جریان تنسور:: عملیات:: TakeManySparseFromTensorsMap
#include <sparse_ops.h>
یک نمایش پراکنده را به یک تانسور متراکم تبدیل می کند.
خلاصه
یک آرایه dense
با شکل output_shape
می سازد به طوری که
اگر sparse_indices اسکالر باشد
متراکم[i] = (i == sparse_indices ? sparse_values : default_value)
اگر sparse_indices یک بردار باشد، برای هر i
متراکم[شاخص_پراکنده[i]] = مقادیر_کم[i]
اگر شاخص_های پراکنده یک ماتریس n در d باشد، برای هر i در [0، n)
متراکم[شاخص_های_پراک[i][0]، ...، شاخص_های_کم[i][d-1]] = مقادیر_کم[i]
All other values in `dense` are set to `default_value`. If `sparse_values` is a scalar, all sparse indices are set to this single value.
Indices should be sorted in lexicographic order, and indices must not contain any repeats. If `validate_indices` is true, these properties are checked during execution.
Arguments: * scope: A Scope object * sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete index where `sparse_values[i]` will be placed. * output_shape: 1-D. Shape of the dense output tensor. * sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, or a scalar value to be used for all sparse indices. * default_value: Scalar value to set for indices not specified in `sparse_indices`.
Optional attributes (see `Attrs`): * validate_indices: If true, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats.
Returns: * `Output`: Dense output tensor of shape `output_shape`. */ class SparseToDense { public: /// Optional attribute setters for SparseToDense struct Attrs { /** If true, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats.
Defaults to true */ TF_MUST_USE_RESULT Attrs ValidateIndices(bool x) { Attrs ret = *this; ret.validate_indices_ = x; return ret; }
bool validate_indices_ = true; }; SparseToDense(const tensorflow::Scope& scope, tensorflow::Input sparse_indices, tensorflow::Input output_shape, tensorflow::Input sparse_values, tensorflow::Input default_value); SparseToDense(const tensorflow::Scope& scope, tensorflow::Input sparse_indices, tensorflow::Input output_shape, tensorflow::Input sparse_values, tensorflow::Input default_value, const SparseToDense::Attrs& attrs); operator ::tensorflow::Output() const { return dense; } operator ::tensorflow::Input() const { return dense; } ::tensorflow::Node* node() const { return dense.node(); }
static Attrs ValidateIndices(bool x) { return Attrs().ValidateIndices(x); }
Operation operation; tensorflow::Output dense; };
/** Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
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] ```Arguments:
- scope: A Scope object
- sparse_handles: 1-D, The
N
serializedSparseTensor
objects. Shape:[N]
. - dtype: The
dtype
of theSparseTensor
objects stored in theSparseTensorsMap
.
Optional attributes (see Attrs
):
- container: The container name for the
SparseTensorsMap
read by this op. - shared_name: The shared name for the
SparseTensorsMap
read by this op. It should not be blank; rather theshared_name
or unique Operation name of the Op that created the originalSparseTensorsMap
should be used.
Returns:
Output
sparse_indices: 2-D. Theindices
of the minibatchSparseTensor
.Output
sparse_values: 1-D. Thevalues
of the minibatchSparseTensor
.Output
sparse_shape: 1-D. Theshape
of the minibatchSparseTensor
.
Constructors and Destructors |
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TakeManySparseFromTensorsMap(const ::tensorflow::Scope & scope, ::tensorflow::Input sparse_handles, DataType dtype)
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TakeManySparseFromTensorsMap(const ::tensorflow::Scope & scope, ::tensorflow::Input sparse_handles, DataType dtype, const TakeManySparseFromTensorsMap::Attrs & attrs)
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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 static functions |
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Container(StringPiece x)
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SharedName(StringPiece x)
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Structs |
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tensorflow:: |
Optional attribute setters for TakeManySparseFromTensorsMap. |
Public attributes
operation
Operation operation
اندیس_های پراکنده
::tensorflow::Output sparse_indices
پراکنده_شکل
::tensorflow::Output sparse_shape
مقادیر_کم
::tensorflow::Output sparse_values
توابع عمومی
TakeManySparseFromTensorsMap
TakeManySparseFromTensorsMap( const ::tensorflow::Scope & scope, ::tensorflow::Input sparse_handles, DataType dtype )
TakeManySparseFromTensorsMap
TakeManySparseFromTensorsMap( const ::tensorflow::Scope & scope, ::tensorflow::Input sparse_handles, DataType dtype, const TakeManySparseFromTensorsMap::Attrs & attrs )
توابع استاتیک عمومی
ظرف
Attrs Container( StringPiece x )
SharedName
Attrs SharedName( StringPiece x )
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تاریخ آخرین بهروزرسانی 2024-12-02 بهوقت ساعت هماهنگ جهانی.