pmlpp/mlpp/ann/ann.h

#ifndef MLPP_ANN_H
#define MLPP_ANN_H

//
//  ANN.hpp
//
//  Created by Marc Melikyan on 11/4/20.
//

#include "../hidden_layer/hidden_layer.h"
#include "../output_layer/output_layer.h"

#include <string>
#include <tuple>
#include <vector>

namespace MLPP {

class ANN {
public:
	ANN(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet);
	~ANN();
	std::vector<double> modelSetTest(std::vector<std::vector<double>> X);
	double modelTest(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);
	void Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool NAG, bool UI = 1);
	void Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI = 1);
	void Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI = 1);
	void Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
	void Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
	void Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
	void AMSGrad(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
	double score();
	void save(std::string fileName);

	void setLearningRateScheduler(std::string type, double decayConstant);
	void setLearningRateScheduler(std::string type, double decayConstant, double dropRate);

	void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
	void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);

private:
	double applyLearningRateScheduler(double learningRate, double decayConstant, double epoch, double dropRate);

	double Cost(std::vector<double> y_hat, std::vector<double> y);

	void forwardPass();
	void updateParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate);
	std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> computeGradients(std::vector<double> y_hat, std::vector<double> outputSet);

	void UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet);

	std::vector<std::vector<double>> inputSet;
	std::vector<double> outputSet;
	std::vector<double> y_hat;

	std::vector<HiddenLayer> network;
	OutputLayer *outputLayer;

	int n;
	int k;

	std::string lrScheduler;
	double decayConstant;
	double dropRate;
};
} //namespace MLPP

#endif /* ANN_hpp */