:github_url: https://github.com/pytorch/pytorch


.. _program_listing_file_torch_csrc_api_include_torch_optim_rmsprop.h:

Program Listing for File rmsprop.h
==================================

|exhale_lsh| :ref:`Return to documentation for file <file_torch_csrc_api_include_torch_optim_rmsprop.h>` (``torch/csrc/api/include/torch/optim/rmsprop.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include <torch/nn/module.h>
   #include <torch/optim/optimizer.h>
   #include <torch/optim/serialize.h>
   #include <torch/serialize/archive.h>
   #include <torch/types.h>
   
   #include <functional>
   #include <memory>
   #include <string>
   #include <utility>
   #include <vector>
   
   namespace torch::serialize {
   class OutputArchive;
   class InputArchive;
   } // namespace torch::serialize
   
   namespace torch::optim {
   
   struct TORCH_API RMSpropOptions
       : public OptimizerCloneableOptions<RMSpropOptions> {
     RMSpropOptions(double lr = 1e-2);
     TORCH_ARG(double, lr) = 1e-2;
     TORCH_ARG(double, alpha) = 0.99;
     TORCH_ARG(double, eps) = 1e-8;
     TORCH_ARG(double, weight_decay) = 0;
     TORCH_ARG(double, momentum) = 0;
     TORCH_ARG(bool, centered) = false;
   
    public:
     void serialize(torch::serialize::InputArchive& archive) override;
     void serialize(torch::serialize::OutputArchive& archive) const override;
     TORCH_API friend bool operator==(
         const RMSpropOptions& lhs,
         const RMSpropOptions& rhs);
     double get_lr() const override;
     void set_lr(const double lr) override;
   };
   
   struct TORCH_API RMSpropParamState
       : public OptimizerCloneableParamState<RMSpropParamState> {
     TORCH_ARG(int64_t, step) = 0;
     TORCH_ARG(torch::Tensor, square_avg);
     TORCH_ARG(torch::Tensor, momentum_buffer);
     TORCH_ARG(torch::Tensor, grad_avg);
   
    public:
     void serialize(torch::serialize::InputArchive& archive) override;
     void serialize(torch::serialize::OutputArchive& archive) const override;
     TORCH_API friend bool operator==(
         const RMSpropParamState& lhs,
         const RMSpropParamState& rhs);
   };
   
   class TORCH_API RMSprop : public Optimizer {
    public:
     explicit RMSprop(
         const std::vector<OptimizerParamGroup>& param_groups,
         RMSpropOptions defaults = {})
         : Optimizer(param_groups, std::make_unique<RMSpropOptions>(defaults)) {
       TORCH_CHECK(defaults.lr() >= 0, "Invalid learning rate: ", defaults.lr());
       TORCH_CHECK(defaults.eps() >= 0, "Invalid epsilon value: ", defaults.eps());
       TORCH_CHECK(
           defaults.momentum() >= 0,
           "Invalid momentum value: ",
           defaults.momentum());
       TORCH_CHECK(
           defaults.weight_decay() >= 0,
           "Invalid weight_decay value: ",
           defaults.weight_decay());
       TORCH_CHECK(
           defaults.alpha() >= 0, "Invalid alpha value: ", defaults.alpha());
     }
   
     explicit RMSprop(std::vector<Tensor> params, RMSpropOptions defaults = {})
         : RMSprop({OptimizerParamGroup(std::move(params))}, std::move(defaults)) {
     }
   
     torch::Tensor step(LossClosure closure = nullptr) override;
     void save(serialize::OutputArchive& archive) const override;
     void load(serialize::InputArchive& archive) override;
   
    private:
     template <typename Self, typename Archive>
     static void serialize(Self& self, Archive& archive) {
       _TORCH_OPTIM_SERIALIZE_WITH_TEMPLATE_ARG(RMSprop);
     }
   };
   } // namespace torch::optim