mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-13 13:57:19 +01:00
103 lines
3.8 KiB
C++
103 lines
3.8 KiB
C++
#ifndef MLPP_ANN_H
|
|
#define MLPP_ANN_H
|
|
|
|
|
|
|
|
#include "core/math/math_defs.h"
|
|
|
|
#include "core/object/reference.h"
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_tensor3.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
#include "../hidden_layer/hidden_layer.h"
|
|
#include "../output_layer/output_layer.h"
|
|
|
|
#include "../activation/activation.h"
|
|
#include "../cost/cost.h"
|
|
#include "../regularization/reg.h"
|
|
#include "../utilities/utilities.h"
|
|
|
|
class MLPPANN : public Reference {
|
|
GDCLASS(MLPPANN, Reference);
|
|
|
|
public:
|
|
enum SchedulerType {
|
|
SCHEDULER_TYPE_NONE = 0,
|
|
SCHEDULER_TYPE_TIME,
|
|
SCHEDULER_TYPE_EPOCH,
|
|
SCHEDULER_TYPE_STEP,
|
|
SCHEDULER_TYPE_EXPONENTIAL,
|
|
};
|
|
|
|
public:
|
|
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
|
|
real_t 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);
|
|
void momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool nag, bool ui = false);
|
|
void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
|
|
void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
|
|
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);
|
|
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);
|
|
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);
|
|
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);
|
|
|
|
real_t score();
|
|
void save(const String &file_name);
|
|
|
|
void set_learning_rate_scheduler(SchedulerType type, real_t decay_constant);
|
|
void set_learning_rate_scheduler_drop(SchedulerType type, real_t decay_constant, real_t drop_rate);
|
|
|
|
void add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
|
void add_output_layer(MLPPActivation::ActivationFunction activation, MLPPCost::CostTypes loss, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
|
|
|
MLPPANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set);
|
|
|
|
MLPPANN();
|
|
~MLPPANN();
|
|
|
|
protected:
|
|
real_t apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate);
|
|
|
|
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
|
|
void forward_pass();
|
|
void update_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
|
|
|
struct ComputeGradientsResult {
|
|
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad;
|
|
Ref<MLPPVector> output_w_grad;
|
|
|
|
ComputeGradientsResult() {
|
|
output_w_grad.instance();
|
|
}
|
|
};
|
|
|
|
ComputeGradientsResult compute_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &_output_set);
|
|
|
|
void print_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &p_output_set);
|
|
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _input_set;
|
|
Ref<MLPPVector> _output_set;
|
|
Ref<MLPPVector> _y_hat;
|
|
|
|
Vector<Ref<MLPPHiddenLayer>> _network;
|
|
Ref<MLPPOutputLayer> _output_layer;
|
|
|
|
int _n;
|
|
int _k;
|
|
|
|
SchedulerType _lr_scheduler;
|
|
real_t _decay_constant;
|
|
real_t _drop_rate;
|
|
};
|
|
|
|
VARIANT_ENUM_CAST(MLPPANN::SchedulerType);
|
|
|
|
#endif /* ANN_hpp */ |