tf.raw_ops.GatherNd
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Gather slices from params
into a Tensor with shape specified by indices
.
tf.raw_ops.GatherNd(
params, indices, name=None
)
indices
is a K-dimensional integer tensor, best thought of as a
(K-1)-dimensional tensor of indices into params
, where each element defines a
slice of params
:
output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]
Whereas in tf.gather
indices
defines slices into the axis
dimension of params
, in tf.gather_nd
, indices
defines slices into the
first N
dimensions of params
, where N = indices.shape[-1]
.
The last dimension of indices
can be at most the rank of
params
:
indices.shape[-1] <= params.rank
The last dimension of indices
corresponds to elements
(if indices.shape[-1] == params.rank
) or slices
(if indices.shape[-1] < params.rank
) along dimension indices.shape[-1]
of params
. The output tensor has shape
indices.shape[:-1] + params.shape[indices.shape[-1]:]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, a 0 is stored in the
corresponding output value.
Some examples below.
Simple indexing into a matrix:
indices = [[0, 0], [1, 1]]
params = [['a', 'b'], ['c', 'd']]
output = ['a', 'd']
Slice indexing into a matrix:
indices = [[1], [0]]
params = [['a', 'b'], ['c', 'd']]
output = [['c', 'd'], ['a', 'b']]
Indexing into a 3-tensor:
indices = [[1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['a1', 'b1'], ['c1', 'd1']]]
indices = [[0, 1], [1, 0]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['c0', 'd0'], ['a1', 'b1']]
indices = [[0, 0, 1], [1, 0, 1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = ['b0', 'b1']
Batched indexing into a matrix:
indices = [[[0, 0]], [[0, 1]]]
params = [['a', 'b'], ['c', 'd']]
output = [['a'], ['b']]
Batched slice indexing into a matrix:
indices = [[[1]], [[0]]]
params = [['a', 'b'], ['c', 'd']]
output = [[['c', 'd']], [['a', 'b']]]
Batched indexing into a 3-tensor:
indices = [[[1]], [[0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[[['a1', 'b1'], ['c1', 'd1']]],
[[['a0', 'b0'], ['c0', 'd0']]]]
indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['c0', 'd0'], ['a1', 'b1']],
[['a0', 'b0'], ['c1', 'd1']]]
indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['b0', 'b1'], ['d0', 'c1']]
See also tf.gather
and tf.batch_gather
.
Args |
params
|
A Tensor . The tensor from which to gather values.
|
indices
|
A Tensor . Must be one of the following types: int16 , int32 , int64 .
Index tensor.
|
name
|
A name for the operation (optional).
|
Returns |
A Tensor . Has the same type as params .
|
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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.raw_ops.GatherNd\n\nGather slices from `params` into a Tensor with shape specified by `indices`.\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.raw_ops.GatherNd`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/GatherNd)\n\n\u003cbr /\u003e\n\n tf.raw_ops.GatherNd(\n params, indices, name=None\n )\n\n`indices` is a K-dimensional integer tensor, best thought of as a\n(K-1)-dimensional tensor of indices into `params`, where each element defines a\nslice of `params`: \n\n output[\\\\(i_0, ..., i_{K-2}\\\\)] = params[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n\nWhereas in [`tf.gather`](../../tf/gather) `indices` defines slices into the `axis`\ndimension of `params`, in [`tf.gather_nd`](../../tf/gather_nd), `indices` defines slices into the\nfirst `N` dimensions of `params`, where `N = indices.shape[-1]`.\n\nThe last dimension of `indices` can be at most the rank of\n`params`: \n\n indices.shape[-1] \u003c= params.rank\n\nThe last dimension of `indices` corresponds to elements\n(if `indices.shape[-1] == params.rank`) or slices\n(if `indices.shape[-1] \u003c params.rank`) along dimension `indices.shape[-1]`\nof `params`. The output tensor has shape \n\n indices.shape[:-1] + params.shape[indices.shape[-1]:]\n\nNote that on CPU, if an out of bound index is found, an error is returned.\nOn GPU, if an out of bound index is found, a 0 is stored in the\ncorresponding output value.\n\nSome examples below.\n\nSimple indexing into a matrix: \n\n indices = [[0, 0], [1, 1]]\n params = [['a', 'b'], ['c', 'd']]\n output = ['a', 'd']\n\nSlice indexing into a matrix: \n\n indices = [[1], [0]]\n params = [['a', 'b'], ['c', 'd']]\n output = [['c', 'd'], ['a', 'b']]\n\nIndexing into a 3-tensor: \n\n indices = [[1]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = [[['a1', 'b1'], ['c1', 'd1']]]\n\n\n indices = [[0, 1], [1, 0]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = [['c0', 'd0'], ['a1', 'b1']]\n\n\n indices = [[0, 0, 1], [1, 0, 1]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = ['b0', 'b1']\n\nBatched indexing into a matrix: \n\n indices = [[[0, 0]], [[0, 1]]]\n params = [['a', 'b'], ['c', 'd']]\n output = [['a'], ['b']]\n\nBatched slice indexing into a matrix: \n\n indices = [[[1]], [[0]]]\n params = [['a', 'b'], ['c', 'd']]\n output = [[['c', 'd']], [['a', 'b']]]\n\nBatched indexing into a 3-tensor: \n\n indices = [[[1]], [[0]]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = [[[['a1', 'b1'], ['c1', 'd1']]],\n [[['a0', 'b0'], ['c0', 'd0']]]]\n\n indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = [[['c0', 'd0'], ['a1', 'b1']],\n [['a0', 'b0'], ['c1', 'd1']]]\n\n\n indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]\n params = [[['a0', 'b0'], ['c0', 'd0']],\n [['a1', 'b1'], ['c1', 'd1']]]\n output = [['b0', 'b1'], ['d0', 'c1']]\n\nSee also [`tf.gather`](../../tf/gather) and `tf.batch_gather`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|------------------------------------------------------------------------------------------|\n| `params` | A `Tensor`. The tensor from which to gather values. |\n| `indices` | A `Tensor`. Must be one of the following types: `int16`, `int32`, `int64`. Index tensor. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `params`. ||\n\n\u003cbr /\u003e"]]