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flujo tensor:: operaciones:: SparseApplyFtrlV2
#include <training_ops.h> Actualice las entradas relevantes en '*var' según el esquema Ftrl-proximal.
Resumen
Es decir, para las filas para las que tenemos grad, actualizamos var, accum y lineal de la siguiente manera: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage lineal += grad_with_shrinkage + (accum_new^(-lr_power) - accum^ (-lr_power)) / lr * var cuadrático = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (signo(lineal) * l1 - lineal) / cuadrático si |lineal| > l1 más 0.0 accum = accum_new
Argumentos:
- alcance: un objeto de alcance
- var: Debe ser de una Variable().
- accum: Debe ser de una Variable().
- lineal: debe ser de una variable ().
- grad: El gradiente.
- índices: Un vector de índices en la primera dimensión de var y accum.
- lr: Factor de escala. Debe ser un escalar.
- l1: regularización L1. Debe ser un escalar.
- l2: Regularización de contracción L2. Debe ser un escalar.
- lr_power: factor de escala. Debe ser un escalar.
Atributos opcionales (ver Attrs ):
- use_locking: si es
True , la actualización de los tensores var y accum estará protegida por un bloqueo; de lo contrario, el comportamiento no está definido, pero puede presentar menos contención.
Devoluciones:
Constructores y destructores |
|---|
SparseApplyFtrlV2 (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 l2_shrinkage, :: tensorflow::Input lr_power)
|
SparseApplyFtrlV2 (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 l2_shrinkage, :: tensorflow::Input lr_power, const SparseApplyFtrlV2::Attrs & attrs)
|
Atributos públicos
Funciones públicas
SparseApplyFtrlV2
SparseApplyFtrlV2(
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 l2_shrinkage,
::tensorflow::Input lr_power
)
SparseApplyFtrlV2
SparseApplyFtrlV2(
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 l2_shrinkage,
::tensorflow::Input lr_power,
const SparseApplyFtrlV2::Attrs & attrs
)
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operador::tensorflow::Salida
operator::tensorflow::Output() const
Funciones estáticas públicas
UsoBloqueo
Attrs UseLocking(
bool x
)
,
flujo tensor:: operaciones:: SparseApplyFtrlV2
#include <training_ops.h> Actualice las entradas relevantes en '*var' según el esquema Ftrl-proximal.
Resumen
Es decir, para las filas para las que tenemos grad, actualizamos var, accum y lineal de la siguiente manera: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage lineal += grad_with_shrinkage + (accum_new^(-lr_power) - accum^ (-lr_power)) / lr * var cuadrático = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (signo(lineal) * l1 - lineal) / cuadrático si |lineal| > l1 más 0.0 accum = accum_new
Argumentos:
- alcance: un objeto de alcance
- var: Debe ser de una Variable().
- accum: Debe ser de una Variable().
- lineal: debe ser de una variable ().
- grad: El gradiente.
- índices: Un vector de índices en la primera dimensión de var y accum.
- lr: Factor de escala. Debe ser un escalar.
- l1: regularización L1. Debe ser un escalar.
- l2: Regularización de contracción L2. Debe ser un escalar.
- lr_power: factor de escala. Debe ser un escalar.
Atributos opcionales (ver Attrs ):
- use_locking: si es
True , la actualización de los tensores var y accum estará protegida por un bloqueo; de lo contrario, el comportamiento no está definido, pero puede presentar menos contención.
Devoluciones:
Constructores y destructores |
|---|
SparseApplyFtrlV2 (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 l2_shrinkage, :: tensorflow::Input lr_power)
|
SparseApplyFtrlV2 (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 l2_shrinkage, :: tensorflow::Input lr_power, const SparseApplyFtrlV2::Attrs & attrs)
|
Atributos públicos
Funciones públicas
SparseApplyFtrlV2
SparseApplyFtrlV2(
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 l2_shrinkage,
::tensorflow::Input lr_power
)
SparseApplyFtrlV2
SparseApplyFtrlV2(
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 l2_shrinkage,
::tensorflow::Input lr_power,
const SparseApplyFtrlV2::Attrs & attrs
)
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operador::tensorflow::Salida
operator::tensorflow::Output() const
Funciones estáticas públicas
UsoBloqueo
Attrs UseLocking(
bool x
)
A menos que se indique lo contrario, el contenido de esta página está sujeto a la licencia Reconocimiento 4.0 de Creative Commons y las muestras de código están sujetas a la licencia Apache 2.0. Para obtener más información, consulta las políticas del sitio web de Google Developers. Java es una marca registrada de Oracle o sus afiliados.
Última actualización: 2025-07-27 (UTC).
[null,null,["Última actualización: 2025-07-27 (UTC)."],[],[],null,["# tensorflow::ops::SparseApplyFtrlV2 Class Reference\n\ntensorflow::ops::SparseApplyFtrlV2\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: grad_with_shrinkage = grad + 2 \\* l2_shrinkage \\* var accum_new = accum + grad_with_shrinkage \\* grad_with_shrinkage linear += grad_with_shrinkage + (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/r2.2/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 shrinkage regularization. Must be a scalar.\n- lr_power: Scaling factor. Must be a scalar.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/sparse-apply-ftrl-v2/attrs#structtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_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- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): Same as \"var\".\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [SparseApplyFtrlV2](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1a5aac85294ef2fe3491d385699c320466)`(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)` var, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` accum, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` linear, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` grad, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l1, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2_shrinkage, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr_power)` ||\n| [SparseApplyFtrlV2](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1abf2e6f5633ae8e3bf9dfbeb7222d1f5e)`(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)` var, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` accum, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` linear, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` grad, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l1, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` l2_shrinkage, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lr_power, const `[SparseApplyFtrlV2::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/sparse-apply-ftrl-v2/attrs#structtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|--------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1a52c8adace57b2f5a40dba99db1ad4e88) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [out](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1ae87d85671d97e97a7662c7c680fc8884) | `::`[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_sparse_apply_ftrl_v2_1ae9527252eb9b5e39d1cc50fe962c03dd)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1a1acf8e4ced3659d1ceecb99da65de05d)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1ac88538dc7963ebfe36cb619839f53581)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|\n| [UseLocking](#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1a0ca751635f400261887fe95775ea4c48)`(bool x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/sparse-apply-ftrl-v2/attrs#structtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2_1_1_attrs) |\n\n| ### Structs ||\n|--------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::SparseApplyFtrlV2::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/sparse-apply-ftrl-v2/attrs) | Optional attribute setters for [SparseApplyFtrlV2](/versions/r2.2/api_docs/cc/class/tensorflow/ops/sparse-apply-ftrl-v2#classtensorflow_1_1ops_1_1_sparse_apply_ftrl_v2). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### out\n\n```text\n::tensorflow::Output out\n``` \n\nPublic functions\n----------------\n\n### SparseApplyFtrlV2\n\n```gdscript\n SparseApplyFtrlV2(\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 l2_shrinkage,\n ::tensorflow::Input lr_power\n)\n``` \n\n### SparseApplyFtrlV2\n\n```gdscript\n SparseApplyFtrlV2(\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 l2_shrinkage,\n ::tensorflow::Input lr_power,\n const SparseApplyFtrlV2::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### UseLocking\n\n```text\nAttrs UseLocking(\n bool x\n)\n```"]]
