pmlpp/mlpp/auto_encoder/auto_encoder.h

89 lines
1.8 KiB
C++

#ifndef MLPP_AUTO_ENCODER_H
#define MLPP_AUTO_ENCODER_H
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
class MLPPAutoEncoder : public Reference {
GDCLASS(MLPPAutoEncoder, Reference);
public:
Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
int get_n_hidden();
void set_n_hidden(const int val);
Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
void gradient_descent(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(const String &file_name);
MLPPAutoEncoder(const Ref<MLPPMatrix> &p_input_set, int p_n_hidden);
MLPPAutoEncoder();
~MLPPAutoEncoder();
protected:
real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
struct PropagateVResult {
Ref<MLPPVector> z2;
Ref<MLPPVector> a2;
};
PropagateVResult propagatev(const Ref<MLPPVector> &x);
Ref<MLPPMatrix> evaluatem(const Ref<MLPPMatrix> &X);
struct PropagateMResult {
Ref<MLPPMatrix> z2;
Ref<MLPPMatrix> a2;
};
PropagateMResult propagatem(const Ref<MLPPMatrix> &X);
void forward_pass();
static void _bind_methods();
Ref<MLPPMatrix> _input_set;
Ref<MLPPMatrix> _y_hat;
Ref<MLPPMatrix> _weights1;
Ref<MLPPMatrix> _weights2;
Ref<MLPPVector> _bias1;
Ref<MLPPVector> _bias2;
Ref<MLPPMatrix> _z2;
Ref<MLPPMatrix> _a2;
int _n;
int _k;
int _n_hidden;
bool _initialized;
};
#endif /* AutoEncoder_hpp */