adam_optimizer.cc 1.7 KB
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#include "adam_optimizer.h"
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#include <cmath>
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namespace paddle {
namespace optimizer {

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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_);
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  learning_rate *= std::sqrt(coef2) / coef1;
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  Tensor &param = *parameter_;
  const Tensor &grad = *gradient;
  Tensor &m = *momentums_;
  Tensor &v = *velocitys_;
  for (size_t i = 0; i < param.size(); ++i) {
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    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];
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    param[i] -=
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        learning_rate * (m[i] / std::sqrt(v[i] + epsilon_) + decay_ * param[i]);
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  }
}
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std::string AdamOptimizer::SerializeState() {
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  AdamOptimizerState state;
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  std::string lr_str = this->lr_policy_->SerializeState();
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  state.mutable_lr_state()->ParseFromString(lr_str);
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  state.set_num_sample_passed(num_sample_passed_);
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  TensorToProto(*parameter_, state.mutable_parameter());
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  TensorToProto(*momentums_, state.mutable_momentums());
  TensorToProto(*velocitys_, state.mutable_velocitys());
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  return state.SerializeAsString();
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}

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

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