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<Object>
asOutput()
Returns the symbolic handle of a tensor.
Output<?>
c()
A CSRSparseMatrix.
static <T> SparseMatrixSparseMatMul
create(Scope scope, Operand<?> a, Operand<?> b, Class<T> type, Options... options)
Factory method to create a class wrapping a new SparseMatrixSparseMatMul operation.
static SparseMatrixSparseMatMul.Options
transposeA(Boolean transposeA)
static SparseMatrixSparseMatMul.Options
transposeB(Boolean transposeB)

Inherited Methods

org.tensorflow.op.PrimitiveOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Returns the underlying Operation
final String
toString()
boolean
equals(Object arg0)
final Class<?>
getClass()
int
hashCode()
final void
notify()
final void
notifyAll()
String
toString()
final void
wait(long arg0, int arg1)
final void
wait(long arg0)
final void
wait()
org.tensorflow.Operand
abstract Output<Object>
asOutput()
Returns the symbolic handle of a tensor.

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 A CSRSparseMatrix.
b A CSRSparseMatrix.
options 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.