tf.raw_ops.TakeManySparseFromTensorsMap
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Read SparseTensors
from a SparseTensorsMap
and concatenate them.
tf.raw_ops.TakeManySparseFromTensorsMap(
sparse_handles,
dtype,
container='',
shared_name='',
name=None
)
The input sparse_handles
must be an int64
matrix of shape [N, 1]
where
N
is the minibatch size and the rows correspond to the output handles of
AddSparseToTensorsMap
or AddManySparseToTensorsMap
. The ranks of the
original SparseTensor
objects that went into the given input ops must all
match. When the final SparseTensor
is created, it has rank one
higher than the ranks of the incoming SparseTensor
objects
(they have been concatenated along a new row dimension on the left).
The output SparseTensor
object's shape values for all dimensions but the
first are the max across the input SparseTensor
objects' shape values
for the corresponding dimensions. Its first shape value is N
, the minibatch
size.
The input SparseTensor
objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run SparseReorder
to restore index ordering.
For example, if the handles represent an input, which is a [2, 3]
matrix
representing two original SparseTensor
objects:
index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
and
index = [ 2]
[10]
values = [4, 5]
shape = [30]
then the final SparseTensor
will be:
index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
Args |
sparse_handles
|
A Tensor of type int64 .
1-D, The N serialized SparseTensor objects.
Shape: [N] .
|
dtype
|
A tf.DType .
The dtype of the SparseTensor objects stored in the
SparseTensorsMap .
|
container
|
An optional string . Defaults to "" .
The container name for the SparseTensorsMap read by this op.
|
shared_name
|
An optional string . Defaults to "" .
The shared name for the SparseTensorsMap read by this op.
It should not be blank; rather the shared_name or unique Operation name
of the Op that created the original SparseTensorsMap should be used.
|
name
|
A name for the operation (optional).
|
Returns |
A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shape).
|
sparse_indices
|
A Tensor of type int64 .
|
sparse_values
|
A Tensor of type dtype .
|
sparse_shape
|
A Tensor of type int64 .
|
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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.TakeManySparseFromTensorsMap\n\n\u003cbr /\u003e\n\nRead `SparseTensors` from a `SparseTensorsMap` and concatenate them.\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.TakeManySparseFromTensorsMap`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/TakeManySparseFromTensorsMap)\n\n\u003cbr /\u003e\n\n tf.raw_ops.TakeManySparseFromTensorsMap(\n sparse_handles,\n dtype,\n container='',\n shared_name='',\n name=None\n )\n\nThe input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where\n`N` is the minibatch size and the rows correspond to the output handles of\n`AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the\noriginal `SparseTensor` objects that went into the given input ops must all\nmatch. When the final `SparseTensor` is created, it has rank one\nhigher than the ranks of the incoming `SparseTensor` objects\n(they have been concatenated along a new row dimension on the left).\n\nThe output `SparseTensor` object's shape values for all dimensions but the\nfirst are the max across the input `SparseTensor` objects' shape values\nfor the corresponding dimensions. Its first shape value is `N`, the minibatch\nsize.\n\nThe input `SparseTensor` objects' indices are assumed ordered in\nstandard lexicographic order. If this is not the case, after this\nstep run `SparseReorder` to restore index ordering.\n\nFor example, if the handles represent an input, which is a `[2, 3]` matrix\nrepresenting two original `SparseTensor` objects: \n\n index = [ 0]\n [10]\n [20]\n values = [1, 2, 3]\n shape = [50]\n\nand \n\n index = [ 2]\n [10]\n values = [4, 5]\n shape = [30]\n\nthen the final `SparseTensor` will be: \n\n index = [0 0]\n [0 10]\n [0 20]\n [1 2]\n [1 10]\n values = [1, 2, 3, 4, 5]\n shape = [2 50]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `sparse_handles` | A `Tensor` of type `int64`. 1-D, The `N` serialized `SparseTensor` objects. Shape: `[N]`. |\n| `dtype` | A [`tf.DType`](../../tf/dtypes/DType). The `dtype` of the `SparseTensor` objects stored in the `SparseTensorsMap`. |\n| `container` | An optional `string`. Defaults to `\"\"`. The container name for the `SparseTensorsMap` read by this op. |\n| `shared_name` | An optional `string`. Defaults to `\"\"`. The shared name for the `SparseTensorsMap` read by this op. It should not be blank; rather the `shared_name` or unique Operation name of the Op that created the original `SparseTensorsMap` should be used. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|------------------|-----------------------------|\n| A tuple of `Tensor` objects (sparse_indices, sparse_values, sparse_shape). ||\n| `sparse_indices` | A `Tensor` of type `int64`. |\n| `sparse_values` | A `Tensor` of type `dtype`. |\n| `sparse_shape` | A `Tensor` of type `int64`. |\n\n\u003cbr /\u003e"]]