tensorflow::ops::MatrixDiagPartV3

Returns the batched diagonal part of a batched tensor.

Summary

Returns a tensor with the k[0]-th to k[1]-th diagonals of the batched input.

Assume input has r dimensions [I, J, ..., L, M, N]. Let max_diag_len be the maximum length among all diagonals to be extracted, max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0)) Let num_diags be the number of diagonals to extract, num_diags = k[1] - k[0] + 1.

If num_diags == 1, the output tensor is of rank r - 1 with shape [I, J, ..., L, max_diag_len] and values:

diagonal[i, j, ..., l, n] = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, padding_value ; otherwise.

Otherwise, the output tensor has rank r with dimensions [I, J, ..., L, num_diags, max_diag_len] with values:

diagonal[i, j, ..., l, m, n] = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, padding_value ; otherwise.

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

The input must be at least a matrix.

For example:

input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4) [5, 6, 7, 8], [9, 8, 7, 6]], [[5, 4, 3, 2], [1, 2, 3, 4], [5, 6, 7, 8]]])

# A main diagonal from each batch. tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3) [5, 2, 7]]

# A superdiagonal from each batch. tf.matrix_diag_part(input, k = 1) ==> [[2, 7, 6], # Output shape: (2, 3) [4, 3, 8]]

# A band from each batch. tf.matrix_diag_part(input, k = (-1, 2)) ==> [[[0, 3, 8], # Output shape: (2, 4, 3) [2, 7, 6], [1, 6, 7], [5, 8, 0]], [[0, 3, 4], [4, 3, 8], [5, 2, 7], [1, 6, 0]]]

# LEFT_RIGHT alignment. tf.matrix_diag_part(input, k = (-1, 2), align="LEFT_RIGHT") ==> [[[3, 8, 0], # Output shape: (2, 4, 3) [2, 7, 6], [1, 6, 7], [0, 5, 8]], [[3, 4, 0], [4, 3, 8], [5, 2, 7], [0, 1, 6]]]

# max_diag_len can be shorter than the main diagonal. tf.matrix_diag_part(input, k = (-2, -1)) ==> [[[5, 8], [9, 0]], [[1, 6], [5, 0]]]

# padding_value = 9 tf.matrix_diag_part(input, k = (1, 3), padding_value = 9) ==> [[[9, 9, 4], # Output shape: (2, 3, 3) [9, 3, 8], [2, 7, 6]], [[9, 9, 2], [9, 3, 4], [4, 3, 8]]]

Args:

• scope: A Scope object
• input: Rank r tensor where r >= 2.
• 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].
• padding_value: The value to fill the area outside the specified diagonal band with. Default is 0.

Optional attributes (see Attrs):

• align: 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.

Returns:

• Output: The extracted diagonal(s).

Public attributes

::tensorflow::Output diagonal

Operation operation

Public functions

 MatrixDiagPartV3(  const ::tensorflow::Scope & scope,  ::tensorflow::Input input,  ::tensorflow::Input k,  ::tensorflow::Input padding_value )

 MatrixDiagPartV3(  const ::tensorflow::Scope & scope,  ::tensorflow::Input input,  ::tensorflow::Input k,  ::tensorflow::Input padding_value,  const MatrixDiagPartV3::Attrs & attrs )

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Public static functions

Attrs Align( StringPiece x )