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flujo tensor:: operaciones:: ResourceSparseApplyRMSProp
#include <training_ops.h>
Actualice '*var' según el algoritmo RMSProp.
Resumen
Tenga en cuenta que en una implementación densa de este algoritmo, ms y mom se actualizarán incluso si el grad es cero, pero en esta implementación escasa, ms y mom no se actualizarán en iteraciones durante las cuales el grad sea cero.
cuadrado_medio = decaimiento * cuadrado_medio + (1-decaimiento) * gradiente ** 2 Delta = tasa_de_aprendizaje * gradiente / sqrt (cuadrado_medio + épsilon)
ms <- rho * ms_{t-1} + (1-rho) * grad * grad mamá <- impulso * mamá_{t-1} + lr * grad / sqrt(ms + épsilon) var <- var - mamá
Argumentos:
- alcance: un objeto de alcance
- var: debe ser de una variable().
- ms: debe ser de una variable().
- mamá: Debería ser de una Variable().
- lr: Factor de escala. Debe ser un escalar.
- rho: tasa de desintegración. Debe ser un escalar.
- épsilon: término de cresta. Debe ser un escalar.
- grad: El gradiente.
- índices: un vector de índices en la primera dimensión de var, ms y mom.
Atributos opcionales (ver Attrs
):
- use_locking: si es
True
, la actualización de los tensores var, ms y mom está protegida por un bloqueo; de lo contrario, el comportamiento no está definido, pero puede presentar menos contención.
Devoluciones:
Constructores y destructores |
---|
ResourceSparseApplyRMSProp (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input ms, :: tensorflow::Input mom, :: tensorflow::Input lr, :: tensorflow::Input rho, :: tensorflow::Input momentum, :: tensorflow::Input epsilon, :: tensorflow::Input grad, :: tensorflow::Input indices)
|
ResourceSparseApplyRMSProp (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input ms, :: tensorflow::Input mom, :: tensorflow::Input lr, :: tensorflow::Input rho, :: tensorflow::Input momentum, :: tensorflow::Input epsilon, :: tensorflow::Input grad, :: tensorflow::Input indices, const ResourceSparseApplyRMSProp::Attrs & attrs) |
Atributos públicos
Funciones públicas
ResourceSparseApplyRMSProp
ResourceSparseApplyRMSProp(
const ::tensorflow::Scope & scope,
::tensorflow::Input var,
::tensorflow::Input ms,
::tensorflow::Input mom,
::tensorflow::Input lr,
::tensorflow::Input rho,
::tensorflow::Input momentum,
::tensorflow::Input epsilon,
::tensorflow::Input grad,
::tensorflow::Input indices,
const ResourceSparseApplyRMSProp::Attrs & attrs
)
operador::tensorflow::Operación
operator::tensorflow::Operation() 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: 2025-07-25 (UTC).
[null,null,["Última actualización: 2025-07-25 (UTC)."],[],[],null,["# tensorflow::ops::ResourceSparseApplyRMSProp Class Reference\n\ntensorflow::ops::ResourceSparseApplyRMSProp\n===========================================\n\n`#include \u003ctraining_ops.h\u003e`\n\nUpdate '\\*var' according to the RMSProp algorithm.\n\nSummary\n-------\n\nNote that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero.\n\nmean_square = decay \\* mean_square + (1-decay) \\* gradient \\*\\* 2 Delta = learning_rate \\* gradient / sqrt(mean_square + epsilon)\n\nms \\\u003c- rho \\* ms_{t-1} + (1-rho) \\* grad \\* grad mom \\\u003c- momentum \\* mom_{t-1} + lr \\* grad / sqrt(ms + epsilon) var \\\u003c- var - mom\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- ms: Should be from a Variable().\n- mom: Should be from a Variable().\n- lr: Scaling factor. Must be a scalar.\n- rho: Decay rate. Must be a scalar.\n- epsilon: Ridge term. Must be a scalar.\n- grad: The gradient.\n- indices: A vector of indices into the first dimension of var, ms and mom.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-r-m-s-prop/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1_1_attrs)):\n\n- use_locking: If `True`, updating of the var, ms, and mom tensors is 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| [ResourceSparseApplyRMSProp](#classtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1aff53e99b8e6dc505fb48976158d11f39)`(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)` ms, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` mom, ::`[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)` rho, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` momentum, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` epsilon, ::`[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)` ||\n| [ResourceSparseApplyRMSProp](#classtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1aba358e7d7c7d78bfcdc5eaed2530fa16)`(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)` ms, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` mom, ::`[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)` rho, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` momentum, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` epsilon, ::`[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, const `[ResourceSparseApplyRMSProp::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-r-m-s-prop/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1abcff2e138b5825b7fe66d8d9e68c9b7f) | [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_r_m_s_prop_1ae4f96774d9e75c094f13f6ea8d5ca0b6)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|-------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [UseLocking](#classtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1a19ab4ce72ae4d3db4e8b7daa0c72ff60)`(bool x)` | [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-r-m-s-prop/attrs#structtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop_1_1_attrs) |\n\n| ### Structs ||\n|------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::ResourceSparseApplyRMSProp::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/resource-sparse-apply-r-m-s-prop/attrs) | Optional attribute setters for [ResourceSparseApplyRMSProp](/versions/r1.15/api_docs/cc/class/tensorflow/ops/resource-sparse-apply-r-m-s-prop#classtensorflow_1_1ops_1_1_resource_sparse_apply_r_m_s_prop). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### ResourceSparseApplyRMSProp\n\n```gdscript\n ResourceSparseApplyRMSProp(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input var,\n ::tensorflow::Input ms,\n ::tensorflow::Input mom,\n ::tensorflow::Input lr,\n ::tensorflow::Input rho,\n ::tensorflow::Input momentum,\n ::tensorflow::Input epsilon,\n ::tensorflow::Input grad,\n ::tensorflow::Input indices\n)\n``` \n\n### ResourceSparseApplyRMSProp\n\n```gdscript\n ResourceSparseApplyRMSProp(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input var,\n ::tensorflow::Input ms,\n ::tensorflow::Input mom,\n ::tensorflow::Input lr,\n ::tensorflow::Input rho,\n ::tensorflow::Input momentum,\n ::tensorflow::Input epsilon,\n ::tensorflow::Input grad,\n ::tensorflow::Input indices,\n const ResourceSparseApplyRMSProp::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```"]]