Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
tf.raw_ops.SparseTensorDenseMatMul(
a_indices, a_values, a_shape, b, adjoint_a=False, adjoint_b=False, name=None
)
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 | |
|---|---|
a_indices
|
A Tensor. Must be one of the following types: int32, int64.
2-D. The indices of the SparseTensor, size [nnz, 2] Matrix.
|
a_values
|
A Tensor.
1-D. The values of the SparseTensor, size [nnz] Vector.
|
a_shape
|
A Tensor of type int64.
1-D. The shape of the SparseTensor, size [2] Vector.
|
b
|
A Tensor. Must have the same type as a_values.
2-D. A dense Matrix.
|
adjoint_a
|
An optional bool. Defaults to False.
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
|
An optional bool. Defaults to False.
Use the adjoint of B in the matrix multiply. If B is complex, this
is transpose(conj(B)). Otherwise it's transpose(B).
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
A Tensor. Has the same type as a_values.
|