tensorflow:: ops:: MatrixDiagV2

Returns a batched diagonal tensor with given batched diagonal values.

Summary

Returns a tensor with the contents in diagonal as k[0] -th to k[1] -th diagonals of a matrix, with everything else padded with padding . num_rows and num_cols specify the dimension of the innermost matrix of the output. If both are not specified, the op assumes the innermost matrix is square and infers its size from k and the innermost dimension of diagonal . If only one of them is specified, the op assumes the unspecified value is the smallest possible based on other criteria.

Let diagonal have r dimensions [I, J, ..., L, M, N] . The output tensor has rank r+1 with shape [I, J, ..., L, M, num_rows, num_cols] when only one diagonal is given ( k is an integer or k[0] == k[1] ). Otherwise, it has rank r with shape [I, J, ..., L, num_rows, num_cols] .

The second innermost dimension of diagonal has double meaning. When k is scalar or k[0] == k[1] , M is part of the batch size [I, J, ..., M], and the output tensor is:

output[i, j, ..., l, m, n]
  = diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper
    padding_value                             ; otherwise

Otherwise, M is treated as the number of diagonals for the matrix in the same batch ( M = k[1]-k[0]+1 ), and the output tensor is:

output[i, j, ..., l, m, n]
  = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1]
    padding_value                                     ; otherwise

For example:

# The main diagonal.
diagonal = np.array([[1, 2, 3, 4],            # Input shape: (2, 4)
                     [5, 6, 7, 8]])
tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0],  # Output shape: (2, 4, 4)
                               [0, 2, 0, 0],
                               [0, 0, 3, 0],
                               [0, 0, 0, 4]],
                              [[5, 0, 0, 0],
                               [0, 6, 0, 0],
                               [0, 0, 7, 0],
                               [0, 0, 0, 8]]]

# A superdiagonal (per batch).
diagonal = np.array([[1, 2, 3],  # Input shape: (2, 3)
                     [4, 5, 6]])
tf.matrix_diag(diagonal, k = 1)
  ==> [[[0, 1, 0, 0],  # Output shape: (2, 4, 4)
        [0, 0, 2, 0],
        [0, 0, 0, 3],
        [0, 0, 0, 0]],
       [[0, 4, 0, 0],
        [0, 0, 5, 0],
        [0, 0, 0, 6],
        [0, 0, 0, 0]]]

# A band of diagonals.
diagonals = np.array([[[1, 2, 3],  # Input shape: (2, 2, 3)
                       [4, 5, 0]],
                      [[6, 7, 9],
                       [9, 1, 0]]])
tf.matrix_diag(diagonals, k = (-1, 0))
  ==> [[[1, 0, 0],  # Output shape: (2, 3, 3)
        [4, 2, 0],
        [0, 5, 3]],
       [[6, 0, 0],
        [9, 7, 0],
        [0, 1, 9]]]

# Rectangular matrix.
diagonal = np.array([1, 2])  # Input shape: (2)
tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)
  ==> [[0, 0, 0, 0],  # Output shape: (3, 4)
       [1, 0, 0, 0],
       [0, 2, 0, 0]]

# Rectangular matrix with inferred num_cols and padding_value = 9.
tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)
  ==> [[9, 9],  # Output shape: (3, 2)
       [1, 9],
       [9, 2]]

Args:

• scope: A Scope object
• diagonal: Rank r , where r >= 1
• k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. k can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. k[0] must not be larger than k[1] .
• num_rows: The number of rows of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of diagonal .
• num_cols: The number of columns of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of diagonal .
• padding_value: The number to fill the area outside the specified diagonal band with. Default is 0.

Returns:

• Output : Has rank r+1 when k is an integer or k[0] == k[1] , rank r otherwise.

Public attributes

Operation operation

::tensorflow::Output output

Public functions

 MatrixDiagV2(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input diagonal,
  ::tensorflow::Input k,
  ::tensorflow::Input num_rows,
  ::tensorflow::Input num_cols,
  ::tensorflow::Input padding_value
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const