Stay organized with collections
Save and categorize content based on your preferences.
tensorflow::
ops::
SparseTensorDenseMatMul
#include <sparse_ops.h>
Multiply
SparseTensor (of rank 2) "A" by dense matrix "B".
Summary
No validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior:
if adjoint_a == false: A should be sorted in lexicographically increasing order. Use
SparseReorder
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).
Args:
-
scope: A
Scope
object
-
a_indices: 2-D. The
indices
of the
SparseTensor
, size
[nnz, 2]
Matrix.
-
a_values: 1-D. The
values
of the
SparseTensor
, size
[nnz]
Vector.
-
a_shape: 1-D. The
shape
of the
SparseTensor
, size
[2]
Vector.
-
b: 2-D. A dense Matrix.
Optional attributes (see
Attrs
):
-
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).
-
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).
Returns:
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
AdjointA
Attrs AdjointA(
bool x
)
AdjointB
Attrs AdjointB(
bool x
)
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tensorflow::ops::SparseTensorDenseMatMul Class Reference\n\ntensorflow::\nops::\nSparseTensorDenseMatMul\n==========================================\n\n`\n#include \u003csparse_ops.h\u003e\n`\n\n\n[Multiply](/versions/r2.5/api_docs/cc/class/tensorflow/ops/multiply#classtensorflow_1_1ops_1_1_multiply)\nSparseTensor (of rank 2) \"A\" by dense matrix \"B\".\n\nSummary\n-------\n\n\nNo validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior:\n\n\nif adjoint_a == false: A should be sorted in lexicographically increasing order. Use\n[SparseReorder](/versions/r2.5/api_docs/cc/class/tensorflow/ops/sparse-reorder#classtensorflow_1_1ops_1_1_sparse_reorder)\nif 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\n\nArgs:\n\n- scope: A [Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- a_indices: 2-D. The `\n indices\n ` of the `\n SparseTensor\n ` , size `\n [nnz, 2]\n ` Matrix.\n- a_values: 1-D. The `\n values\n ` of the `\n SparseTensor\n ` , size `\n [nnz]\n ` Vector.\n- a_shape: 1-D. The `\n shape\n ` of the `\n SparseTensor\n ` , size `\n [2]\n ` Vector.\n- b: 2-D. A dense Matrix.\n\n\u003cbr /\u003e\n\n\nOptional attributes (see\n`\n`[Attrs](/versions/r2.5/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):\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\n\nReturns:\n\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` : 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/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_indices, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_values, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_shape, :: `[tensorflow::Input](/versions/r2.5/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/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_indices, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_values, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` a_shape, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` b, const `[SparseTensorDenseMatMul::Attrs](/versions/r2.5/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/r2.5/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/r2.5/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/r2.5/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/r2.5/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/r2.5/api_docs/cc/struct/tensorflow/ops/sparse-tensor-dense-mat-mul/attrs) | Optional attribute setters for [SparseTensorDenseMatMul](/versions/r2.5/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```"]]
