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/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef SRC_MODEL_OPTIMIZER_NESTEROV_H_
#define SRC_MODEL_OPTIMIZER_NESTEROV_H_
#include "singa/model/optimizer.h"
#include <functional>
namespace singa {
void Nesterov::Setup(const OptimizerConf& conf) {
float m = conf.momentum();
SetMomentumGenerator([m](int step) { return m; });
}
// tmp = history;
// history = lr * grad + history * mom
// tmp = (1+mom) * history - tmp * mom;
// value = value - tmp;
void Nesterov::Apply(int epoch, float lr, const string& name, Tensor& grad,
Tensor& value, int step) {
if (grad.empty())
return;
ApplyRegularizerConstraint(epoch, name, value, grad, step);
if (learning_rate_multplier_.find(name) != learning_rate_multplier_.end())
lr *= learning_rate_multplier_.at(name);
if (momentum_generator_) {
float mom = momentum_generator_(step);
if (history_gradient_.find(name) == history_gradient_.end()) {
history_gradient_[name].ResetLike(value);
history_gradient_[name].SetValue(0.0f);
}
Tensor& history = history_gradient_[name];
Tensor tmp = history.Clone();
history *= mom;
Axpy(lr, grad, &history);
tmp *= -mom;
Axpy(1 + mom, history, &tmp);
value -= tmp;
}
}
} // namespace singa
#endif // SRC_MODEL_OPTIMIZER_NESTEROV_H_