pmlpp/mlpp/output_layer/output_layer.h

151 lines
3.7 KiB
C++

#ifndef MLPP_OUTPUT_LAYER_H
#define MLPP_OUTPUT_LAYER_H
//
// OutputLayer.hpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "core/math/math_defs.h"
#include "core/string/ustring.h"
#include "core/object/reference.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <map>
#include <string>
#include <vector>
class MLPPOutputLayer : public Reference {
GDCLASS(MLPPOutputLayer, Reference);
public:
int get_n_hidden();
void set_n_hidden(const int val);
MLPPActivation::ActivationFunction get_activation();
void set_activation(const MLPPActivation::ActivationFunction val);
MLPPCost::CostTypes get_cost();
void set_cost(const MLPPCost::CostTypes val);
Ref<MLPPMatrix> get_input();
void set_input(const Ref<MLPPMatrix> &val);
Ref<MLPPVector> get_weights();
void set_weights(const Ref<MLPPVector> &val);
real_t get_bias();
void set_bias(const real_t val);
Ref<MLPPVector> get_z();
void set_z(const Ref<MLPPVector> &val);
Ref<MLPPVector> get_a();
void set_a(const Ref<MLPPVector> &val);
Ref<MLPPVector> get_z_test();
void set_z_test(const Ref<MLPPVector> &val);
Ref<MLPPVector> get_a_test();
void set_a_test(const Ref<MLPPVector> &val);
Ref<MLPPVector> get_delta();
void set_delta(const Ref<MLPPVector> &val);
MLPPReg::RegularizationType get_reg();
void set_reg(const MLPPReg::RegularizationType val);
real_t get_lambda();
void set_lambda(const real_t val);
real_t get_alpha();
void set_alpha(const real_t val);
MLPPUtilities::WeightDistributionType get_weight_init();
void set_weight_init(const MLPPUtilities::WeightDistributionType val);
void forward_pass();
void test(const Ref<MLPPVector> &x);
MLPPOutputLayer(int p_n_hidden, MLPPActivation::ActivationFunction p_activation, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha);
MLPPOutputLayer();
~MLPPOutputLayer();
protected:
static void _bind_methods();
int n_hidden;
MLPPActivation::ActivationFunction activation;
MLPPCost::CostTypes cost;
Ref<MLPPMatrix> input;
Ref<MLPPVector> weights;
real_t bias;
Ref<MLPPVector> z;
Ref<MLPPVector> a;
Ref<MLPPVector> z_test;
Ref<MLPPVector> a_test;
Ref<MLPPVector> delta;
// Regularization Params
MLPPReg::RegularizationType reg;
real_t lambda; /* Regularization Parameter */
real_t alpha; /* This is the controlling param for Elastic Net*/
MLPPUtilities::WeightDistributionType weight_init;
};
class MLPPOldOutputLayer {
public:
MLPPOldOutputLayer(int n_hidden, std::string activation, std::string cost, std::vector<std::vector<real_t>> input, std::string weightInit, std::string reg, real_t lambda, real_t alpha);
int n_hidden;
std::string activation;
std::string cost;
std::vector<std::vector<real_t>> input;
std::vector<real_t> weights;
real_t bias;
std::vector<real_t> z;
std::vector<real_t> a;
std::map<std::string, std::vector<real_t> (MLPPActivation::*)(std::vector<real_t>, bool)> activation_map;
std::map<std::string, real_t (MLPPActivation::*)(real_t, bool)> activationTest_map;
std::map<std::string, real_t (MLPPCost::*)(std::vector<real_t>, std::vector<real_t>)> cost_map;
std::map<std::string, std::vector<real_t> (MLPPCost::*)(std::vector<real_t>, std::vector<real_t>)> costDeriv_map;
real_t z_test;
real_t a_test;
std::vector<real_t> delta;
// Regularization Params
std::string reg;
real_t lambda; /* Regularization Parameter */
real_t alpha; /* This is the controlling param for Elastic Net*/
std::string weightInit;
void forwardPass();
void Test(std::vector<real_t> x);
};
#endif /* OutputLayer_hpp */