mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
506 lines
14 KiB
C++
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_multv(alg.transposem(_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_productm(alg.transposem(D1_1), avn.sigmoid_derivm(_z2));
|
|
Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(_input_set), D1_2);
|
|
|
|
// weight an bias updation for layer 1
|
|
_weights1->set_from_mlpp_matrix(alg.subtractionm(_weights1, alg.scalar_multiplym(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_rows(_bias1, alg.scalar_multiplym(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->get_row_into_mlpp_vector(output_Index, input_set_row_tmp);
|
|
real_t output_element = _output_set->get_element(output_Index);
|
|
output_set_row_tmp->set_element(0, output_element);
|
|
|
|
real_t ly_hat = evaluatev(input_set_row_tmp);
|
|
y_hat_row_tmp->set_element(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.subtractionm(_weights1, alg.scalar_multiplym(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_multv(alg.transposem(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_productm(D1_1, avn.sigmoid_derivm(lz2));
|
|
Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(current_input), D1_2);
|
|
|
|
// weight an bias updation for layer 1
|
|
_weights1->set_from_mlpp_matrix(alg.subtractionm(_weights1, alg.scalar_multiplym(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_rows(_bias1, alg.scalar_multiplym(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_addv(alg.matmultm(X, _weights1), _bias1);
|
|
Ref<MLPPMatrix> pa2 = avn.sigmoid_normm(pz2);
|
|
|
|
return avn.sigmoid_normv(alg.scalar_addnv(_bias2, alg.mat_vec_multv(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_addv(alg.matmultm(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_multv(alg.transposem(_weights1), x), _bias1);
|
|
Ref<MLPPVector> pa2 = avn.sigmoid_normv(pz2);
|
|
|
|
return avn.sigmoid_normr(alg.dotv(_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_multv(alg.transposem(_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_addv(alg.matmultm(_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_multv(_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);
|
|
}
|