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MLPPSoftmaxReg api rework.

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Relintai 2023-04-28 21:23:37 +02:00
parent b9eda1bb2d
commit 38dbc2d470
3 changed files with 120 additions and 109 deletions
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186
mlpp/softmax_reg/softmax_reg.cpp
View File

@ -13,65 +13,79 @@
#include <random>
Ref<MLPPMatrix> MLPPSoftmaxReg::get_input_set() {
Ref<MLPPMatrix> MLPPSoftmaxReg::get_input_set() const {
return _input_set;
}
void MLPPSoftmaxReg::set_input_set(const Ref<MLPPMatrix> &val) {
_input_set = val;
_initialized = false;
}
Ref<MLPPMatrix> MLPPSoftmaxReg::get_output_set() {
Ref<MLPPMatrix> MLPPSoftmaxReg::get_output_set() const {
return _output_set;
}
void MLPPSoftmaxReg::set_output_set(const Ref<MLPPMatrix> &val) {
_output_set = val;
_initialized = false;
}
MLPPReg::RegularizationType MLPPSoftmaxReg::get_reg() {
MLPPReg::RegularizationType MLPPSoftmaxReg::get_reg() const {
return _reg;
}
void MLPPSoftmaxReg::set_reg(const MLPPReg::RegularizationType val) {
_reg = val;
_initialized = false;
}
real_t MLPPSoftmaxReg::get_lambda() {
real_t MLPPSoftmaxReg::get_lambda() const {
return _lambda;
}
void MLPPSoftmaxReg::set_lambda(const real_t val) {
_lambda = val;
_initialized = false;
}
real_t MLPPSoftmaxReg::get_alpha() {
real_t MLPPSoftmaxReg::get_alpha() const {
return _alpha;
}
void MLPPSoftmaxReg::set_alpha(const real_t val) {
_alpha = val;
}
_initialized = false;
Ref<MLPPMatrix> MLPPSoftmaxReg::data_y_hat_get() const {
return _y_hat;
}
void MLPPSoftmaxReg::data_y_hat_set(const Ref<MLPPMatrix> &val) {
_y_hat = val;
}
Ref<MLPPMatrix> MLPPSoftmaxReg::data_weights_get() const {
return _weights;
}
void MLPPSoftmaxReg::data_weights_set(const Ref<MLPPMatrix> &val) {
_weights = val;
}
Ref<MLPPVector> MLPPSoftmaxReg::data_bias_get() const {
return _bias;
}
void MLPPSoftmaxReg::data_bias_set(const Ref<MLPPVector> &val) {
_bias = val;
}
Ref<MLPPVector> MLPPSoftmaxReg::model_test(const Ref<MLPPVector> &x) {
ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
return evaluatev(x);
}
Ref<MLPPMatrix> MLPPSoftmaxReg::model_set_test(const Ref<MLPPMatrix> &X) {
ERR_FAIL_COND_V(!_initialized, Ref<MLPPMatrix>());
ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
ERR_FAIL_COND_V(needs_init(), Ref<MLPPMatrix>());
return evaluatem(X);
}
void MLPPSoftmaxReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
void MLPPSoftmaxReg::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
ERR_FAIL_COND(needs_init());
MLPPReg regularization;
real_t cost_prev = 0;
@ -113,17 +127,19 @@ void MLPPSoftmaxReg::gradient_descent(real_t learning_rate, int max_epoch, bool
}
}
void MLPPSoftmaxReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
void MLPPSoftmaxReg::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
ERR_FAIL_COND(needs_init());
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
int n = _input_set->size().y;
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_int_distribution<int> distribution(0, int(_n - 1));
std::uniform_int_distribution<int> distribution(0, int(n - 1));
Ref<MLPPVector> input_set_row_tmp;
input_set_row_tmp.instance();
@ -185,15 +201,17 @@ void MLPPSoftmaxReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
forward_pass();
}
void MLPPSoftmaxReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
ERR_FAIL_COND(!_initialized);
void MLPPSoftmaxReg::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
ERR_FAIL_COND(needs_init());
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
int n = _input_set->size().y;
// Creating the mini-batches
int n_mini_batch = _n / mini_batch_size;
int n_mini_batch = n / mini_batch_size;
MLPPUtilities::CreateMiniBatchMMBatch batches = MLPPUtilities::create_mini_batchesmm(_input_set, _output_set, n_mini_batch);
while (true) {
@ -234,98 +252,80 @@ void MLPPSoftmaxReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
}
real_t MLPPSoftmaxReg::score() {
ERR_FAIL_COND_V(!_initialized, 0);
ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), 0);
ERR_FAIL_COND_V(needs_init(), 0);
MLPPUtilities util;
return util.performance_mat(_y_hat, _output_set);
}
void MLPPSoftmaxReg::save(const String &file_name) {
ERR_FAIL_COND(!_initialized);
MLPPUtilities util;
//util.saveParameters(file_name, _weights, _bias);
}
bool MLPPSoftmaxReg::is_initialized() {
return _initialized;
}
void MLPPSoftmaxReg::initialize() {
if (_initialized) {
return;
bool MLPPSoftmaxReg::needs_init() const {
if (!_input_set.is_valid()) {
return true;
}
if (!_output_set.is_valid()) {
return true;
}
int n = _input_set->size().y;
int k = _input_set->size().x;
int n_class = _output_set->size().x;
if (_y_hat->size().x != n) {
return true;
}
if (_weights->size() != Size2i(n_class, k)) {
return true;
}
if (_bias->size() != n_class) {
return true;
}
return false;
}
void MLPPSoftmaxReg::initialize() {
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
_n = _input_set->size().y;
_k = _input_set->size().x;
_n_class = _output_set->size().x;
int n = _input_set->size().y;
int k = _input_set->size().x;
int n_class = _output_set->size().x;
_y_hat.instance();
_y_hat->resize(Size2i(_n, 0));
_y_hat->resize(Size2i(n, 0));
MLPPUtilities util;
_weights.instance();
_weights->resize(Size2i(_n_class, _k));
_bias.instance();
_bias->resize(_n_class);
_weights->resize(Size2i(n_class, k));
_bias->resize(n_class);
util.weight_initializationm(_weights);
util.bias_initializationv(_bias);
_initialized = true;
}
MLPPSoftmaxReg::MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
_input_set = p_input_set;
_output_set = p_output_set;
_n = _input_set->size().y;
_k = _input_set->size().x;
_n_class = _output_set->size().x;
_reg = p_reg;
_lambda = p_lambda;
_alpha = p_alpha;
if (!_y_hat.is_valid()) {
_y_hat.instance();
}
_y_hat->resize(Size2i(_n, 0));
MLPPUtilities util;
if (!_weights.is_valid()) {
_weights.instance();
}
_weights->resize(Size2i(_n_class, _k));
if (!_bias.is_valid()) {
_bias.instance();
}
_bias->resize(_n_class);
util.weight_initializationm(_weights);
util.bias_initializationv(_bias);
_initialized = true;
_y_hat.instance();
_weights.instance();
_bias.instance();
}
MLPPSoftmaxReg::MLPPSoftmaxReg() {
_n = 0;
_k = 0;
_n_class = 0;
// Regularization Params
_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
_lambda = 0.5;
_alpha = 0.5; /* This is the controlling param for Elastic Net*/
_initialized = false;
_y_hat.instance();
_weights.instance();
_bias.instance();
}
MLPPSoftmaxReg::~MLPPSoftmaxReg() {
}
@ -376,17 +376,27 @@ void MLPPSoftmaxReg::_bind_methods() {
ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPSoftmaxReg::set_alpha);
ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPSoftmaxReg::data_y_hat_get);
ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSoftmaxReg::data_y_hat_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_y_hat_set", "data_y_hat_get");
ClassDB::bind_method(D_METHOD("data_weights_get"), &MLPPSoftmaxReg::data_weights_get);
ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPSoftmaxReg::data_weights_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_weights_set", "data_weights_get");
ClassDB::bind_method(D_METHOD("data_bias_get"), &MLPPSoftmaxReg::data_bias_get);
ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPSoftmaxReg::data_bias_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_bias", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_bias_set", "data_bias_get");
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSoftmaxReg::model_test);
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSoftmaxReg::model_set_test);
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxReg::gradient_descent, false);
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxReg::sgd, false);
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxReg::mbgd, false);
ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxReg::train_gradient_descent, false);
ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxReg::train_sgd, false);
ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxReg::train_mbgd, false);
ClassDB::bind_method(D_METHOD("score"), &MLPPSoftmaxReg::score);
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSoftmaxReg::save);
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSoftmaxReg::is_initialized);
ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSoftmaxReg::needs_init);
ClassDB::bind_method(D_METHOD("initialize"), &MLPPSoftmaxReg::initialize);
}

41
mlpp/softmax_reg/softmax_reg.h
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@ -10,44 +10,51 @@
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "core/object/resource.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
class MLPPSoftmaxReg : public Reference {
GDCLASS(MLPPSoftmaxReg, Reference);
class MLPPSoftmaxReg : public Resource {
GDCLASS(MLPPSoftmaxReg, Resource);
public:
Ref<MLPPMatrix> get_input_set();
Ref<MLPPMatrix> get_input_set() const;
void set_input_set(const Ref<MLPPMatrix> &val);
Ref<MLPPMatrix> get_output_set();
Ref<MLPPMatrix> get_output_set() const;
void set_output_set(const Ref<MLPPMatrix> &val);
MLPPReg::RegularizationType get_reg();
MLPPReg::RegularizationType get_reg() const;
void set_reg(const MLPPReg::RegularizationType val);
real_t get_lambda();
real_t get_lambda() const;
void set_lambda(const real_t val);
real_t get_alpha();
real_t get_alpha() const;
void set_alpha(const real_t val);
Ref<MLPPMatrix> data_y_hat_get() const;
void data_y_hat_set(const Ref<MLPPMatrix> &val);
Ref<MLPPMatrix> data_weights_get() const;
void data_weights_set(const Ref<MLPPMatrix> &val);
Ref<MLPPVector> data_bias_get() const;
void data_bias_set(const Ref<MLPPVector> &val);
Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &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 train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
real_t score();
void save(const String &file_name);
bool is_initialized();
bool needs_init() const;
void initialize();
MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
@ -75,12 +82,6 @@ protected:
Ref<MLPPMatrix> _y_hat;
Ref<MLPPMatrix> _weights;
Ref<MLPPVector> _bias;
int _n;
int _k;
int _n_class;
bool _initialized;
};
#endif /* SoftmaxReg_hpp */

2
test/mlpp_tests.cpp
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@ -345,7 +345,7 @@ void MLPPTests::test_softmax_regression(bool ui) {
// SOFTMAX REGRESSION
MLPPSoftmaxReg model(dt->get_input(), dt->get_output());
model.sgd(0.1, 10000, ui);
model.train_sgd(0.1, 10000, ui);
PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}