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