Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
fluxo tensor:: ops:: ResourceSparseApplyFtrl
#include <training_ops.h>
Atualize as entradas relevantes em '*var' de acordo com o esquema Ftrl-proximal.
Resumo
Isso é para linhas para as quais temos grad, atualizamos var, accum e linear da seguinte forma: accum_new = accum + grad * grad linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadrático = 1,0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sinal(linear) * l1 - linear) / quadrático se |linear| > l1 senão 0,0 acumular = acumular_novo
Argumentos:
- escopo: um objeto Escopo
- var: Deve ser de uma variável().
- accum: Deve ser de uma variável().
- linear: deve ser de uma variável().
- graduação: O gradiente.
- índices: Um vetor de índices na primeira dimensão de var e accum.
- lr: Fator de escala. Deve ser um escalar.
- l1: regularização L1. Deve ser um escalar.
- l2: regularização L2. Deve ser um escalar.
- lr_power: fator de escala. Deve ser um escalar.
Atributos opcionais (veja Attrs
):
- use_locking: Se
True
, a atualização dos tensores var e accum será protegida por um bloqueio; caso contrário, o comportamento será indefinido, mas poderá apresentar menos contenção.
Retorna:
Construtores e Destruidores |
---|
ResourceSparseApplyFtrl (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input accum, :: tensorflow::Input linear, :: tensorflow::Input grad, :: tensorflow::Input indices, :: tensorflow::Input lr, :: tensorflow::Input l1, :: tensorflow::Input l2, :: tensorflow::Input lr_power)
|
ResourceSparseApplyFtrl (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input accum, :: tensorflow::Input linear, :: tensorflow::Input grad, :: tensorflow::Input indices, :: tensorflow::Input lr, :: tensorflow::Input l1, :: tensorflow::Input l2, :: tensorflow::Input lr_power, const ResourceSparseApplyFtrl::Attrs & attrs) |
Atributos públicos
Funções públicas
ResourceSparseApplyFtrl
ResourceSparseApplyFtrl(
const ::tensorflow::Scope & scope,
::tensorflow::Input var,
::tensorflow::Input accum,
::tensorflow::Input linear,
::tensorflow::Input grad,
::tensorflow::Input indices,
::tensorflow::Input lr,
::tensorflow::Input l1,
::tensorflow::Input l2,
::tensorflow::Input lr_power,
const ResourceSparseApplyFtrl::Attrs & attrs
)
operador::tensorflow::Operação
operator::tensorflow::Operation() const
Funções estáticas públicas
UseLocking
Attrs UseLocking(
bool x
)
Exceto em caso de indicação contrária, o conteúdo desta página é licenciado de acordo com a Licença de atribuição 4.0 do Creative Commons, e as amostras de código são licenciadas de acordo com a Licença Apache 2.0. Para mais detalhes, consulte as políticas do site do Google Developers. Java é uma marca registrada da Oracle e/ou afiliadas.
Última atualização 2025-07-26 UTC.
[null,null,["Última atualização 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::ResourceSparseApplyFtrl Class Reference\n\ntensorflow::ops::ResourceSparseApplyFtrl\n========================================\n\n`#include \u003ctraining_ops.h\u003e`\n\nUpdate relevant entries in '\\*var' according to the Ftrl-proximal scheme.\n\nSummary\n-------\n\nThat is for rows we have grad for, we update var, accum and linear as follows: accum_new = accum + grad \\* grad linear += grad - (accum_new\\^(-lr_power) - accum\\^(-lr_power)) / lr \\* var quadratic = 1.0 / (accum_new\\^(lr_power) \\* lr) + 2 \\* l2 var = (sign(linear) \\* l1 - linear) / quadratic if \\|linear\\| \\\u003e l1 else 0.0 accum = accum_new\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- var: Should be from a Variable().\n- accum: Should be from a Variable().\n- linear: Should be from a Variable().\n- grad: The gradient.\n- indices: A vector of indices into the first dimension of var and accum.\n- lr: Scaling factor. Must be a scalar.\n- l1: L1 regularization. Must be a scalar.\n- l2: L2 regularization. Must be a scalar.\n- lr_power: Scaling factor. Must be a scalar.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-ftrl/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1_1_attrs)):\n\n- use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.\n\n\u003cbr /\u003e\n\nReturns:\n\n- the created [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [ResourceSparseApplyFtrl](#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1aee900421c9006251c84f6dd00fb1892d)`(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)` var, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` accum, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` linear, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` grad, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l1, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr_power)` ||\n| [ResourceSparseApplyFtrl](#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1aca1e9533dccd71ef1217f5b21b2429ae)`(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)` var, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` accum, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` linear, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` grad, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l1, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr_power, const `[ResourceSparseApplyFtrl::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-ftrl/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|--------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1a8c62c5bad64bbb58ad975e6e6d6e7510) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n\n| ### Public functions ||\n|-----------------------------------------------------------------------------------------------------------------------------------------|---------|\n| [operator::tensorflow::Operation](#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1ad6adcd76de214e2887864742642d04a9)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [UseLocking](#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1ad1edb0a2e7e8c453477bb22f82046f29)`(bool x)` | [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-ftrl/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl_1_1_attrs) |\n\n| ### Structs ||\n|---------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::ResourceSparseApplyFtrl::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-ftrl/attrs) | Optional attribute setters for [ResourceSparseApplyFtrl](/versions/r1.15/api_docs/cc/class/tensorflow/ops/resource-sparse-apply-ftrl#classtensorflow_1_1ops_1_1_resource_sparse_apply_ftrl). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### ResourceSparseApplyFtrl\n\n```gdscript\n ResourceSparseApplyFtrl(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input var,\n ::tensorflow::Input accum,\n ::tensorflow::Input linear,\n ::tensorflow::Input grad,\n ::tensorflow::Input indices,\n ::tensorflow::Input lr,\n ::tensorflow::Input l1,\n ::tensorflow::Input l2,\n ::tensorflow::Input lr_power\n)\n``` \n\n### ResourceSparseApplyFtrl\n\n```gdscript\n ResourceSparseApplyFtrl(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input var,\n ::tensorflow::Input accum,\n ::tensorflow::Input linear,\n ::tensorflow::Input grad,\n ::tensorflow::Input indices,\n ::tensorflow::Input lr,\n ::tensorflow::Input l1,\n ::tensorflow::Input l2,\n ::tensorflow::Input lr_power,\n const ResourceSparseApplyFtrl::Attrs & attrs\n)\n``` \n\n### operator::tensorflow::Operation\n\n```gdscript\n operator::tensorflow::Operation() const \n``` \n\nPublic static functions\n-----------------------\n\n### UseLocking\n\n```text\nAttrs UseLocking(\n bool x\n)\n```"]]