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rmsprop.cc
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/
rmsprop.cc
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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_ADAGRAD_H_
#define SRC_MODEL_OPTIMIZER_ADAGRAD_H_
#include "singa/model/optimizer.h"
#include <functional>
namespace singa {
void RMSProp::Setup(const OptimizerConf& conf) {
delta_ = conf.delta();
rho_ = conf.rho();
}
// history = history * rho + grad * grad * (1 - rho)
// value = value - lr * grad / sqrt(history + delta)
void RMSProp::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 (history_gradient_.find(name) == history_gradient_.end()) {
history_gradient_[name].ResetLike(value);
history_gradient_[name].SetValue(0.0f);
}
Tensor& history = history_gradient_[name];
history *= rho_;
Tensor tmp = Square(grad);
Axpy(1 - rho_, tmp, &history);
Sqrt(history + delta_, &tmp);
Div(grad, tmp, &tmp);
Axpy(-lr, tmp, &value);
}
} // namespace singa
#endif // SRC_MODEL_OPTIMIZER_ADAGRAD_H_