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// Copyright Yangqing Jia 2013
#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
#define CAFFE_OPTIMIZATION_SOLVER_HPP_
#include <vector>
namespace caffe {
template <typename Dtype>
class Solver {
public:
explicit Solver(const SolverParameter& param)
: param_(param) {}
// The main entry of the solver function. In default, iter will be zero. Pass
// in a non-zero iter number to resume training for a pre-trained net.
void Solve(Net<Dtype>* net, char* state_file = NULL);
virtual ~Solver() {}
protected:
// PreSolve is run before any solving iteration starts, allowing one to
// put up some scaffold.
virtual void PreSolve() {}
// Get the update value for the current iteration.
virtual void ComputeUpdateValue() = 0;
// The Solver::Snapshot function implements the basic snapshotting utility
// that stores the learned net. You should implement the SnapshotSolverState()
// function that produces a SolverState protocol buffer that needs to be
// written to disk together with the learned net.
void Snapshot(bool is_final = false);
virtual void SnapshotSolverState(SolverState* state) = 0;
// The Restore function implements how one should restore the solver to a
// previously snapshotted state. You should implement the RestoreSolverState()
// function that restores the state from a SolverState protocol buffer.
void Restore(char* state_file);
virtual void RestoreSolverState(const SolverState& state) = 0;
SolverParameter param_;
int iter_;
Net<Dtype>* net_;
DISABLE_COPY_AND_ASSIGN(Solver);
};
template <typename Dtype>
class SGDSolver : public Solver<Dtype> {
public:
explicit SGDSolver(const SolverParameter& param)
: Solver<Dtype>(param) {}
protected:
virtual void PreSolve();
virtual Dtype GetLearningRate();
virtual void ComputeUpdateValue();
virtual void SnapshotSolverState(SolverState * state);
virtual void RestoreSolverState(const SolverState& state);
// history maintains the historical momentum data.
vector<shared_ptr<Blob<Dtype> > > history_;
};
} // namspace caffe
#endif // CAFFE_OPTIMIZATION_SOLVER_HPP_
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