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73 lines
3.0 KiB
C++
73 lines
3.0 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 "core/math/math_defs.h"
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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 "../hidden_layer/hidden_layer_old.h"
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#include "../output_layer/output_layer_old.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class MLPPANN {
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public:
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MLPPANN(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet);
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~MLPPANN();
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std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
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real_t modelTest(std::vector<real_t> x);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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void Momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool NAG, bool UI = false);
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void Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool UI = false);
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void Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool UI = false);
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void Adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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void Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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void Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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void AMSGrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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real_t score();
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void save(std::string fileName);
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void setLearningRateScheduler(std::string type, real_t decayConstant);
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void setLearningRateScheduler(std::string type, real_t decayConstant, real_t dropRate);
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void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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private:
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real_t applyLearningRateScheduler(real_t learningRate, real_t decayConstant, real_t epoch, real_t dropRate);
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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void forwardPass();
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void updateParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> computeGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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void UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> outputSet;
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std::vector<real_t> y_hat;
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std::vector<MLPPOldHiddenLayer> network;
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MLPPOldOutputLayer *outputLayer;
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int n;
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int k;
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std::string lrScheduler;
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real_t decayConstant;
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real_t dropRate;
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};
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#endif /* ANN_hpp */ |