संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
टेंसरफ़्लो:: ऑप्स:: मैट्रिक्सडायगपार्टV3
#include <array_ops.h> बैच किए गए टेंसर का बैच विकर्ण भाग लौटाता है।
सारांश
बैच किए गए input के k[0] -वें से k[1] -वें विकर्णों के साथ एक टेंसर लौटाता है।
मान लें कि input में r आयाम हैं [I, J, ..., L, M, N] । मान लीजिए max_diag_len निकाले जाने वाले सभी विकर्णों के बीच अधिकतम लंबाई है, max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0)) मान लीजिए num_diags विकर्णों की संख्या है निकालें, num_diags = k[1] - k[0] + 1 ।
यदि num_diags == 1 , तो आउटपुट टेंसर आकार [I, J, ..., L, max_diag_len] और मानों के साथ r - 1 रैंक का है:
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. जहां
y = max(-k[1], 0) ,
x = max(k[1], 0) .
अन्यथा, आउटपुट टेंसर में आयामों के साथ रैंक r है [I, J, ..., L, num_diags, max_diag_len] मानों के साथ:
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. जहां
d = k[1] - m ,
y = max(-d, 0) - offset , और
x = max(d, 0) - offset ।
offset शून्य है, सिवाय इसके कि जब विकर्ण का संरेखण दाईं ओर हो।
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 जहां
diag_len(d) = min(cols - max(d, 0), rows + min(d, 0)) इनपुट कम से कम एक मैट्रिक्स होना चाहिए.
उदाहरण के लिए:
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]]]
Arguments:
- 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
सार्वजनिक समारोह
नोड
::tensorflow::Node * node() const
operator::tensorflow::Input() const
ऑपरेटर::टेन्सरफ़्लो::आउटपुट
operator::tensorflow::Output() const
सार्वजनिक स्थैतिक कार्य
संरेखित
Attrs Align(
StringPiece x
)
जब तक कुछ अलग से न बताया जाए, तब तक इस पेज की सामग्री को Creative Commons Attribution 4.0 License के तहत और कोड के नमूनों को Apache 2.0 License के तहत लाइसेंस मिला है. ज़्यादा जानकारी के लिए, Google Developers साइट नीतियां देखें. Oracle और/या इससे जुड़ी हुई कंपनियों का, Java एक रजिस्टर किया हुआ ट्रेडमार्क है.
आखिरी बार 2025-07-27 (UTC) को अपडेट किया गया.
[null,null,["आखिरी बार 2025-07-27 (UTC) को अपडेट किया गया."],[],[],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````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 tensor where r \u003e= 2.\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- padding_value: The value to fill the area outside the specified diagonal band with. Default is 0.\n\n \n\n Optional attributes (see /versions/r2.2/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 \n\n Returns:\n \n- /versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: The extracted diagonal(s). \n\n \n\n \n\n\n \n### Constructors and Destructors\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1abda51edecba9f012bd9118b2b4e4eb39(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 k, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input padding_value)\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1ab2a7181a88ac68c2c1f8aa8e54d94f36(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 k, ::/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input padding_value, const /versions/r2.2/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\n \n\n\n \n\n\n \n### Public attributes\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1ab5b1ebb490b4c3ac451095ad9c2860ce\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 #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1aab8e9d1b13b2fafd7954d2acc89077f3\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\n\n \n### Public functions\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1a42ebd82d85100f3cae0b90acce7c7d41() const \n \n\n \n\n ::tensorflow::Node *\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1a077796bdba19bb0c51a49a758b47b2f7() const \n \n\n \n\n `\n` \n`\n` \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_diag_part_v3_1aad6d6537777dd1424e046801937d308c() 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_diag_part_v3_1a2bd0085f20db6aaa612f12494bfd9c6e(StringPiece x)\n \n\n \n\n /versions/r2.2/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\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-diag-part-v3/attrs\n \n\n \nOptional attribute setters for /versions/r2.2/api_docs/cc/class/tensorflow/ops/matrix-diag-part-v3#classtensorflow_1_1ops_1_1_matrix_diag_part_v3. \n\n \n\n\n Public attributes\n \n \n### diagonal\n\n\n \n```\n::tensorflow::Output diagonal\n```\n\n \n\n \n \n \n### operation\n\n\n \n\n\n```text\nOperation operation\n```\n\n \n\n \n Public functions\n \n \n### MatrixDiagPartV3\n\n\n \n\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 \n\n \n \n \n### MatrixDiagPartV3\n\n\n \n\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 \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````"]]
