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tensorflow::ops::MatrixDiagPartV3
#include <array_ops.h>
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. 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.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 static functions
|
Align(StringPiece x)
|
|
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
Align
Attrs Align(
StringPiece x
)
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Last updated 2021-11-15 UTC.
[null,null,["Last updated 2021-11-15 UTC."],[],[],null,["# tensorflow::ops::MatrixDiagPartV3 Class Reference\n\ntensorflow::ops::MatrixDiagPartV3\n=================================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns the batched diagonal part of a batched tensor.\n\nSummary\n-------\n\nReturns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched `input`.\n\nAssume `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`.\n\nIf `num_diags == 1`, the output tensor is of rank `r - 1` with shape `[I, J, ..., L, max_diag_len]` and values:\n\n\u003cbr /\u003e\n\n```scdoc\ndiagonal[i, j, ..., l, n]\n = input[i, j, ..., l, n+y, n+x] ; if 0 \u003c= n+y \u003c M and 0 \u003c= n+x \u003c N,\n padding_value ; otherwise.\n```\nwhere `y = max(-k[1], 0)`, `x = max(k[1], 0)`.\n\n\u003cbr /\u003e\n\nOtherwise, the output tensor has rank `r` with dimensions `[I, J, ..., L, num_diags, max_diag_len]` with values:\n\n\u003cbr /\u003e\n\n```scdoc\ndiagonal[i, j, ..., l, m, n]\n = input[i, j, ..., l, n+y, n+x] ; if 0 \u003c= n+y \u003c M and 0 \u003c= n+x \u003c N,\n padding_value ; otherwise.\n```\nwhere `d = k[1] - m`, `y = max(-d, 0) - offset`, and `x = max(d, 0) - offset`.\n\n\u003cbr /\u003e\n\n`offset` is zero except when the alignment of the diagonal is to the right. \n\n```mysql\noffset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT}\n and `d \u003e= 0`) or\n (`align` in {LEFT_RIGHT, RIGHT_RIGHT}\n and `d \u003c= 0`)\n 0 ; otherwise\n```\nwhere `diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))`.\n\n\u003cbr /\u003e\n\nThe input must be at least a matrix.\n\nFor example:\n\n\n```text\ninput = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4)\n [5, 6, 7, 8],\n [9, 8, 7, 6]],\n [[5, 4, 3, 2],\n [1, 2, 3, 4],\n [5, 6, 7, 8]]])\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A main diagonal from each batch.\ntf.matrix_diag_part(input) ==\u003e [[1, 6, 7], # Output shape: (2, 3)\n [5, 2, 7]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A superdiagonal from each batch.\ntf.matrix_diag_part(input, k = 1)\n ==\u003e [[2, 7, 6], # Output shape: (2, 3)\n [4, 3, 8]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A band from each batch.\ntf.matrix_diag_part(input, k = (-1, 2))\n ==\u003e [[[0, 3, 8], # Output shape: (2, 4, 3)\n [2, 7, 6],\n [1, 6, 7],\n [5, 8, 0]],\n [[0, 3, 4],\n [4, 3, 8],\n [5, 2, 7],\n [1, 6, 0]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# LEFT_RIGHT alignment.\ntf.matrix_diag_part(input, k = (-1, 2), align=\"LEFT_RIGHT\")\n ==\u003e [[[3, 8, 0], # Output shape: (2, 4, 3)\n [2, 7, 6],\n [1, 6, 7],\n [0, 5, 8]],\n [[3, 4, 0],\n [4, 3, 8],\n [5, 2, 7],\n [0, 1, 6]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# max_diag_len can be shorter than the main diagonal.\ntf.matrix_diag_part(input, k = (-2, -1))\n ==\u003e [[[5, 8],\n [9, 0]],\n [[1, 6],\n [5, 0]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# padding_value = 9\ntf.matrix_diag_part(input, k = (1, 3), padding_value = 9)\n ==\u003e [[[9, 9, 4], # Output shape: (2, 3, 3)\n [9, 3, 8],\n [2, 7, 6]],\n [[9, 9, 2],\n [9, 3, 4],\n [4, 3, 8]]]\n```\n\n\u003cbr /\u003e\n\n\n```text\n\n```\n\n\u003cbr /\u003e\n\nArgs:\n\n- scope: A [Scope](/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: Rank `r` tensor where `r \u003e= 2`.\n- 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]`.\n- padding_value: The value to fill the area outside the specified diagonal band with. Default is 0.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/api_docs/cc/struct/tensorflow/ops/matrix-diag-part-v3/attrs#structtensorflow_1_1ops_1_1_matrix_diag_part_v3_1_1_attrs)):\n\n- 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.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The extracted diagonal(s).\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [MatrixDiagPartV3](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1abda51edecba9f012bd9118b2b4e4eb39)`(const ::`[tensorflow::Scope](/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` k, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` padding_value)` ||\n| [MatrixDiagPartV3](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1ab2a7181a88ac68c2c1f8aa8e54d94f36)`(const ::`[tensorflow::Scope](/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` k, ::`[tensorflow::Input](/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` padding_value, const `[MatrixDiagPartV3::Attrs](/api_docs/cc/struct/tensorflow/ops/matrix-diag-part-v3/attrs#structtensorflow_1_1ops_1_1_matrix_diag_part_v3_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|\n| [diagonal](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1ab5b1ebb490b4c3ac451095ad9c2860ce) | `::`[tensorflow::Output](/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [operation](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1aab8e9d1b13b2fafd7954d2acc89077f3) | [Operation](/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n\n| ### Public functions ||\n|-------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1a42ebd82d85100f3cae0b90acce7c7d41)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1a077796bdba19bb0c51a49a758b47b2f7)`() const ` | |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1aad6d6537777dd1424e046801937d308c)`() const ` | |\n\n| ### Public static functions ||\n|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|\n| [Align](#classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1a2bd0085f20db6aaa612f12494bfd9c6e)`(StringPiece x)` | [Attrs](/api_docs/cc/struct/tensorflow/ops/matrix-diag-part-v3/attrs#structtensorflow_1_1ops_1_1_matrix_diag_part_v3_1_1_attrs) |\n\n| ### Structs ||\n|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::MatrixDiagPartV3::Attrs](/api_docs/cc/struct/tensorflow/ops/matrix-diag-part-v3/attrs) | Optional attribute setters for [MatrixDiagPartV3](/api_docs/cc/class/tensorflow/ops/matrix-diag-part-v3#classtensorflow_1_1ops_1_1_matrix_diag_part_v3). |\n\nPublic attributes\n-----------------\n\n### diagonal\n\n```text\n::tensorflow::Output diagonal\n``` \n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### MatrixDiagPartV3\n\n```gdscript\n MatrixDiagPartV3(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input k,\n ::tensorflow::Input padding_value\n)\n``` \n\n### MatrixDiagPartV3\n\n```gdscript\n MatrixDiagPartV3(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input k,\n ::tensorflow::Input padding_value,\n const MatrixDiagPartV3::Attrs & attrs\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n``` \n\nPublic static functions\n-----------------------\n\n### Align\n\n```text\nAttrs Align(\n StringPiece x\n)\n```"]]
