tf.raw_ops.MatrixDiagV2

Returns a batched diagonal tensor with given batched diagonal values.

tf.raw_ops.MatrixDiagV2( diagonal, k, num_rows, num_cols, padding_value, name=None )

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

where d = n - m, diag_index = k[1] - d, and index_in_diag = n - max(d, 0).

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]]

diagonal A Tensor. Rank r, where r >= 1
k A Tensor of type int32. 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 A Tensor of type int32. 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 A Tensor of type int32. 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 A Tensor. Must have the same type as diagonal. The number to fill the area outside the specified diagonal band with. Default is 0.
name A name for the operation (optional).

A Tensor. Has the same type as diagonal.