tensorflow::ops::SparseTensorDenseMatMul

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).

Arguments:

• 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:

• Output: The product tensor.

Public attributes

Operation operation

::tensorflow::Output product

 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
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs AdjointA(
  bool x
)

Attrs AdjointB(
  bool x
)