tf.sparse.transpose
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Transposes a SparseTensor.
tf.sparse.transpose(
sp_input, perm=None, name=None
)
Permutes the dimensions according to the value of perm. This is the sparse
version of tf.transpose.
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:
x = tf.SparseTensor(indices=[[0, 1], [0, 3], [2, 3], [3, 1]],
values=[1.1, 2.2, 3.3, 4.4],
dense_shape=[4, 5])
print('x =', tf.sparse.to_dense(x))
x = tf.Tensor(
[[0. 1.1 0. 2.2 0. ]
[0. 0. 0. 0. 0. ]
[0. 0. 0. 3.3 0. ]
[0. 4.4 0. 0. 0. ]], shape=(4, 5), dtype=float32)
x_transpose = tf.sparse.transpose(x)
print('x_transpose =', tf.sparse.to_dense(x_transpose))
x_transpose = tf.Tensor(
[[0. 0. 0. 0. ]
[1.1 0. 0. 4.4]
[0. 0. 0. 0. ]
[2.2 0. 3.3 0. ]
[0. 0. 0. 0. ]], shape=(5, 4), dtype=float32)
Equivalently, you could call tf.sparse.transpose(x, perm=[1, 0]). The
perm argument is more useful for n-dimensional tensors where n > 2.
x = tf.SparseTensor(indices=[[0, 0, 1], [0, 0, 3], [1, 2, 3], [1, 3, 1]],
values=[1.1, 2.2, 3.3, 4.4],
dense_shape=[2, 4, 5])
print('x =', tf.sparse.to_dense(x))
x = tf.Tensor(
[[[0. 1.1 0. 2.2 0. ]
[0. 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. ]]
[[0. 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. ]
[0. 0. 0. 3.3 0. ]
[0. 4.4 0. 0. 0. ]]], shape=(2, 4, 5), dtype=float32)
As above, simply calling tf.sparse.transpose will default to perm=[2,1,0].
To take the transpose of a batch of sparse matrices, where 0 is the batch
dimension, you would set perm=[0,2,1].
x_transpose = tf.sparse.transpose(x, perm=[0, 2, 1])
print('x_transpose =', tf.sparse.to_dense(x_transpose))
x_transpose = tf.Tensor(
[[[0. 0. 0. 0. ]
[1.1 0. 0. 0. ]
[0. 0. 0. 0. ]
[2.2 0. 0. 0. ]
[0. 0. 0. 0. ]]
[[0. 0. 0. 0. ]
[0. 0. 0. 4.4]
[0. 0. 0. 0. ]
[0. 0. 3.3 0. ]
[0. 0. 0. 0. ]]], shape=(2, 5, 4), dtype=float32)
Args |
|---|
sp_input
|
The input SparseTensor.
|
perm
|
A permutation vector 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 2023-10-06 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 2023-10-06 UTC."],[],[],null,["# tf.sparse.transpose\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/sparse_ops.py#L2813-L2918) |\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`](https://www.tensorflow.org/api_docs/python/tf/sparse/transpose), [`tf.compat.v1.sparse_transpose`](https://www.tensorflow.org/api_docs/python/tf/sparse/transpose)\n\n\u003cbr /\u003e\n\n tf.sparse.transpose(\n sp_input, perm=None, name=None\n )\n\nPermutes the dimensions according to the value of `perm`. This is the sparse\nversion of [`tf.transpose`](../../tf/transpose).\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 the rank\nof the input tensor. Hence, by default, this operation performs a regular\nmatrix transpose on 2-D input Tensors.\n\n#### For example:\n\n x = tf.SparseTensor(indices=[[0, 1], [0, 3], [2, 3], [3, 1]],\n values=[1.1, 2.2, 3.3, 4.4],\n dense_shape=[4, 5])\n print('x =', tf.sparse.to_dense(x))\n x = tf.Tensor(\n [[0. 1.1 0. 2.2 0. ]\n [0. 0. 0. 0. 0. ]\n [0. 0. 0. 3.3 0. ]\n [0. 4.4 0. 0. 0. ]], shape=(4, 5), dtype=float32)\n\n x_transpose = tf.sparse.transpose(x)\n print('x_transpose =', tf.sparse.to_dense(x_transpose))\n x_transpose = tf.Tensor(\n [[0. 0. 0. 0. ]\n [1.1 0. 0. 4.4]\n [0. 0. 0. 0. ]\n [2.2 0. 3.3 0. ]\n [0. 0. 0. 0. ]], shape=(5, 4), dtype=float32)\n\nEquivalently, you could call `tf.sparse.transpose(x, perm=[1, 0])`. The\n`perm` argument is more useful for n-dimensional tensors where n \\\u003e 2. \n\n x = tf.SparseTensor(indices=[[0, 0, 1], [0, 0, 3], [1, 2, 3], [1, 3, 1]],\n values=[1.1, 2.2, 3.3, 4.4],\n dense_shape=[2, 4, 5])\n print('x =', tf.sparse.to_dense(x))\n x = tf.Tensor(\n [[[0. 1.1 0. 2.2 0. ]\n [0. 0. 0. 0. 0. ]\n [0. 0. 0. 0. 0. ]\n [0. 0. 0. 0. 0. ]]\n [[0. 0. 0. 0. 0. ]\n [0. 0. 0. 0. 0. ]\n [0. 0. 0. 3.3 0. ]\n [0. 4.4 0. 0. 0. ]]], shape=(2, 4, 5), dtype=float32)\n\nAs above, simply calling [`tf.sparse.transpose`](../../tf/sparse/transpose) will default to `perm=[2,1,0]`.\n\nTo take the transpose of a batch of sparse matrices, where 0 is the batch\ndimension, you would set `perm=[0,2,1]`. \n\n x_transpose = tf.sparse.transpose(x, perm=[0, 2, 1])\n print('x_transpose =', tf.sparse.to_dense(x_transpose))\n x_transpose = tf.Tensor(\n [[[0. 0. 0. 0. ]\n [1.1 0. 0. 0. ]\n [0. 0. 0. 0. ]\n [2.2 0. 0. 0. ]\n [0. 0. 0. 0. ]]\n [[0. 0. 0. 0. ]\n [0. 0. 0. 4.4]\n [0. 0. 0. 0. ]\n [0. 0. 3.3 0. ]\n [0. 0. 0. 0. ]]], shape=(2, 5, 4), dtype=float32)\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 vector 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"]]
View source on GitHub