Mantieni tutto organizzato con le raccolte
Salva e classifica i contenuti in base alle tue preferenze.
tensoreflusso:: ops:: ConiugatoTrasposizione
#include <array_ops.h>
Mescola le dimensioni di x secondo una permutazione e coniuga il risultato.
Riepilogo
L'output y
ha lo stesso rango di x
. Le forme di x
soddisfano: y.shape[i] == x.shape[ y
y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]
y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])
Argomenti:
Resi:
Attributi pubblici
Funzioni pubbliche
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operatore::tensorflow::Output
operator::tensorflow::Output() const
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-27 UTC.
[null,null,["Ultimo aggiornamento 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::ConjugateTranspose Class Reference\n\ntensorflow::ops::ConjugateTranspose\n===================================\n\n`#include \u003carray_ops.h\u003e`\n\nShuffle dimensions of x according to a permutation and conjugate the result.\n\nSummary\n-------\n\nThe output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]``y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])`\n\nArguments:\n\n- scope: A [Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The y tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [ConjugateTranspose](#classtensorflow_1_1ops_1_1_conjugate_transpose_1a4a5368d3cec175ad261612c95e8da6d3)`(const ::`[tensorflow::Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` x, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` perm)` ||\n\n| ### Public attributes ||\n|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_conjugate_transpose_1aa4e3004e201a961572c3999a46990f0b) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [y](#classtensorflow_1_1ops_1_1_conjugate_transpose_1a804efbc2f1fec9fee64ccac9402bbbdd) | `::`[tensorflow::Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|-------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_conjugate_transpose_1a3829d54bcdcdc65f244e364383c52a12)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_conjugate_transpose_1af0205b3679ff8def147607935343b1c1)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_conjugate_transpose_1aec6563c894874b88ae5b51e91f251ef5)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### y\n\n```text\n::tensorflow::Output y\n``` \n\nPublic functions\n----------------\n\n### ConjugateTranspose\n\n```gdscript\n ConjugateTranspose(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input x,\n ::tensorflow::Input perm\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```"]]