/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/optim
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/optim/adagrad.h (3239B)
#pragma once
#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 AdagradOptions : public OptimizerCloneableOptions {
AdagradOptions(double lr = 1e-2);
TORCH_ARG(double, lr) = 1e-2;
TORCH_ARG(double, lr_decay) = 0;
TORCH_ARG(double, weight_decay) = 0;
TORCH_ARG(double, initial_accumulator_value) = 0;
TORCH_ARG(double, eps) = 1e-10;
public:
void serialize(torch::serialize::InputArchive& archive) override;
void serialize(torch::serialize::OutputArchive& archive) const override;
TORCH_API friend bool operator==(const AdagradOptions& lhs, const AdagradOptions& rhs);
~AdagradOptions() override = default;
double get_lr() const override;
void set_lr(const double lr) override;
};
struct TORCH_API AdagradParamState : public OptimizerCloneableParamState {
TORCH_ARG(torch::Tensor, sum);
TORCH_ARG(int64_t, step) = 0;
public:
void serialize(torch::serialize::InputArchive& archive) override;
void serialize(torch::serialize::OutputArchive& archive) const override;
TORCH_API friend bool operator==(const AdagradParamState& lhs, const AdagradParamState& rhs);
~AdagradParamState() override = default;
};
class TORCH_API Adagrad : public Optimizer {
public:
explicit Adagrad(std::vector param_groups,
AdagradOptions defaults = {}) : Optimizer(std::move(param_groups), std::make_unique(defaults)) {
TORCH_CHECK(defaults.lr() >= 0, "Invalid learning rate: ", defaults.lr());
TORCH_CHECK(defaults.lr_decay() >= 0, "Invalid lr_decay value: ", defaults.lr_decay());
TORCH_CHECK(defaults.weight_decay() >= 0, "Invalid weight_decay value: ", defaults.weight_decay());
TORCH_CHECK(defaults.initial_accumulator_value() >= 0, "Invalid initial_accumulator_value value: ", defaults.initial_accumulator_value());
TORCH_CHECK(defaults.eps() >= 0, "Invalid epsilon value: ", defaults.eps());
for (const auto& group : param_groups_) {
for (const auto& p : group.params()) {
auto state = std::make_unique();
state->step(0);
state->sum(torch::full_like(p.data(), defaults.initial_accumulator_value(), at::MemoryFormat::Preserve));
state_[c10::guts::to_string(p.unsafeGetTensorImpl())] = std::move(state);
}
}
}
explicit Adagrad(
std::vector params,
// NOLINTNEXTLINE(performance-move-const-arg)
AdagradOptions defaults = {}) : Adagrad({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(Adagrad);
}
};
} // namespace optim
} // namespace torch