pmlpp/mlpp/mlp/mlp.cpp
2023-04-29 15:07:30 +02:00

506 lines
14 KiB
C++

//
// MLP.cpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "mlp.h"
#include "core/log/logger.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
#include <iostream>
#include <random>
Ref<MLPPMatrix> MLPPMLP::get_input_set() {
return _input_set;
}
void MLPPMLP::set_input_set(const Ref<MLPPMatrix> &val) {
_input_set = val;
_initialized = false;
}
Ref<MLPPVector> MLPPMLP::get_output_set() {
return _output_set;
}
void MLPPMLP::set_output_set(const Ref<MLPPVector> &val) {
_output_set = val;
_initialized = false;
}
int MLPPMLP::get_n_hidden() {
return _n_hidden;
}
void MLPPMLP::set_n_hidden(const int val) {
_n_hidden = val;
_initialized = false;
}
real_t MLPPMLP::get_lambda() {
return _lambda;
}
void MLPPMLP::set_lambda(const real_t val) {
_lambda = val;
_initialized = false;
}
real_t MLPPMLP::get_alpha() {
return _alpha;
}
void MLPPMLP::set_alpha(const real_t val) {
_alpha = val;
_initialized = false;
}
MLPPReg::RegularizationType MLPPMLP::get_reg() {
return _reg;
}
void MLPPMLP::set_reg(const MLPPReg::RegularizationType val) {
_reg = val;
_initialized = false;
}
Ref<MLPPVector> MLPPMLP::model_set_test(const Ref<MLPPMatrix> &X) {
return evaluatem(X);
}
real_t MLPPMLP::model_test(const Ref<MLPPVector> &x) {
return evaluatev(x);
}
void MLPPMLP::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
_y_hat->fill(0);
forward_pass();
while (true) {
cost_prev = cost(_y_hat, _output_set);
// Calculating the errors
Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
// Calculating the weight/bias gradients for layer 2
Ref<MLPPVector> D2_1 = alg.mat_vec_multnv(alg.transposenm(_a2), error);
// weights and bias updation for layer 2
_weights2->set_from_mlpp_vector(alg.subtractionnv(_weights2, alg.scalar_multiplynv(learning_rate / static_cast<real_t>(_n), D2_1)));
_weights2->set_from_mlpp_vector(regularization.reg_weightsv(_weights2, _lambda, _alpha, _reg));
_bias2 -= learning_rate * alg.sum_elementsv(error) / static_cast<real_t>(_n);
// Calculating the weight/bias for layer 1
Ref<MLPPMatrix> D1_1 = alg.outer_product(error, _weights2);
Ref<MLPPMatrix> D1_2 = alg.hadamard_productnm(alg.transposenm(D1_1), avn.sigmoid_derivm(_z2));
Ref<MLPPMatrix> D1_3 = alg.matmultnm(alg.transposenm(_input_set), D1_2);
// weight an bias updation for layer 1
_weights1->set_from_mlpp_matrix(alg.subtractionnm(_weights1, alg.scalar_multiplynm(learning_rate / _n, D1_3)));
_weights1->set_from_mlpp_matrix(regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg));
_bias1->set_from_mlpp_vector(alg.subtract_matrix_rowsnv(_bias1, alg.scalar_multiplynm(learning_rate / _n, D1_2)));
forward_pass();
// UI PORTION
if (UI) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(_weights1, _bias1);
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(_weights2, _bias2);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
}
void MLPPMLP::sgd(real_t learning_rate, int max_epoch, bool UI) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_int_distribution<int> distribution(0, int(_n - 1));
Ref<MLPPVector> input_set_row_tmp;
input_set_row_tmp.instance();
input_set_row_tmp->resize(_input_set->size().x);
Ref<MLPPVector> output_set_row_tmp;
output_set_row_tmp.instance();
output_set_row_tmp->resize(1);
Ref<MLPPVector> y_hat_row_tmp;
y_hat_row_tmp.instance();
y_hat_row_tmp->resize(1);
Ref<MLPPVector> lz2;
lz2.instance();
Ref<MLPPVector> la2;
la2.instance();
while (true) {
int output_Index = distribution(generator);
_input_set->row_get_into_mlpp_vector(output_Index, input_set_row_tmp);
real_t output_element = _output_set->element_get(output_Index);
output_set_row_tmp->element_set(0, output_element);
real_t ly_hat = evaluatev(input_set_row_tmp);
y_hat_row_tmp->element_set(0, ly_hat);
propagatev(input_set_row_tmp, lz2, la2);
cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
real_t error = ly_hat - output_element;
// Weight updation for layer 2
Ref<MLPPVector> D2_1 = alg.scalar_multiplynv(error, la2);
_weights2->set_from_mlpp_vector(alg.subtractionnv(_weights2, alg.scalar_multiplynv(learning_rate, D2_1)));
_weights2->set_from_mlpp_vector(regularization.reg_weightsv(_weights2, _lambda, _alpha, _reg));
// Bias updation for layer 2
_bias2 -= learning_rate * error;
// Weight updation for layer 1
Ref<MLPPVector> D1_1 = alg.scalar_multiplynv(error, _weights2);
Ref<MLPPVector> D1_2 = alg.hadamard_productnv(D1_1, avn.sigmoid_derivv(lz2));
Ref<MLPPMatrix> D1_3 = alg.outer_product(input_set_row_tmp, D1_2);
_weights1->set_from_mlpp_matrix(alg.subtractionnm(_weights1, alg.scalar_multiplynm(learning_rate, D1_3)));
_weights1->set_from_mlpp_matrix(regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg));
// Bias updation for layer 1
_bias1->set_from_mlpp_vector(alg.subtractionnv(_bias1, alg.scalar_multiplynv(learning_rate, D1_2)));
ly_hat = evaluatev(input_set_row_tmp);
if (UI) {
MLPPUtilities::cost_info(epoch, cost_prev, cost_prev);
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(_weights1, _bias1);
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(_weights2, _bias2);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forward_pass();
}
void MLPPMLP::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
Ref<MLPPMatrix> lz2;
lz2.instance();
Ref<MLPPMatrix> la2;
la2.instance();
// Creating the mini-batches
int n_mini_batch = _n / mini_batch_size;
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
Ref<MLPPMatrix> current_input = batches.input_sets[i];
Ref<MLPPVector> current_output = batches.output_sets[i];
Ref<MLPPVector> ly_hat = evaluatem(current_input);
propagatem(current_input, lz2, la2);
cost_prev = cost(ly_hat, current_output);
// Calculating the errors
Ref<MLPPVector> error = alg.subtractionnv(ly_hat, current_output);
// Calculating the weight/bias gradients for layer 2
Ref<MLPPVector> D2_1 = alg.mat_vec_multnv(alg.transposenm(la2), error);
real_t lr_d_cos = learning_rate / static_cast<real_t>(current_output->size());
// weights and bias updation for layser 2
_weights2->set_from_mlpp_vector(alg.subtractionnv(_weights2, alg.scalar_multiplynv(lr_d_cos, D2_1)));
_weights2->set_from_mlpp_vector(regularization.reg_weightsv(_weights2, _lambda, _alpha, _reg));
// Calculating the bias gradients for layer 2
real_t b_gradient = alg.sum_elementsv(error);
// Bias Updation for layer 2
_bias2 -= learning_rate * b_gradient / current_output->size();
//Calculating the weight/bias for layer 1
Ref<MLPPMatrix> D1_1 = alg.outer_product(error, _weights2);
Ref<MLPPMatrix> D1_2 = alg.hadamard_productnm(D1_1, avn.sigmoid_derivm(lz2));
Ref<MLPPMatrix> D1_3 = alg.matmultnm(alg.transposenm(current_input), D1_2);
// weight an bias updation for layer 1
_weights1->set_from_mlpp_matrix(alg.subtractionnm(_weights1, alg.scalar_multiplynm(lr_d_cos, D1_3)));
_weights1->set_from_mlpp_matrix(regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg));
_bias1->set_from_mlpp_vector(alg.subtract_matrix_rowsnv(_bias1, alg.scalar_multiplynm(lr_d_cos, D1_2)));
_y_hat = evaluatem(current_input);
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost(ly_hat, current_output));
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(_weights1, _bias1);
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(_weights2, _bias2);
}
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forward_pass();
}
real_t MLPPMLP::score() {
MLPPUtilities util;
return util.performance_vec(_y_hat, _output_set);
}
void MLPPMLP::save(const String &fileName) {
ERR_FAIL_COND(!_initialized);
MLPPUtilities util;
//util.saveParameters(fileName, weights1, bias1, 0, 1);
//util.saveParameters(fileName, weights2, bias2, 1, 2);
}
bool MLPPMLP::is_initialized() {
return _initialized;
}
void MLPPMLP::initialize() {
if (_initialized) {
return;
}
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid() || _n_hidden == 0);
_n = _input_set->size().y;
_k = _input_set->size().x;
MLPPActivation avn;
_y_hat->resize(_n);
MLPPUtilities util;
_weights1->resize(Size2i(_k, _n_hidden));
_weights2->resize(_n_hidden);
_bias1->resize(_n_hidden);
util.weight_initializationm(_weights1);
util.weight_initializationv(_weights2);
util.bias_initializationv(_bias1);
_bias2 = util.bias_initializationr();
_z2.instance();
_a2.instance();
_initialized = true;
}
real_t MLPPMLP::cost(const Ref<MLPPVector> &p_y_hat, const Ref<MLPPVector> &p_y) {
MLPPReg regularization;
MLPPCost mlpp_cost;
return mlpp_cost.log_lossv(p_y_hat, p_y) + regularization.reg_termv(_weights2, _lambda, _alpha, _reg) + regularization.reg_termm(_weights1, _lambda, _alpha, _reg);
}
Ref<MLPPVector> MLPPMLP::evaluatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
MLPPActivation avn;
Ref<MLPPMatrix> pz2 = alg.mat_vec_addnm(alg.matmultnm(X, _weights1), _bias1);
Ref<MLPPMatrix> pa2 = avn.sigmoid_normm(pz2);
return avn.sigmoid_normv(alg.scalar_addnv(_bias2, alg.mat_vec_multnv(pa2, _weights2)));
}
void MLPPMLP::propagatem(const Ref<MLPPMatrix> &X, Ref<MLPPMatrix> z2_out, Ref<MLPPMatrix> a2_out) {
MLPPLinAlg alg;
MLPPActivation avn;
z2_out->set_from_mlpp_matrix(alg.mat_vec_addnm(alg.matmultnm(X, _weights1), _bias1));
a2_out->set_from_mlpp_matrix(avn.sigmoid_normm(z2_out));
}
real_t MLPPMLP::evaluatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
MLPPActivation avn;
Ref<MLPPVector> pz2 = alg.additionnv(alg.mat_vec_multnv(alg.transposenm(_weights1), x), _bias1);
Ref<MLPPVector> pa2 = avn.sigmoid_normv(pz2);
return avn.sigmoid_normr(alg.dotnv(_weights2, pa2) + _bias2);
}
void MLPPMLP::propagatev(const Ref<MLPPVector> &x, Ref<MLPPVector> z2_out, Ref<MLPPVector> a2_out) {
MLPPLinAlg alg;
MLPPActivation avn;
z2_out->set_from_mlpp_vector(alg.additionnv(alg.mat_vec_multnv(alg.transposenm(_weights1), x), _bias1));
a2_out->set_from_mlpp_vector(avn.sigmoid_normv(z2_out));
}
void MLPPMLP::forward_pass() {
MLPPLinAlg alg;
MLPPActivation avn;
_z2->set_from_mlpp_matrix(alg.mat_vec_addnm(alg.matmultnm(_input_set, _weights1), _bias1));
_a2->set_from_mlpp_matrix(avn.sigmoid_normm(_z2));
_y_hat->set_from_mlpp_vector(avn.sigmoid_normv(alg.scalar_addnv(_bias2, alg.mat_vec_multnv(_a2, _weights2))));
}
MLPPMLP::MLPPMLP(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
_input_set = p_input_set;
_output_set = p_output_set;
_y_hat.instance();
_n_hidden = p_n_hidden;
_n = _input_set->size().y;
_k = _input_set->size().x;
_reg = p_reg;
_lambda = p_lambda;
_alpha = p_alpha;
MLPPActivation avn;
_y_hat->resize(_n);
MLPPUtilities util;
_weights1.instance();
_weights1->resize(Size2i(_k, _n_hidden));
_weights2.instance();
_weights2->resize(_n_hidden);
_bias1.instance();
_bias1->resize(_n_hidden);
util.weight_initializationm(_weights1);
util.weight_initializationv(_weights2);
util.bias_initializationv(_bias1);
_bias2 = util.bias_initializationr();
_z2.instance();
_a2.instance();
_initialized = true;
}
MLPPMLP::MLPPMLP() {
_y_hat.instance();
_n_hidden = 0;
_n = 0;
_k = 0;
_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
_lambda = 0.5;
_alpha = 0.5;
_weights1.instance();
_weights2.instance();
_bias1.instance();
_bias2 = 0;
_z2.instance();
_a2.instance();
_initialized = false;
}
MLPPMLP::~MLPPMLP() {
}
void MLPPMLP::_bind_methods() {
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPMLP::get_input_set);
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPMLP::set_input_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPMLP::get_output_set);
ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPMLP::set_output_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
ClassDB::bind_method(D_METHOD("get_n_hidden"), &MLPPMLP::get_n_hidden);
ClassDB::bind_method(D_METHOD("set_n_hidden", "val"), &MLPPMLP::set_n_hidden);
ADD_PROPERTY(PropertyInfo(Variant::INT, "n_hidden"), "set_n_hidden", "get_n_hidden");
ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPMLP::get_lambda);
ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPMLP::set_lambda);
ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPMLP::get_alpha);
ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPMLP::set_alpha);
ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
ClassDB::bind_method(D_METHOD("get_reg"), &MLPPMLP::get_reg);
ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPMLP::set_reg);
ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPMLP::is_initialized);
ClassDB::bind_method(D_METHOD("initialize"), &MLPPMLP::initialize);
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPMLP::model_set_test);
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPMLP::model_test);
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "UI"), &MLPPMLP::gradient_descent, false);
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "UI"), &MLPPMLP::sgd, false);
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "UI"), &MLPPMLP::mbgd, false);
ClassDB::bind_method(D_METHOD("score"), &MLPPMLP::score);
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPMLP::save);
}