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テンソルフロー::作戦:: MatrixSetDiagV3
#include <array_ops.h>新しいバッチ化された対角値を含むバッチ化された行列テンソルを返します。
まとめ
inputとdiagonalを指定すると、この演算は、最も内側の行列の指定された対角を除き、 inputと同じ形状と値を持つテンソルを返します。これらは、 diagonalの値によって上書きされます。
inputはr+1次元[I, J, ..., L, M, N]です。 kがスカラーまたはk[0] == k[1]の場合、 diagonalはr次元[I, J, ..., L, max_diag_len]になります。それ以外の場合は、 r+1次元[I, J, ..., L, num_diags, max_diag_len]になります。 num_diagsは対角線の数です。 num_diags = k[1] - k[0] + 1 。 max_diag_lenは範囲[k[0], k[1]]の最長の対角線です、 max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
出力は、次元[I, J, ..., L, M, N]のランクk+1のテンソルです。 kがスカラー、またはk[0] == k[1]の場合:
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さもないと、
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ここで、
d = n - m 、
diag_index = k[1] - d 、および
index_in_diag = n - max(d, 0) + offset 。
対角線が右に配置されている場合を除き、 offsetはゼロです。
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ここで
diag_len(d) = min(cols - max(d, 0), rows + min(d, 0)) 。
例えば：
# 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]]]
Arguments:
- scope: A Scope object
- input: Rank
r+1, where r >= 1.
- diagonal: Rank
r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k >= 1.
- 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].
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: Rank r+1, with output.shape = input.shape.
Public static functions
|
Align(StringPiece x)
|
|
Public attributes
公共機能
ノード
::tensorflow::Node * node() const
operator::tensorflow::Input() const
演算子::tensorflow::出力
operator::tensorflow::Output() const
パブリック静的関数
整列
Attrs Align(
StringPiece x
)
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最終更新日 2025-07-27 UTC。
[null,null,["最終更新日 2025-07-27 UTC。"],[],[],null,["# tensorflow::ops::MatrixSetDiagV3 Class Reference\n\ntensorflow::ops::MatrixSetDiagV3\n================================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns a batched matrix tensor with new batched diagonal values.\n\nSummary\n-------\n\nGiven `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`.\n\n`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))`\n\nThe output is a tensor of rank `k+1` with dimensions `[I, J, ..., L, M, N]`. If `k` is scalar or `k[0] == k[1]`:\n\n\n```text\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]\n input[i, j, ..., l, m, n] ; otherwise\n```\n\n\u003cbr /\u003e\n\nOtherwise,\n\n\u003cbr /\u003e\n\n```scdoc\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] \u003c= d \u003c= k[1]\n input[i, j, ..., l, m, n] ; otherwise\n```\nwhere `d = n - m`, `diag_index = k[1] - d`, and `index_in_diag = n - 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\nFor example:\n\n\n```scdoc\n# The main diagonal.\ninput = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4)\n [7, 7, 7, 7],\n [7, 7, 7, 7]],\n [[7, 7, 7, 7],\n [7, 7, 7, 7],\n [7, 7, 7, 7]]])\ndiagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3)\n [4, 5, 6]])\ntf.matrix_set_diag(input, diagonal)\n ==\u003e [[[1, 7, 7, 7], # Output shape: (2, 3, 4)\n [7, 2, 7, 7],\n [7, 7, 3, 7]],\n [[4, 7, 7, 7],\n [7, 5, 7, 7],\n [7, 7, 6, 7]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A superdiagonal (per batch).\ntf.matrix_set_diag(input, diagonal, k = 1)\n ==\u003e [[[7, 1, 7, 7], # Output shape: (2, 3, 4)\n [7, 7, 2, 7],\n [7, 7, 7, 3]],\n [[7, 4, 7, 7],\n [7, 7, 5, 7],\n [7, 7, 7, 6]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A band of diagonals.\ndiagonals = np.array([[[0, 9, 1], # Diagonal shape: (2, 4, 3)\n [6, 5, 8],\n [1, 2, 3],\n [4, 5, 0]],\n [[0, 1, 2],\n [5, 6, 4],\n [6, 1, 2],\n [3, 4, 0]]])\ntf.matrix_set_diag(input, diagonals, k = (-1, 2))\n ==\u003e [[[1, 6, 9, 7], # Output shape: (2, 3, 4)\n [4, 2, 5, 1],\n [7, 5, 3, 8]],\n [[6, 5, 1, 7],\n [3, 1, 6, 2],\n [7, 4, 2, 4]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# LEFT_RIGHT alignment.\ndiagonals = np.array([[[9, 1, 0], # Diagonal shape: (2, 4, 3)\n [6, 5, 8],\n [1, 2, 3],\n [0, 4, 5]],\n [[1, 2, 0],\n [5, 6, 4],\n [6, 1, 2],\n [0, 3, 4]]])\ntf.matrix_set_diag(input, diagonals, k = (-1, 2), align=\"LEFT_RIGHT\")\n ==\u003e [[[1, 6, 9, 7], # Output shape: (2, 3, 4)\n [4, 2, 5, 1],\n [7, 5, 3, 8]],\n [[6, 5, 1, 7],\n [3, 1, 6, 2],\n [7, 4, 2, 4]]]\n```\n\n\u003cbr /\u003e\n\n\n````gdscript\n \n Arguments:\n \n- scope: A /versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope object\n\n \n- input: Rank r+1, where r \u003e= 1.\n\n \n- diagonal: Rank r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k \u003e= 1.\n\n \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\n \n\n Optional attributes (see /versions/r2.3/api_docs/cc/struct/tensorflow/ops/matrix-set-diag-v3/attrs#structtensorflow_1_1ops_1_1_matrix_set_diag_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 \n\n Returns:\n \n- /versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: Rank r+1, with output.shape = input.shape. \n\n \n\n \n\n\n \n### Constructors and Destructors\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1aa1c50423313605b235615b20b525285d(const ::/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input input, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input k)\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1a581008278c2f4caccfe290b868cc9790(const ::/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input input, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input k, const /versions/r2.3/api_docs/cc/struct/tensorflow/ops/matrix-set-diag-v3/attrs#structtensorflow_1_1ops_1_1_matrix_set_diag_v3_1_1_attrs & attrs)\n \n\n \n\n\n \n\n\n \n### Public attributes\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1aa97bc3e979e617469d1ef1df3b816663\n \n\n \n\n /versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1a07f228688563f554887686af516dd9a1\n \n\n \n\n ::/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output\n \n\n \n\n\n \n\n\n \n### Public functions\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1a15eca00505d35ebcd159a9fb0fab1640() const \n \n\n \n\n ::tensorflow::Node *\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1af545db03e9535f6c8be459ac4b684047() const \n \n\n \n\n `\n` \n`\n` \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1a9005a31328cf4653bd9fda4ff88b6bbf() const \n \n\n \n\n `\n` \n`\n` \n\n\n \n\n\n \n### Public static functions\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v3_1a60c1784ebf8f798d1b35e1143c07b467(StringPiece x)\n \n\n \n\n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/matrix-set-diag-v3/attrs#structtensorflow_1_1ops_1_1_matrix_set_diag_v3_1_1_attrs\n \n\n \n\n\n \n\n\n \n### Structs\n\n\n \n\n\n\n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/matrix-set-diag-v3/attrs\n \n\n \nOptional attribute setters for /versions/r2.3/api_docs/cc/class/tensorflow/ops/matrix-set-diag-v3#classtensorflow_1_1ops_1_1_matrix_set_diag_v3. \n\n \n\n\n Public attributes\n \n \n### operation\n\n\n \n```\nOperation operation\n```\n\n \n\n \n \n \n### output\n\n\n \n\n\n```text\n::tensorflow::Output output\n```\n\n \n\n \n Public functions\n \n \n### MatrixSetDiagV3\n\n\n \n\n\n```gdscript\n MatrixSetDiagV3(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input diagonal,\n ::tensorflow::Input k\n)\n```\n\n \n\n \n \n \n### MatrixSetDiagV3\n\n\n \n\n\n```gdscript\n MatrixSetDiagV3(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input diagonal,\n ::tensorflow::Input k,\n const MatrixSetDiagV3::Attrs & attrs\n)\n```\n\n \n\n \n \n \n### node\n\n\n \n\n\n```gdscript\n::tensorflow::Node * node() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Input\n\n\n \n\n\n```gdscript\n operator::tensorflow::Input() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Output\n\n\n \n\n\n```gdscript\n operator::tensorflow::Output() const \n```\n\n \n\n \n Public static functions\n \n \n### Align\n\n\n \n\n\n```text\nAttrs Align(\n StringPiece x\n)\n```\n\n \n\n \n\n \n\n \n````"]]
