Paddle/paddle/optimizer/adam_optimizer.cc

#include "adam_optimizer.h"
#include <cmath>

namespace paddle {
namespace optimizer {

void AdamOptimizer::Update(const Tensor *gradient) {
  num_sample_passed_ += 1;
  double learning_rate = lr_policy_->LearningRate(num_sample_passed_);
  double coef1 = 1.0 - std::pow(beta_1_, num_sample_passed_);
  double coef2 = 1.0 - std::pow(beta_2_, num_sample_passed_);
  learning_rate *= std::sqrt(coef2) / coef1;
  Tensor &param = *parameter_;
  const Tensor &grad = *gradient;
  Tensor &m = *momentums_;
  Tensor &v = *velocitys_;
  for (size_t i = 0; i < param.size(); ++i) {
    m[i] = beta_1_ * m[i] + (1.0 - beta_1_) * grad[i];
    v[i] = beta_2_ * v[i] + (1.0 - beta_2_) * grad[i] * grad[i];
    param[i] -=
        learning_rate * (m[i] / std::sqrt(v[i] + epsilon_) + decay_ * param[i]);
  }
}

std::string AdamOptimizer::SerializeState() {
  AdamOptimizerState state;
  std::string lr_str = this->lr_policy_->SerializeState();
  state.mutable_lr_state()->ParseFromString(lr_str);
  state.set_num_sample_passed(num_sample_passed_);

  TensorToProto(*parameter_, state.mutable_parameter());
  TensorToProto(*momentums_, state.mutable_momentums());
  TensorToProto(*velocitys_, state.mutable_velocitys());
  return state.SerializeAsString();
}

void AdamOptimizer::DeserializeState(const std::string &str) {
  AdamOptimizerState state;
  state.ParseFromString(str);
  auto lr_state = state.lr_state();
  this->lr_policy_->DeserializeState(lr_state.SerializeAsString());
  num_sample_passed_ = state.num_sample_passed();

  ProtoToTensor(state.parameter(), parameter_);
  ProtoToTensor(state.momentums(), momentums_);
  ProtoToTensor(state.velocitys(), velocitys_);
}
}  // namespace optimizer
}  // namespace paddle