संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
टेंसरफ़्लो:: ऑप्स:: मैट्रिक्ससेटडायगV2
#include <array_ops.h> नए बैच वाले विकर्ण मानों के साथ बैचयुक्त मैट्रिक्स टेंसर लौटाता है।
सारांश
input और diagonal देखते हुए, यह ऑपरेशन input के समान आकार और मान के साथ एक टेंसर लौटाता है, अंतरतम मैट्रिक्स के निर्दिष्ट विकर्णों को छोड़कर। इन्हें diagonal में मानों द्वारा अधिलेखित कर दिया जाएगा।
input में r+1 आयाम हैं [I, J, ..., L, M, N] । जब k अदिश या k[0] == k[1] , diagonal r आयाम होता है [I, J, ..., L, max_diag_len] । अन्यथा, इसके r+1 आयाम हैं [I, J, ..., L, num_diags, max_diag_len] । num_diags विकर्णों की संख्या है, num_diags = k[1] - k[0] + 1 । max_diag_len श्रेणी में सबसे लंबा विकर्ण है [k[0], k[1]] , max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
आउटपुट [I, J, ..., L, M, N] आयामों के साथ रैंक k+1 का एक टेंसर है। यदि k अदिश राशि है या 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 अन्यथा,
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 जहां
d = n - m ,
diag_index = k[1] - d , और
index_in_diag = n - max(d, 0) ।
उदाहरण के लिए:
# 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(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(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([[[1, 2, 3], # Diagonal shape: (2, 2, 3)
[4, 5, 0]],
[[6, 1, 2],
[3, 4, 0]]])
tf.matrix_set_diag(diagonals, k = (-1, 0))
==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4)
[4, 2, 7, 7],
[0, 5, 3, 7]],
[[6, 7, 7, 7],
[3, 1, 7, 7],
[7, 4, 2, 7]]]
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].
Returns:
Output: Rank r+1, with output.shape = input.shape.
Public attributes
सार्वजनिक समारोह
नोड
::tensorflow::Node * node() const
operator::tensorflow::Input() const
ऑपरेटर::टेन्सरफ़्लो::आउटपुट
operator::tensorflow::Output() const
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::MatrixSetDiagV2 Class Reference\n\ntensorflow::ops::MatrixSetDiagV2\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)`.\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(diagonal) ==\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(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([[[1, 2, 3], # Diagonal shape: (2, 2, 3)\n [4, 5, 0]],\n [[6, 1, 2],\n [3, 4, 0]]])\ntf.matrix_set_diag(diagonals, k = (-1, 0))\n ==\u003e [[[1, 7, 7, 7], # Output shape: (2, 3, 4)\n [4, 2, 7, 7],\n [0, 5, 3, 7]],\n [[6, 7, 7, 7],\n [3, 1, 7, 7],\n [7, 4, 2, 7]]]\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 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_v2_1a438f858712bda6df180ee19d8f278bf4(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\n\n \n### Public attributes\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a433c91a80772823c3acd4729a873900f\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_v2_1a390fc69019f7170f80f7c4c3acb12cee\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_v2_1ab0f95dc9ddcb2221701f55c8caddcdb1() const \n \n\n \n\n ::tensorflow::Node *\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a333c742af8203776572da9009d8c0930() const \n \n\n \n\n `\n` \n`\n` \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a0574bd8260d99f8d93fa3a0cb880f0fa() const \n \n\n \n\n `\n` \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### MatrixSetDiagV2\n\n\n \n\n\n```gdscript\n MatrixSetDiagV2(\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### 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\n \n\n \n````"]]
