tf.raw_ops.SparseMatrixSparseMatMul

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

tf.raw_ops.SparseMatrixSparseMatMul(
    a, b, type, transpose_a=False, transpose_b=False, adjoint_a=False,
    adjoint_b=False, name=None
)

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.

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.SparseTensor(a_indices, a_values, a_dense_shape)
      b_st = tf.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.

Args:

  • a: A Tensor of type variant. A CSRSparseMatrix.
  • b: A Tensor of type variant. A CSRSparseMatrix.
  • type: A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.
  • transpose_a: An optional bool. Defaults to False. Indicates whether a should be transposed.
  • transpose_b: An optional bool. Defaults to False. Indicates whether b should be transposed.
  • adjoint_a: An optional bool. Defaults to False. Indicates whether a should be conjugate-transposed.
  • adjoint_b: An optional bool. Defaults to False. Indicates whether b should be conjugate-transposed.
  • name: A name for the operation (optional).

Returns:

A Tensor of type variant.