tf.raw_ops.SparseTensorDenseMatMul

{ }

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).

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).

A Tensor. Has the same type as a_values.