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67 lines
2.3 KiB
C
67 lines
2.3 KiB
C
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//
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// SoftmaxNet.hpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#ifndef SoftmaxNet_hpp
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#define SoftmaxNet_hpp
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#include <vector>
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#include <string>
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namespace MLPP {
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class SoftmaxNet{
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public:
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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);
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std::vector<double> modelTest(std::vector<double> x);
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std::vector<std::vector<double>> modelSetTest(std::vector<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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double score();
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void save(std::string fileName);
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std::vector<std::vector<double>> getEmbeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
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private:
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double Cost(std::vector<std::vector<double>> y_hat, std::vector<std::vector<double>> y);
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std::vector<std::vector<double>> Evaluate(std::vector<std::vector<double>> X);
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> propagate(std::vector<std::vector<double>> X);
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std::vector<double> Evaluate(std::vector<double> x);
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std::tuple<std::vector<double>, std::vector<double>> propagate(std::vector<double> x);
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void forwardPass();
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std::vector<std::vector<double>> inputSet;
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std::vector<std::vector<double>> outputSet;
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std::vector<std::vector<double>> y_hat;
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std::vector<std::vector<double>> weights1;
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std::vector<std::vector<double>> weights2;
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std::vector<double> bias1;
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std::vector<double> bias2;
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std::vector<std::vector<double>> z2;
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std::vector<std::vector<double>> a2;
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int n;
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int k;
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int n_class;
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int n_hidden;
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// Regularization Params
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std::string reg;
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double lambda;
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double alpha; /* This is the controlling param for Elastic Net*/
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};
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}
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#endif /* SoftmaxNet_hpp */
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