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68 lines
2.8 KiB
C++
68 lines
2.8 KiB
C++
#ifndef MLPP_ANN_H
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#define MLPP_ANN_H
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//
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// ANN.hpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class ANN {
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public:
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ANN(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet);
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~ANN();
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std::vector<double> modelSetTest(std::vector<std::vector<double>> X);
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double modelTest(std::vector<double> x);
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void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
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void SGD(double learning_rate, int max_epoch, bool UI = 1);
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void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
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void Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool NAG, bool UI = 1);
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void Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI = 1);
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void Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI = 1);
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void Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
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void Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
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void Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
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void AMSGrad(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1);
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double score();
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void save(std::string fileName);
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void setLearningRateScheduler(std::string type, double decayConstant);
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void setLearningRateScheduler(std::string type, double decayConstant, double dropRate);
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void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
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void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
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private:
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double applyLearningRateScheduler(double learningRate, double decayConstant, double epoch, double dropRate);
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double Cost(std::vector<double> y_hat, std::vector<double> y);
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void forwardPass();
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void updateParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate);
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std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> computeGradients(std::vector<double> y_hat, std::vector<double> outputSet);
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void UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet);
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std::vector<std::vector<double>> inputSet;
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std::vector<double> outputSet;
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std::vector<double> y_hat;
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std::vector<HiddenLayer> network;
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OutputLayer *outputLayer;
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int n;
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int k;
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std::string lrScheduler;
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double decayConstant;
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double dropRate;
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};
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#endif /* ANN_hpp */ |