|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L2245-L2385) | Returns the batched diagonal part of a batched tensor. #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.matrix_diag_part`](https://www.tensorflow.org/api_docs/python/tf/linalg/diag_part)
tf.linalg.diag_part( input, name='diag_part', k=0, padding_value=0, align='RIGHT_LEFT' ) ### Used in the notebooks | Used in the tutorials | |---| | - [Uncertainty-aware Deep Learning with SNGP](https://www.tensorflow.org/tutorials/understanding/sngp) - [TFP Release Notes notebook (0.12.1)](https://www.tensorflow.org/probability/examples/TFP_Release_Notebook_0_12_1) | 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. where `y = max(-k[1], 0)`, `x = max(k[1], 0)`. 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. where `d = k[1] - m`, `y = max(-d, 0) - offset`, and `x = 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))`. 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.linalg.diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3) [5, 2, 7]] # A superdiagonal from each batch. tf.linalg.diag_part(input, k = 1) ==> [[2, 7, 6], # Output shape: (2, 3) [4, 3, 8]] # A band from each batch. tf.linalg.diag_part(input, k = (-1, 2)) ==> [[[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]]] # RIGHT_LEFT alignment. tf.linalg.diag_part(input, k = (-1, 2), align="RIGHT_LEFT") ==> [[[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]]] # max_diag_len can be shorter than the main diagonal. tf.linalg.diag_part(input, k = (-2, -1)) ==> [[[5, 8], [0, 9]], [[1, 6], [0, 5]]] # padding_value = 9 tf.linalg.diag_part(input, k = (1, 3), padding_value = 9) ==> [[[4, 9, 9], # Output shape: (2, 3, 3) [3, 8, 9], [2, 7, 6]], [[2, 9, 9], [3, 4, 9], [4, 3, 8]]]


| ## Args || |---|---| | `input` | A `Tensor` with `rank k >= 2`. | | `name` | A name for the operation (optional). | | `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. | | `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 || |---|---| | A Tensor containing diagonals of `input`. Has the same type as `input`. ||



| ## Raises || |---|---| | `InvalidArgumentError` | When `k` is out of bound or when `k[0]>k[1:]`. |