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tensoreflusso:: ops:: MatrixDiag
#include <array_ops.h> Restituisce un tensore diagonale in batch con determinati valori diagonali in batch.
Riepilogo
Data una diagonal , questa operazione restituisce un tensore con la diagonal e tutto il resto riempito con zeri. La diagonale si calcola come segue:
Supponiamo che diagonal abbia k dimensioni [I, J, K, ..., N] , quindi l'output è un tensore di rango k+1 con dimensioni [I, J, K, ..., N, N]` dove:
output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n] .
Per esempio:
# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
and diagonal.shape = (2, 4)
tf.matrix_diag(diagonal) ==> [[[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]]]which has shape (2, 4, 4)
Argomenti:
- scope: un oggetto Scope
- diagonale: rango
k , dove k >= 1 .
Resi:
-
Output : Rango k+1 , con output.shape = diagonal.shape + [diagonal.shape[-1]] .
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 2025-07-26 UTC.
[null,null,["Ultimo aggiornamento 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::MatrixDiag Class Reference\n\ntensorflow::ops::MatrixDiag\n===========================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns a batched diagonal tensor with a given batched diagonal values.\n\nSummary\n-------\n\nGiven a `diagonal`, this operation returns a tensor with the `diagonal` and everything else padded with zeros. The diagonal is computed as follows:\n\nAssume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a tensor of rank `k+1` with dimensions \\[I, J, K, ..., N, N\\]\\` where:\n\n`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.\n\nFor example:\n\n\n```text\n# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]\n```\n\n\u003cbr /\u003e\n\n\n```text\nand diagonal.shape = (2, 4)\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\ntf.matrix_diag(diagonal) ==\u003e [[[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```perl6\nwhich has shape (2, 4, 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- diagonal: Rank `k`, where `k \u003e= 1`.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [MatrixDiag](#classtensorflow_1_1ops_1_1_matrix_diag_1a2b263945a55c830cec2aa8e732ad4c37)`(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)` diagonal)` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_matrix_diag_1a2a3f9fd08f8b6b8b5209a62bc2c0e4e4) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_matrix_diag_1aba2480ed932f279c48fc6028f6be7a92) | `::`[tensorflow::Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|-----------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_matrix_diag_1aa1db7faefb57b9fee4eddaee99c3a5a3)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_matrix_diag_1ae38fc37ca0a5a229e9c9d3f827ebfa6d)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_matrix_diag_1aaaad00f636d2ad7be0fd131133b79006)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### MatrixDiag\n\n```gdscript\n MatrixDiag(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input diagonal\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```"]]
