transpose)\n\n\u003cbr /\u003e\n\n tf.sparse.transpose(\n sp_input, perm=None, name=None\n )\n\nThe returned tensor's dimension i will correspond to the input dimension\n`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is\nthe rank of the input tensor. Hence by default, this operation performs a\nregular matrix transpose on 2-D input Tensors.\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 `[5, 4]` and\n`indices` / `values`: \n\n [0, 2]: c\n [1, 0]: a\n [1, 3]: d\n [3, 0]: b\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|---------------------------------------------------|\n| `sp_input` | The input `SparseTensor`. |\n| `perm` | A permutation of the dimensions of `sp_input`. |\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 transposed `SparseTensor`. ||\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"]]
TensorFlow 1 version
View source on GitHub