# SparseMatrixSparseMatMul

public final class SparseMatrixSparseMatMul

Sparse-matrix-multiplies two CSR matrices `a` and `b`.

Performs a matrix multiplication of a sparse matrix `a` with a sparse matrix `b`; returns a sparse matrix `a * b`, unless either `a` or `b` is transposed or adjointed.

Each matrix may be transposed or adjointed (conjugated and transposed) according to the Boolean parameters `transpose_a`, `adjoint_a`, `transpose_b` and `adjoint_b`. At most one of `transpose_a` or `adjoint_a` may be True. Similarly, at most one of `transpose_b` or `adjoint_b` may be True.

The inputs must have compatible shapes. That is, the inner dimension of `a` must be equal to the outer dimension of `b`. This requirement is adjusted according to whether either `a` or `b` is transposed or adjointed.

The `type` parameter denotes the type of the matrix elements. Both `a` and `b` must have the same type. The supported types are: `float32`, `float64`, `complex64` and `complex128`.

Both `a` and `b` must have the same rank. Broadcasting is not supported. If they have rank 3, each batch of 2D CSRSparseMatrices within `a` and `b` must have the same dense shape.

The sparse matrix product may have numeric (non-structural) zeros. TODO(anudhyan): Consider adding a boolean attribute to control whether to prune zeros.

Usage example:

``````from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops

a_indices = np.array([[0, 0], [2, 3], [2, 4], [3, 0]])
a_values = np.array([1.0, 5.0, -1.0, -2.0], np.float32)
a_dense_shape = [4, 5]

b_indices = np.array([[0, 0], [3, 0], [3, 1]])
b_values = np.array([2.0, 7.0, 8.0], np.float32)
b_dense_shape = [5, 3]

with tf.Session() as sess:
# Define (COO format) Sparse Tensors over Numpy arrays
a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
b_st = tf.sparse.SparseTensor(b_indices, b_values, b_dense_shape)

# Convert SparseTensors to CSR SparseMatrix
a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
a_st.indices, a_st.values, a_st.dense_shape)
b_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
b_st.indices, b_st.values, b_st.dense_shape)

# Compute the CSR SparseMatrix matrix multiplication
c_sm = sparse_csr_matrix_ops.sparse_matrix_sparse_mat_mul(
a=a_sm, b=b_sm, type=tf.float32)

# Convert the CSR SparseMatrix product to a dense Tensor
c_sm_dense = sparse_csr_matrix_ops.csr_sparse_matrix_to_dense(
c_sm, tf.float32)
# Evaluate the dense Tensor value
c_sm_dense_value = sess.run(c_sm_dense)
``````
`c_sm_dense_value` stores the dense matrix product:
``````[[  2.   0.   0.]
[  0.   0.   0.]
[ 35.  40.   0.]
[ -4.   0.   0.]]
``````
a: A `CSRSparseMatrix`. b: A `CSRSparseMatrix` with the same type and rank as `a`. type: The type of both `a` and `b`. transpose_a: If True, `a` transposed before multiplication. transpose_b: If True, `b` transposed before multiplication. adjoint_a: If True, `a` adjointed before multiplication. adjoint_b: If True, `b` adjointed before multiplication.

### Nested Classes

 class SparseMatrixSparseMatMul.Options Optional attributes for `SparseMatrixSparseMatMul`

### Public Methods

 static SparseMatrixSparseMatMul.Options adjointA(Boolean adjointA) static SparseMatrixSparseMatMul.Options adjointB(Boolean adjointB) Output

## Public Methods

#### public static SparseMatrixSparseMatMul.Options adjointA(Boolean adjointA)

##### Parameters
adjointA Indicates whether `a` should be conjugate-transposed.

#### public static SparseMatrixSparseMatMul.Options adjointB(Boolean adjointB)

##### Parameters
adjointB Indicates whether `b` should be conjugate-transposed.

#### public Output<Object> asOutput()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public Output<?> c()

A CSRSparseMatrix.

#### public static SparseMatrixSparseMatMul create(Scope scope, Operand<?> a, Operand<?> b, Class<T> type, Options... options)

Factory method to create a class wrapping a new SparseMatrixSparseMatMul operation.

##### Parameters
scope current scope A CSRSparseMatrix. A CSRSparseMatrix. carries optional attributes values
##### Returns
• a new instance of SparseMatrixSparseMatMul

#### public static SparseMatrixSparseMatMul.Options transposeA(Boolean transposeA)

##### Parameters
transposeA Indicates whether `a` should be transposed.

#### public static SparseMatrixSparseMatMul.Options transposeB(Boolean transposeB)

##### Parameters
transposeB Indicates whether `b` should be transposed.
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