Adam.h

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/*
 * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
#ifndef __ONERT_BACKEND_TRAIN_OPTIMIZER_ADAM_H__
#define __ONERT_BACKEND_TRAIN_OPTIMIZER_ADAM_H__
 
#include "exec/train/optimizer/Optimizer.h"
 
#include "backend/basic/Tensor.h"
#include "ir/OperandIndexMap.h"
 
namespace onert::backend::train::optimizer
{
 
class Adam : public exec::train::optimizer::Optimizer
{
public:
  using UpdateFactors = exec::train::optimizer::UpdateFactors;
 
public:
  struct Property
  {
    double beta1{0.9};
    double beta2{0.999};
    double epsilon{1e-07};
  };
  struct Property {…};
 
public:
  explicit Adam() : _props{}, _learning_rate{0.001} {}
  explicit Adam(const Property &props) : _props{props}, _learning_rate{0.001} {}
  explicit Adam(double lr) : _props{}, _learning_rate{lr} {}
  explicit Adam(const Property &props, double lr) : _props{props}, _learning_rate{lr} {}
 
public:
  std::string name() const override { return std::string{"Adam"}; }
 
  double getLearningRate(uint32_t training_step) const override;
 
  virtual uint32_t getVarCount() const override { return 2; };
 
  void applyGradient(const UpdateFactors &factors) const override;
 
private:
  Property _props;
  double _learning_rate;
};
class Adam : public exec::train::optimizer::Optimizer {…};
 
} // namespace onert::backend::train::optimizer
 
#endif // __ONERT_BACKEND_TRAIN_OPTIMIZER_ADAM_H__