tensorflow::
ops::
DeserializeManySparse
#include <sparse_ops.h>
Deserialize and concatenate
SparseTensors
from a serialized minibatch.
Summary
The input
serialized_sparse
must be a string matrix of shape
[N x 3]
where
N
is the minibatch size and the rows correspond to packed outputs of
SerializeSparse
. The ranks of the original
SparseTensor
objects 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).
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 serialized input is a
[2 x 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 deserialized
SparseTensor
will be:
index = [0 0] [0 10] [0 20] [1 2] [1 10] values = [1, 2, 3, 4, 5] shape = [2 50]
Args:
- scope: A Scope object
-
serialized_sparse: 2-D, The
N
serializedSparseTensor
objects. Must have 3 columns. -
dtype: The
dtype
of the serializedSparseTensor
objects.
Returns:
Constructors and Destructors |
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DeserializeManySparse
(const ::
tensorflow::Scope
& scope, ::
tensorflow::Input
serialized_sparse, DataType dtype)
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Public attributes |
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operation
|
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sparse_indices
|
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sparse_shape
|
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sparse_values
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Public attributes
Public functions
DeserializeManySparse
DeserializeManySparse( const ::tensorflow::Scope & scope, ::tensorflow::Input serialized_sparse, DataType dtype )