Metaheuristics-Template-Library

Metaheuristics Template Library(MTL) is supported Genetic Algorithm, Simulated Annealing and Neural Network.

Attention: It is under development.

##Overview MTL is a header-only library for the meta-heuristics programming.

When you want to use the meta-heuristics, or when you need more abstract and highly reusable architecture, this library would be helpful.

Description

MTL is supporting the following algorithm.

Genetic Algorithm
Simulated Annealing
Neural Network

MTL is a static implementation and works faster than the other library. Instead, the compilation will take more time.

Anyone who is using recently C++ would be able to handle this MTL easily.

Requirement

C++14 Compiler

OpenCV 2.49 (TSP of GA/SA sample)

Install

Please place the MTL repository in the same directory with your project and compile it.

compile option: -std=c++1y

Usage

NeuralNetwork

#include"NNSolver.hpp"
#include<fstream>
#include<vector>
#include<utility>

int main() {
/* Neural Network Learning Solver(NNSolver) requires template arguments            */
/* First template arg: network structure                  */
/* Second template arg: activation function            */

/* sample: static network                              */
/* mtl::FeedForward constructs multilayer perceptron network as statically       */
/* mtl::FeedForward requires variadic template arguments */
/* variadic template arguments :  the number of units on each layer. order: input-output-hide(multi) */
/* below sample constructs 2 unit input layers / 1 unit output layers */
 mtl::NNSolver< mtl::FeedForward<2, 1>, mtl::tanh_af > solver(0.05);

/* training samples */
/* pair.first : input, pair.second : output */
 std::vector< std::pair< std::array<double,2>, std::array<double,1> > > list;
 list.push_back(std::make_pair( std::array<double,2>{1,1}, std::array<double,1>{1} ));
 list.push_back(std::make_pair( std::array<double,2>{1,-1}, std::array<double,1>{-1} ));
 list.push_back(std::make_pair( std::array<double,2>{-1,1}, std::array<double,1>{-1} ));
 list.push_back(std::make_pair( std::array<double,2>{-1,-1}, std::array<double,1>{-1} ));
 
 /* Exec learning */
 /* training function requires learning algorithm */
 solver.training<mtl::Backpropagation>(list);
 
 /* if training succeeded, solveAnswer function returns correctly value...  */
 auto ans = solver.solveAnswer({0, 0});
 
 /* dynamic network version */
 std::vector<mtl::FeedForward_Dy::size_t> network_struct = {2, 4, 1}; // input: 2 hide: 4 output: 1
 mtl::NNSolver< mtl::FeedForward_Dy, mtl::tanh_af > solver(network_struct);
 ...
 solver.training<mtl::Backpropagation>(0.15, list);
 
 /* gpu-accelerated version */
std::vector<mtl::FeedForward_Dy::size_t> network_struct = {2, 4, 1};
mtl::FeedForward_Amp<4> network; //template argument requires max unit size
   network.setStruct(network_struct);

   mtl::NNSolver< mtl::FeedForward_Amp_View<4>, mtl::tanh_af_gpu_accel > solver(network);

   std::vector< std::pair< std::vector<float>, std::vector<float> > > list;
   list.push_back(std::make_pair(std::vector<float>{1, 1}, std::vector<float>{-1}));
   ...
   
   solver.training<mtl::Backpropagation_Gpu_Accel>(0.15, list); //gpe acceleration algorithm
 
 return 0;
}

Licence

MIT

Link

My thesis

Development of OCR system by Gpu-Accelerated Deep-Learning
Original(ja): GPUによって加速化されたディープラーニングによる光学文字認識システムの開発

Author

りやさん(@Riyaaaa_a)

riyaaaaa / metaheuristics-template-library Goto Github PK