FM / optim

/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/optim/

/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/optim

Name	Size	Mode	Actions
schedulers/	-	0755	rm
adagrad.h	3239	0644	edit dl rm
adam.h	2951	0644	edit dl rm
adamw.h	2973	0644	edit dl rm
lbfgs.h	3560	0644	edit dl rm
optimizer.h	7442	0644	edit dl rm
rmsprop.h	2948	0644	edit dl rm
serialize.h	10935	0644	edit dl rm
sgd.h	2667	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/optim/rmsprop.h (2948B)

#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 <vector>

namespace torch {
namespace serialize {
class OutputArchive;
class InputArchive;
} // namespace serialize
} // namespace torch

namespace torch {
namespace 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);
  ~RMSpropOptions() override = default;
  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);
  ~RMSpropParamState() override = default;
};

class TORCH_API RMSprop : public Optimizer {
 public:
  explicit RMSprop(std::vector<OptimizerParamGroup> param_groups,
      RMSpropOptions defaults = {}) : Optimizer(std::move(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,
      // NOLINTNEXTLINE(performance-move-const-arg)
      RMSpropOptions defaults = {}) : RMSprop({std::move(OptimizerParamGroup(params))}, 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 optim
} // namespace torch

#pragma once #include #include #include #include #include #include #include #include #include namespace torch { namespace serialize { class OutputArchive; class InputArchive; } // namespace serialize } // namespace torch namespace torch { namespace optim { struct TORCH_API RMSpropOptions : public OptimizerCloneableOptions { 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); ~RMSpropOptions() override = default; double get_lr() const override; void set_lr(const double lr) override; }; struct TORCH_API RMSpropParamState : public OptimizerCloneableParamState { 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); ~RMSpropParamState() override = default; }; class TORCH_API RMSprop : public Optimizer { public: explicit RMSprop(std::vector param_groups, RMSpropOptions defaults = {}) : Optimizer(std::move(param_groups), std::make_unique(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 params, // NOLINTNEXTLINE(performance-move-const-arg) RMSpropOptions defaults = {}) : RMSprop({std::move(OptimizerParamGroup(params))}, defaults) {} torch::Tensor step(LossClosure closure = nullptr) override; void save(serialize::OutputArchive& archive) const override; void load(serialize::InputArchive& archive) override; private: template static void serialize(Self& self, Archive& archive) { _TORCH_OPTIM_SERIALIZE_WITH_TEMPLATE_ARG(RMSprop); } }; } // namespace optim } // namespace torch