pmlpp/mlpp/softmax_net/softmax_net.h

67 lines
2.3 KiB
C++

//
// SoftmaxNet.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
#ifndef SoftmaxNet_hpp
#define SoftmaxNet_hpp
#include <vector>
#include <string>
namespace MLPP {
class SoftmaxNet{
public:
SoftmaxNet(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet, int n_hidden, std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
std::vector<double> modelTest(std::vector<double> x);
std::vector<std::vector<double>> modelSetTest(std::vector<std::vector<double>> X);
void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
void SGD(double learning_rate, int max_epoch, bool UI = 1);
void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
double score();
void save(std::string fileName);
std::vector<std::vector<double>> getEmbeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
private:
double Cost(std::vector<std::vector<double>> y_hat, std::vector<std::vector<double>> y);
std::vector<std::vector<double>> Evaluate(std::vector<std::vector<double>> X);
std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> propagate(std::vector<std::vector<double>> X);
std::vector<double> Evaluate(std::vector<double> x);
std::tuple<std::vector<double>, std::vector<double>> propagate(std::vector<double> x);
void forwardPass();
std::vector<std::vector<double>> inputSet;
std::vector<std::vector<double>> outputSet;
std::vector<std::vector<double>> y_hat;
std::vector<std::vector<double>> weights1;
std::vector<std::vector<double>> weights2;
std::vector<double> bias1;
std::vector<double> bias2;
std::vector<std::vector<double>> z2;
std::vector<std::vector<double>> a2;
int n;
int k;
int n_class;
int n_hidden;
// Regularization Params
std::string reg;
double lambda;
double alpha; /* This is the controlling param for Elastic Net*/
};
}
#endif /* SoftmaxNet_hpp */