Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
aliran tensor:: operasi:: MatriksSetDiagV3
#include <array_ops.h> Mengembalikan tensor matriks batch dengan nilai diagonal batch baru.
Ringkasan
Mengingat input dan diagonal , operasi ini mengembalikan tensor dengan bentuk dan nilai yang sama dengan input , kecuali untuk diagonal tertentu dari matriks terdalam. Ini akan ditimpa oleh nilai-nilai di diagonal .
input memiliki dimensi r+1 [I, J, ..., L, M, N] . Jika k adalah skalar atau k[0] == k[1] , diagonal memiliki r dimensi [I, J, ..., L, max_diag_len] . Jika tidak, ia memiliki dimensi r+1 [I, J, ..., L, num_diags, max_diag_len] . num_diags adalah jumlah diagonal, num_diags = k[1] - k[0] + 1 . max_diag_len adalah diagonal terpanjang dalam rentang [k[0], k[1]] , max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
Outputnya adalah tensor rank k+1 dengan dimensi [I, J, ..., L, M, N] . Jika k adalah skalar atau 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 Jika tidak,
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 di mana
d = n - m ,
diag_index = k[1] - d , dan
index_in_diag = n - max(d, 0) + offset .
offset nol kecuali jika garis diagonalnya ke kanan.
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 di mana
diag_len(d) = min(cols - max(d, 0), rows + min(d, 0)) .
Misalnya:
# 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
Fungsi publik
simpul
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Keluaran
operator::tensorflow::Output() const
Fungsi statis publik
Meluruskan
Attrs Align(
StringPiece x
)
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-07-27 UTC.
[null,null,["Terakhir diperbarui pada 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/r2.2/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/r2.2/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/r2.2/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/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input input, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::/versions/r2.2/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/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input input, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input k, const /versions/r2.2/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/r2.2/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/r2.2/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/r2.2/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/r2.2/api_docs/cc/struct/tensorflow/ops/matrix-set-diag-v3/attrs\n \n\n \nOptional attribute setters for /versions/r2.2/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````"]]
