tf.raw_ops.MatrixSetDiagV3

Returns a batched matrix tensor with new batched diagonal values.

tf.raw_ops.MatrixSetDiagV3( input, diagonal, k, align='RIGHT_LEFT', name=None )

Given input and diagonal, this operation returns a tensor with the same shape and values as input, except for the specified diagonals of the innermost matrices. These will be overwritten by the values in diagonal.

input has r+1 dimensions [I, J, ..., L, M, N]. When k is scalar or k[0] == k[1], diagonal has r dimensions [I, J, ..., L, max_diag_len]. Otherwise, it has r+1 dimensions [I, J, ..., L, num_diags, max_diag_len]. num_diags is the number of diagonals, num_diags = k[1] - k[0] + 1. max_diag_len is the longest diagonal in the range [k[0], k[1]], max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))

The output is a tensor of rank k+1 with dimensions [I, J, ..., L, M, N]. If k is scalar or k[0] == k[1]:

output[i, j, ..., l, m, n] = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1] input[i, j, ..., l, m, n] ; otherwise

Otherwise,

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

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

offset is zero except when the alignment of the diagonal is to the right.

offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT} and `d >= 0`) or (`align` in {LEFT_RIGHT, RIGHT_RIGHT} and `d <= 0`) 0 ; otherwise

where diag_len(d) = min(cols - max(d, 0), rows + min(d, 0)).

For example:

# The main diagonal. input = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4) [7, 7, 7, 7], [7, 7, 7, 7]], [[7, 7, 7, 7], [7, 7, 7, 7], [7, 7, 7, 7]]]) diagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3) [4, 5, 6]]) tf.matrix_set_diag(input, diagonal) ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4) [7, 2, 7, 7], [7, 7, 3, 7]], [[4, 7, 7, 7], [7, 5, 7, 7], [7, 7, 6, 7]]] # A superdiagonal (per batch). tf.matrix_set_diag(input, diagonal, k = 1) ==> [[[7, 1, 7, 7], # Output shape: (2, 3, 4) [7, 7, 2, 7], [7, 7, 7, 3]], [[7, 4, 7, 7], [7, 7, 5, 7], [7, 7, 7, 6]]] # A band of diagonals. diagonals = np.array([[[0, 9, 1], # Diagonal shape: (2, 4, 3) [6, 5, 8], [1, 2, 3], [4, 5, 0]], [[0, 1, 2], [5, 6, 4], [6, 1, 2], [3, 4, 0]]]) tf.matrix_set_diag(input, diagonals, k = (-1, 2)) ==> [[[1, 6, 9, 7], # Output shape: (2, 3, 4) [4, 2, 5, 1], [7, 5, 3, 8]], [[6, 5, 1, 7], [3, 1, 6, 2], [7, 4, 2, 4]]] # LEFT_RIGHT alignment. diagonals = np.array([[[9, 1, 0], # Diagonal shape: (2, 4, 3) [6, 5, 8], [1, 2, 3], [0, 4, 5]], [[1, 2, 0], [5, 6, 4], [6, 1, 2], [0, 3, 4]]]) tf.matrix_set_diag(input, diagonals, k = (-1, 2), align="LEFT_RIGHT") ==> [[[1, 6, 9, 7], # Output shape: (2, 3, 4) [4, 2, 5, 1], [7, 5, 3, 8]], [[6, 5, 1, 7], [3, 1, 6, 2], [7, 4, 2, 4]]]

input A Tensor. Rank r+1, where r >= 1.
diagonal A Tensor. Must have the same type as input. Rank r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k >= 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].
align An optional string from: "LEFT_RIGHT", "RIGHT_LEFT", "LEFT_LEFT", "RIGHT_RIGHT". Defaults to "RIGHT_LEFT". Some diagonals are shorter than max_diag_len and need to be padded. align is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. There are four possible alignments: "RIGHT_LEFT" (default), "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is the opposite alignment.
name A name for the operation (optional).

A Tensor. Has the same type as input.