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fluxo tensor:: ops:: LRN
#include <nn_ops.h>
Normalização da resposta local.
Resumo
O tensor input
4-D é tratado como uma matriz 3-D de vetores 1-D (ao longo da última dimensão), e cada vetor é normalizado independentemente. Dentro de um determinado vetor, cada componente é dividido pela soma quadrada ponderada das entradas dentro de depth_radius
. Em detalhes,
sqr_sum[a, b, c, d] =
sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta
Para obter detalhes, consulte Krizhevsky et al., Classificação ImageNet com redes neurais convolucionais profundas (NIPS 2012) .
Argumentos:
- escopo: um objeto Escopo
- entrada: 4-D.
Atributos opcionais (veja Attrs
):
- raio_profundidade: 0-D. Meia largura da janela de normalização 1-D.
- viés: um deslocamento (geralmente positivo para evitar a divisão por 0).
- alfa: Um fator de escala, geralmente positivo.
- beta: Um expoente.
Retorna:
Atributos públicos
Funções públicas
nó
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operador::tensorflow::Saída
operator::tensorflow::Output() const
Funções estáticas públicas
Alfa
Attrs Alpha(
float x
)
Beta
Attrs Beta(
float x
)
Viés
Attrs Bias(
float x
)
Raio de profundidade
Attrs DepthRadius(
int64 x
)
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Última atualização 2025-07-27 UTC.
[null,null,["Última atualização 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::LRN Class Reference\n\ntensorflow::ops::LRN\n====================\n\n`#include \u003cnn_ops.h\u003e`\n\nLocal Response Normalization.\n\nSummary\n-------\n\nThe 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, \n\n```scdoc\nsqr_sum[a, b, c, d] =\n sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)\noutput = input / (bias + alpha * sqr_sum) ** beta\n```\n\n\u003cbr /\u003e\n\nFor details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).\n\nArguments:\n\n- scope: A [Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: 4-D.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs)):\n\n- depth_radius: 0-D. Half-width of the 1-D normalization window.\n- bias: An offset (usually positive to avoid dividing by 0).\n- alpha: A scale factor, usually positive.\n- beta: An exponent.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The output tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [LRN](#classtensorflow_1_1ops_1_1_l_r_n_1adbadf9462bc6ae9916f535bb2aa2762f)`(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)` input)` ||\n| [LRN](#classtensorflow_1_1ops_1_1_l_r_n_1ab702d3657c46710fcf7a63f7c712c5df)`(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)` input, const `[LRN::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_l_r_n_1a001e6e41e5fb3ff78b42decdd680ea82) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_l_r_n_1a69396918e55e1de00f68a1113ef173b0) | `::`[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_l_r_n_1aa28d07232c5df13dad811653f1276a2a)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_l_r_n_1aa00d48e5a8ca805aa2532b7155b8c28b)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_l_r_n_1ae58da447d50c92abb12785d8ab7b618b)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|\n| [Alpha](#classtensorflow_1_1ops_1_1_l_r_n_1a7788a93182ddfbf8bb5bd1820b081392)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [Beta](#classtensorflow_1_1ops_1_1_l_r_n_1a6bbb26306e2265f6e2368f5dfb39ef13)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [Bias](#classtensorflow_1_1ops_1_1_l_r_n_1ac8da24639c0d90ef6e68df756f3e345f)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [DepthRadius](#classtensorflow_1_1ops_1_1_l_r_n_1ac579054901f30ab7fd4989ca39237a0e)`(int64 x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n\n| ### Structs ||\n|---------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::LRN::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs) | Optional attribute setters for [LRN](/versions/r2.2/api_docs/cc/class/tensorflow/ops/l-r-n#classtensorflow_1_1ops_1_1_l_r_n). |\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### LRN\n\n```gdscript\n LRN(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input\n)\n``` \n\n### LRN\n\n```gdscript\n LRN(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n const LRN::Attrs & attrs\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``` \n\nPublic static functions\n-----------------------\n\n### Alpha\n\n```text\nAttrs Alpha(\n float x\n)\n``` \n\n### Beta\n\n```text\nAttrs Beta(\n float x\n)\n``` \n\n### Bias\n\n```text\nAttrs Bias(\n float x\n)\n``` \n\n### DepthRadius\n\n```text\nAttrs DepthRadius(\n int64 x\n)\n```"]]