49 lines
1.6 KiB
49 lines
1.6 KiB
#include "adam_optimizer.h"
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#include <cmath>
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namespace paddle {
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namespace optimizer {
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void AdamOptimizer::Update(const Tensor *gradient) {
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num_sample_passed_ += 1;
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double learning_rate = lr_policy_->LearningRate(num_sample_passed_);
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double coef1 = 1.0 - std::pow(beta_1_, num_sample_passed_);
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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 ¶m = *parameter_;
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const Tensor &grad = *gradient;
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Tensor &m = *momentums_;
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Tensor &v = *velocitys_;
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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];
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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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}
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const char *AdamOptimizer::SerializeState(int *state_len) {
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AdamOptimizerState state;
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// TODO(zhihong) : add lr_policy serialization
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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());
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TensorToProto(*velocitys_, state.mutable_velocitys());
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auto str = state.SerializeAsString();
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*state_len = str.size();
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return str.c_str();
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}
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void AdamOptimizer::DeserializeState(const std::string &str) {
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AdamOptimizerState state;
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state.ParseFromString(str);
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// TODO(zhihong) : add lr_policy DeserializeState
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num_sample_passed_ = state.num_sample_passed();
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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
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} // namespace paddle
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