sgd_optimizer.cc 1.6 KB
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#include "sgd_optimizer.h"
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#include "serialization.h"
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namespace paddle {
namespace optimizer {

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void SGDOptimizer::Update(const Tensor *gradient) {
  num_sample_passed_ += 1;
  double learning_rate = lr_policy_->LearningRate(num_sample_passed_);
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  float velocity = 0.0;
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  Tensor &param = *parameter_;
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  const Tensor &grad = *gradient;
  Tensor &m = *momentums_;
  for (size_t i = 0; i < param.size(); ++i) {
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    if (momentum_ == 0.0) {
      velocity = -learning_rate * grad[i] - learning_rate * decay_ * param[i];
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    } else {
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      m[i] = momentum_ * m[i] - learning_rate * grad[i] -
             learning_rate * decay_ * param[i];
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      velocity = m[i];
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    }
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    if (nesterov_) {
      param[i] += momentum_ * velocity - learning_rate * grad[i];
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    } else {
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      param[i] += velocity;
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    }
  }
}

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std::string SGDOptimizer::SerializeState() {
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  SGDOptimizerState state;
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  state.set_num_sample_passed(num_sample_passed_);
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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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  TensorToProto(*parameter_, state.mutable_parameter());
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  if (momentum_ != 0.0) TensorToProto(*momentums_, state.mutable_momentums());
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  return state.SerializeAsString();
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}

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void SGDOptimizer::DeserializeState(const std::string &str) {
  SGDOptimizerState 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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  if (momentum_ != 0.0) ProtoToTensor(state.momentums(), momentums_);
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}

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}  // namespace optimizer
}  // namespace paddle