org.tensorflow.op.linalg.sparse

Class SparseMatrixSparseMatMul

java.lang.Object

org.tensorflow.op.RawOp

org.tensorflow.op.linalg.sparse.SparseMatrixSparseMatMul

All Implemented Interfaces:

org.tensorflow.ndarray.Shaped, Op, Operand<TType>

public final class SparseMatrixSparseMatMul
extends RawOp
implements Operand<TType>

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 Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SparseMatrixSparseMatMul.Inputs
static class	SparseMatrixSparseMatMul.Options Optional attributes for SparseMatrixSparseMatMul

Field Summary

Fields

Modifier and Type	Field and Description
static String	OP_NAME The name of this op, as known by TensorFlow core engine

Fields inherited from class org.tensorflow.op.RawOp

operation

Constructor Summary

Constructors

Constructor and Description
SparseMatrixSparseMatMul(Operation operation)

Method Summary

Modifier and Type	Method and Description
static SparseMatrixSparseMatMul.Options	adjointA(Boolean adjointA) Sets the adjointA option.
static SparseMatrixSparseMatMul.Options	adjointB(Boolean adjointB) Sets the adjointB option.
Output<TType>	asOutput() Returns the symbolic handle of the tensor.
Output<? extends TType>	c() Gets c.
static <T extends TType> SparseMatrixSparseMatMul	create(Scope scope, Operand<? extends TType> a, Operand<? extends TType> b, Class<T> type, SparseMatrixSparseMatMul.Options... options) Factory method to create a class wrapping a new SparseMatrixSparseMatMul operation.
static SparseMatrixSparseMatMul.Options	transposeA(Boolean transposeA) Sets the transposeA option.
static SparseMatrixSparseMatMul.Options	transposeB(Boolean transposeB) Sets the transposeB option.

Methods inherited from class org.tensorflow.op.RawOp

equals, hashCode, op, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.tensorflow.Operand

asTensor, shape, type

Methods inherited from interface org.tensorflow.op.Op

env, op

Methods inherited from interface org.tensorflow.ndarray.Shaped

rank, size

Field Detail

OP_NAME

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

See Also:

Constant Field Values

Constructor Detail

SparseMatrixSparseMatMul

public SparseMatrixSparseMatMul(Operation operation)

Method Detail

create

@Endpoint(describeByClass=true)
public static <T extends TType> SparseMatrixSparseMatMul create(Scope scope,
                                                                                                Operand<? extends TType> a,
                                                                                                Operand<? extends TType> b,
                                                                                                Class<T> type,
                                                                                                SparseMatrixSparseMatMul.Options... options)

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

Type Parameters:

T - data type for SparseMatrixSparseMatMul output and operands

Parameters:

scope - current scope

a - A CSRSparseMatrix.

b - A CSRSparseMatrix.

type - The value of the type attribute

options - carries optional attribute values

Returns:

a new instance of SparseMatrixSparseMatMul

transposeA

public static SparseMatrixSparseMatMul.Options transposeA(Boolean transposeA)

Sets the transposeA option.

Parameters:

transposeA - Indicates whether a should be transposed.

Returns:

this Options instance.

transposeB

public static SparseMatrixSparseMatMul.Options transposeB(Boolean transposeB)

Sets the transposeB option.

Parameters:

transposeB - Indicates whether b should be transposed.

Returns:

this Options instance.

adjointA

public static SparseMatrixSparseMatMul.Options adjointA(Boolean adjointA)

Sets the adjointA option.

Parameters:

adjointA - Indicates whether a should be conjugate-transposed.

Returns:

this Options instance.

adjointB

public static SparseMatrixSparseMatMul.Options adjointB(Boolean adjointB)

Sets the adjointB option.

Parameters:

adjointB - Indicates whether b should be conjugate-transposed.

Returns:

this Options instance.

c

public Output<? extends TType> c()

Gets c. A CSRSparseMatrix.

Returns:

c.

asOutput

public Output<TType> asOutput()

Description copied from interface: Operand

Returns the symbolic handle of the 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.

Specified by:

asOutput in interface Operand<TType>

See Also:

OperationBuilder.addInput(Output)