50 lines
1.5 KiB
50 lines
1.5 KiB
#include "sgd_optimizer.h"
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#include "serialization.h"
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namespace paddle {
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namespace optimizer {
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void SGDOptimizer::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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float velocity = 0.0;
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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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for (size_t i = 0; i < param.size(); ++i) {
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if (momentum_ == 0.0) {
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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] -
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learning_rate * decay_ * param[i];
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velocity = m[i];
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}
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if (nesterov_) {
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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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}
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}
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const char *SGDOptimizer::SerializeState(int *state_len) {
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SGDOptimizerState state;
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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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if (momentum_ != 0.0) TensorToProto(*momentums_, state.mutable_momentums());
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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 SGDOptimizer::DeserializeState(const std::string &str) {
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SGDOptimizerState state;
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state.ParseFromString(str);
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num_sample_passed_ = state.num_sample_passed();
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ProtoToTensor(state.parameter(), parameter_);
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if (momentum_ != 0.0) ProtoToTensor(state.parameter(), momentums_);
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}
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} // namespace optimizer
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} // namespace paddle
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