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flujo tensor:: operaciones:: MatrixDiagPart
#include <array_ops.h> Devuelve la parte diagonal por lotes de un tensor por lotes.
Resumen
Esta operación devuelve un tensor con la parte diagonal de la input por lotes. La parte diagonal se calcula de la siguiente manera:
Supongamos que input tiene k dimensiones [I, J, K, ..., M, N] , entonces la salida es un tensor de rango k - 1 con dimensiones [I, J, K, ..., min(M, N)] dónde:
diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n] .
La entrada debe ser al menos una matriz.
Por ejemplo:
# 'input' is [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]and input.shape = (2, 4, 4)
tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
which has shape (2, 4)
Argumentos:
- alcance: un objeto de alcance
- entrada: Rango
k tensor donde k >= 2 .
Devoluciones:
-
Output : Las diagonales extraídas tienen forma diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])] .
Atributos públicos
Funciones públicas
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operador::tensorflow::Salida
operator::tensorflow::Output() const
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Última actualización: 2025-07-26 (UTC).
[null,null,["Última actualización: 2025-07-26 (UTC)."],[],[],null,["# tensorflow::ops::MatrixDiagPart Class Reference\n\ntensorflow::ops::MatrixDiagPart\n===============================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns the batched diagonal part of a batched tensor.\n\nSummary\n-------\n\nThis operation returns a tensor with the `diagonal` part of the batched `input`. The `diagonal` part is computed as follows:\n\nAssume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where:\n\n`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.\n\nThe input must be at least a matrix.\n\nFor example:\n\n\n```text\n# 'input' is [[[1, 0, 0, 0]\n [0, 2, 0, 0]\n [0, 0, 3, 0]\n [0, 0, 0, 4]],\n [[5, 0, 0, 0]\n [0, 6, 0, 0]\n [0, 0, 7, 0]\n [0, 0, 0, 8]]]\n```\n\n\u003cbr /\u003e\n\n\n```text\nand input.shape = (2, 4, 4)\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\ntf.matrix_diag_part(input) ==\u003e [[1, 2, 3, 4], [5, 6, 7, 8]]\n```\n\n\u003cbr /\u003e\n\n\n```perl6\nwhich has shape (2, 4)\n```\n\n\u003cbr /\u003e\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: Rank `k` tensor where `k \u003e= 2`.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The extracted diagonal(s) having shape `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [MatrixDiagPart](#classtensorflow_1_1ops_1_1_matrix_diag_part_1a2ff08591126639a356e8ddb7b1bbe901)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input)` ||\n\n| ### Public attributes ||\n|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [diagonal](#classtensorflow_1_1ops_1_1_matrix_diag_part_1aefc6c0270b1e5a8ecca5253aa3197301) | `::`[tensorflow::Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [operation](#classtensorflow_1_1ops_1_1_matrix_diag_part_1a2ee7a3d61dc294c15227bb4a4fd796fb) | [Operation](/versions/r1.15/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_1a6310cbc4148604ca613410d6d3f6794e)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_matrix_diag_part_1ab168eaa21921f9a0ff6ca197fbbbd8da)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_matrix_diag_part_1a6ac92f6b3dfd62c3ef3c08777c62d543)`() const ` | ` ` ` ` |\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### MatrixDiagPart\n\n```gdscript\n MatrixDiagPart(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input\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```"]]
