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70 lines
2.5 KiB
70 lines
2.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "adadelta_optimizer.h"
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#include <algorithm>
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#include <cmath>
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namespace paddle {
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namespace optimizer {
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void AdadeltaOptimizer::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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Tensor& param = *parameter_;
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const Tensor& grad = *gradient;
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Tensor& accum_g = *accum_gradient_;
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Tensor& accum_d = *accum_delta_;
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Tensor& update_d = *update_delta_;
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for (size_t i = 0; i < param.size(); ++i) {
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accum_g[i] = rho_ * accum_g[i] + (1.0 - rho_) * grad[i] * grad[i];
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update_d[i] = std::sqrt(accum_d[i] + epsilon_) /
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std::sqrt(accum_g[i] + epsilon_) * grad[i];
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accum_d[i] = rho_ * accum_d[i] + (1.0 - rho_) * update_d[i] * update_d[i];
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param[i] -= learning_rate * update_d[i] + learning_rate * decay_ * param[i];
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}
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}
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std::string AdadeltaOptimizer::SerializeState() {
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AdadeltaOptimizerState 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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TensorToProto(*accum_gradient_, state.mutable_accum_gradient());
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TensorToProto(*accum_delta_, state.mutable_accum_delta());
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TensorToProto(*update_delta_, state.mutable_update_delta());
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return state.SerializeAsString();
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}
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void AdadeltaOptimizer::DeserializeState(const std::string& str) {
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AdadeltaOptimizerState state;
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state.ParseFromString(str);
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auto lr_state = state.lr_state();
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this->lr_policy_->DeserializeState(lr_state.SerializeAsString());
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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.accum_gradient(), accum_gradient_);
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ProtoToTensor(state.accum_delta(), accum_delta_);
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ProtoToTensor(state.update_delta(), update_delta_);
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}
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} // namespace optimizer
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} // namespace paddle
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