tensorflow:: אופס:: SparseTensorDenseMatMul
#include <sparse_ops.h>
הכפל SparseTensor (מדרגה 2) "A" במטריצה צפופה "B".
תַקצִיר
לא מתבצעת בדיקת תקפות במדדים של A. עם זאת, פורמט הקלט הבא מומלץ להתנהגות מיטבית:
if adjoint_a == false: יש למיין את A בסדר הולך וגדל מבחינה לקסיקוגרפית. השתמש ב- SparseReorder אם אתה לא בטוח. if adjoint_a == true: יש למיין את A לפי סדר הגדלת ממד 1 (כלומר, סדר "עמודה מרכזית" במקום סדר "שורה מרכזית").
טיעונים:
- scope: אובייקט Scope
- a_indices: 2-D.
indices
שלSparseTensor
, גודל[nnz, 2]
מטריקס. - a_values: 1-D.
values
שלSparseTensor
, גודל[nnz]
וקטור. - a_shape: 1-D.
shape
ה-SparseTensor
, גודל[2]
וקטור. - ב: 2-D. מטריקס צפוף.
מאפיינים אופציונליים (ראה Attrs
):
- adjoint_a: השתמש בצמוד של A במטריצת הכפל. אם A מורכבת, זהו טרנספוזי (conj(A)). אחרת זה טרנספוזי (A).
- adjoint_b: השתמש בצמוד של B במטריצת הכפל. אם B מורכב, זהו טרנספוזי (conj(B)). אחרת זה טרנספוזי (B).
החזרות:
-
Output
: טנזור המוצר.
בנאים והורסים | |
---|---|
SparseTensorDenseMatMul (const :: tensorflow::Scope & scope, :: tensorflow::Input a_indices, :: tensorflow::Input a_values, :: tensorflow::Input a_shape, :: tensorflow::Input b) | |
SparseTensorDenseMatMul (const :: tensorflow::Scope & scope, :: tensorflow::Input a_indices, :: tensorflow::Input a_values, :: tensorflow::Input a_shape, :: tensorflow::Input b, const SparseTensorDenseMatMul::Attrs & attrs) |
תכונות ציבוריות | |
---|---|
operation | |
product |
תפקידים ציבוריים | |
---|---|
node () const | ::tensorflow::Node * |
operator::tensorflow::Input () const | |
operator::tensorflow::Output () const |
פונקציות סטטיות ציבוריות | |
---|---|
AdjointA (bool x) | |
AdjointB (bool x) |
מבנים | |
---|---|
tensorflow:: ops:: SparseTensorDenseMatMul:: Attrs | קובעי תכונות אופציונליים עבור SparseTensorDenseMatMul . |
תכונות ציבוריות
מִבצָע
Operation operation
מוּצָר
::tensorflow::Output product
תפקידים ציבוריים
SparseTensorDenseMatMul
SparseTensorDenseMatMul( const ::tensorflow::Scope & scope, ::tensorflow::Input a_indices, ::tensorflow::Input a_values, ::tensorflow::Input a_shape, ::tensorflow::Input b )
SparseTensorDenseMatMul
SparseTensorDenseMatMul( const ::tensorflow::Scope & scope, ::tensorflow::Input a_indices, ::tensorflow::Input a_values, ::tensorflow::Input a_shape, ::tensorflow::Input b, const SparseTensorDenseMatMul::Attrs & attrs )
צוֹמֶת
::tensorflow::Node * node() const
מפעיל::tensorflow::קלט
operator::tensorflow::Input() const
אופרטור::tensorflow::פלט
operator::tensorflow::Output() const
פונקציות סטטיות ציבוריות
AdjointA
Attrs AdjointA( bool x )
AdjointB
Attrs AdjointB( bool x )
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עדכון אחרון: 2025-07-25 (שעון UTC).
[null,null,["עדכון אחרון: 2025-07-25 (שעון UTC)."],[],[],null,["# tensorflow::ops::SparseTensorDenseMatMul Class Reference\n\ntensorflow::ops::SparseTensorDenseMatMul\n========================================\n\n`#include \u003csparse_ops.h\u003e`\n\n[Multiply](/versions/r2.1/api_docs/cc/class/tensorflow/ops/multiply#classtensorflow_1_1ops_1_1_multiply) SparseTensor (of rank 2) \"A\" by dense matrix \"B\".\n\nSummary\n-------\n\nNo validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior:\n\nif adjoint_a == false: A should be sorted in lexicographically increasing order. Use [SparseReorder](/versions/r2.1/api_docs/cc/class/tensorflow/ops/sparse-reorder#classtensorflow_1_1ops_1_1_sparse_reorder) if you're not sure. if adjoint_a == true: A should be sorted in order of increasing dimension 1 (i.e., \"column major\" order instead of \"row major\" order).\n\nArguments:\n\n- scope: A [Scope](/versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.\n- a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.\n- a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.\n- b: 2-D. A dense Matrix.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r2.1/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs#structtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1_1_attrs)):\n\n- adjoint_a: Use the adjoint of A in the matrix multiply. If A is complex, this is transpose(conj(A)). Otherwise it's transpose(A).\n- adjoint_b: Use the adjoint of B in the matrix multiply. If B is complex, this is transpose(conj(B)). Otherwise it's transpose(B).\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.1/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The product tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [SparseTensorDenseMatMul](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1adf1f689b8b8d0d72c059efbea5fb9cac)`(const ::`[tensorflow::Scope](/versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_indices, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_values, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_shape, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` b)` ||\n| [SparseTensorDenseMatMul](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a3643c83b6940a54319e70b0bc094f948)`(const ::`[tensorflow::Scope](/versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_indices, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_values, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_shape, ::`[tensorflow::Input](/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` b, const `[SparseTensorDenseMatMul::Attrs](/versions/r2.1/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs#structtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|---------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a5213c7ac11f10109585773e8fe2cd041) | [Operation](/versions/r2.1/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [product](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a8895f99af9af5585d8bd937b817bb0ae) | `::`[tensorflow::Output](/versions/r2.1/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|---------------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a3c65317b9c1886136c7de4e03ec51641)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1ac394c138be85d3c36ae20b40f867d72c)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a888b8f4b59aea6d4ff0bdeeba2ad5338)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [AdjointA](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1a972ac5b3a5538d477e96e8d1d857ccca)`(bool x)` | [Attrs](/versions/r2.1/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs#structtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1_1_attrs) |\n| [AdjointB](#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1ac3e772adcb84c991bdd702ba2f6f7b98)`(bool x)` | [Attrs](/versions/r2.1/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs#structtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul_1_1_attrs) |\n\n| ### Structs ||\n|---------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::SparseTensorDenseMatMul::Attrs](/versions/r2.1/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs) | Optional attribute setters for [SparseTensorDenseMatMul](/versions/r2.1/api_docs/cc/class/tensorflow/ops/sparse-tensor-dense-mat-mul#classtensorflow_1_1ops_1_1_sparse_tensor_dense_mat_mul). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### product\n\n```text\n::tensorflow::Output product\n``` \n\nPublic functions\n----------------\n\n### SparseTensorDenseMatMul\n\n```gdscript\n SparseTensorDenseMatMul(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input a_indices,\n ::tensorflow::Input a_values,\n ::tensorflow::Input a_shape,\n ::tensorflow::Input b\n)\n``` \n\n### SparseTensorDenseMatMul\n\n```gdscript\n SparseTensorDenseMatMul(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input a_indices,\n ::tensorflow::Input a_values,\n ::tensorflow::Input a_shape,\n ::tensorflow::Input b,\n const SparseTensorDenseMatMul::Attrs & attrs\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n``` \n\nPublic static functions\n-----------------------\n\n### AdjointA\n\n```text\nAttrs AdjointA(\n bool x\n)\n``` \n\n### AdjointB\n\n```text\nAttrs AdjointB(\n bool x\n)\n```"]]