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Now MLPPSoftmaxNet uses engine classes.

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Relintai 2023-02-14 00:01:12 +01:00
parent 686d81a258
commit 999d55b667
3 changed files with 174 additions and 137 deletions
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253
mlpp/softmax_net/softmax_net.cpp
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@ -5,6 +5,7 @@
// //
#include "softmax_net.h" #include "softmax_net.h"
#include "../activation/activation.h" #include "../activation/activation.h"
#include "../cost/cost.h" #include "../cost/cost.h"
#include "../data/data.h" #include "../data/data.h"
@ -12,7 +13,8 @@
#include "../regularization/reg.h" #include "../regularization/reg.h"
#include "../utilities/utilities.h" #include "../utilities/utilities.h"
#include <iostream> #include "core/log/logger.h"
#include <random> #include <random>
/* /*
@ -62,11 +64,11 @@ void MLPPSoftmaxNet::set_alpha(const real_t val) {
} }
*/ */
std::vector<real_t> MLPPSoftmaxNet::model_test(std::vector<real_t> x) { Ref<MLPPVector> MLPPSoftmaxNet::model_test(const Ref<MLPPVector> &x) {
return evaluatev(x); return evaluatev(x);
} }
std::vector<std::vector<real_t>> MLPPSoftmaxNet::model_set_test(std::vector<std::vector<real_t>> X) { Ref<MLPPMatrix> MLPPSoftmaxNet::model_set_test(const Ref<MLPPMatrix> &X) {
return evaluatem(X); return evaluatem(X);
} }
@ -84,43 +86,41 @@ void MLPPSoftmaxNet::gradient_descent(real_t learning_rate, int max_epoch, bool
cost_prev = cost(_y_hat, _output_set); cost_prev = cost(_y_hat, _output_set);
// Calculating the errors // Calculating the errors
std::vector<std::vector<real_t>> error = alg.subtraction(_y_hat, _output_set); Ref<MLPPMatrix> error = alg.subtractionm(_y_hat, _output_set);
// Calculating the weight/bias gradients for layer 2 // Calculating the weight/bias gradients for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(_a2), error); Ref<MLPPMatrix> D2_1 = alg.matmultm(alg.transposem(_a2), error);
// weights and bias updation for layer 2 // weights and bias updation for layer 2
_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1)); _weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, D2_1));
//_reg _weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error)); _bias2 = alg.subtract_matrix_rows(_bias2, alg.scalar_multiplym(learning_rate, error));
//Calculating the weight/bias for layer 1 //Calculating the weight/bias for layer 1
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2)); Ref<MLPPMatrix> D1_1 = alg.matmultm(error, alg.transposem(_weights2));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(_z2, true)); Ref<MLPPMatrix> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivm(_z2));
std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(_input_set), D1_2); Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(_input_set), D1_2);
// weight an bias updation for layer 1 // weight an bias updation for layer 1
_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3)); _weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
//_reg _weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2)); _bias1 = alg.subtract_matrix_rows(_bias1, alg.scalar_multiplym(learning_rate, D1_2));
forward_pass(); forward_pass();
// UI PORTION // UI PORTION
if (ui) { if (ui) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set)); MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
std::cout << "Layer 1:" << std::endl; PLOG_MSG("Layer 1:");
MLPPUtilities::UI(_weights1, _bias1); MLPPUtilities::print_ui_mb(_weights1, _bias1);
std::cout << "Layer 2:" << std::endl; PLOG_MSG("Layer 2:");
MLPPUtilities::UI(_weights2, _bias2); MLPPUtilities::print_ui_mb(_weights2, _bias2);
} }
epoch++; epoch++;
@ -143,47 +143,64 @@ void MLPPSoftmaxNet::sgd(real_t learning_rate, int max_epoch, bool ui) {
std::default_random_engine generator(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();
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(_output_set->size().x);
Ref<MLPPMatrix> y_hat_mat_tmp;
y_hat_mat_tmp.instance();
y_hat_mat_tmp->resize(Size2i(_bias1->size(), 1));
Ref<MLPPMatrix> output_row_mat_tmp;
output_row_mat_tmp.instance();
output_row_mat_tmp->resize(Size2i(_output_set->size().x, 1));
while (true) { while (true) {
int outputIndex = distribution(generator); int output_index = distribution(generator);
std::vector<real_t> y_hat = evaluatev(_input_set[outputIndex]); _input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
_output_set->get_row_into_mlpp_vector(output_index, output_set_row_tmp);
output_row_mat_tmp->set_row_mlpp_vector(0, output_set_row_tmp);
auto prop_res = propagatev(_input_set[outputIndex]); Ref<MLPPVector> y_hat = evaluatev(input_set_row_tmp);
auto z2 = std::get<0>(prop_res); y_hat_mat_tmp->set_row_mlpp_vector(0, y_hat);
auto a2 = std::get<1>(prop_res);
cost_prev = cost({ y_hat }, { _output_set[outputIndex] }); PropagateVResult prop_res = propagatev(input_set_row_tmp);
std::vector<real_t> error = alg.subtraction(y_hat, _output_set[outputIndex]);
cost_prev = cost(y_hat_mat_tmp, output_row_mat_tmp);
Ref<MLPPVector> error = alg.subtractionnv(y_hat, output_set_row_tmp);
// Weight updation for layer 2 // Weight updation for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2); Ref<MLPPMatrix> D2_1 = alg.outer_product(error, prop_res.a2);
_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1))); _weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, alg.transposem(D2_1)));
//_reg _weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
// Bias updation for layer 2 // Bias updation for layer 2
_bias2 = alg.subtraction(_bias2, alg.scalarMultiply(learning_rate, error)); _bias2 = alg.subtractionnv(_bias2, alg.scalar_multiplynv(learning_rate, error));
// Weight updation for layer 1 // Weight updation for layer 1
std::vector<real_t> D1_1 = alg.mat_vec_mult(_weights2, error); Ref<MLPPVector> D1_1 = alg.mat_vec_multv(_weights2, error);
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); Ref<MLPPVector> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivv(prop_res.z2));
std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(_input_set[outputIndex], D1_2); Ref<MLPPMatrix> D1_3 = alg.outer_product(input_set_row_tmp, D1_2);
_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3)); _weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
//_reg _weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
// Bias updation for layer 1 // Bias updation for layer 1
_bias1 = alg.subtraction(_bias1, alg.scalarMultiply(learning_rate, D1_2)); _bias1 = alg.subtractionnv(_bias1, alg.scalar_multiplynv(learning_rate, D1_2));
y_hat = evaluatev(_input_set[outputIndex]); y_hat = evaluatev(input_set_row_tmp);
if (ui) { if (ui) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] })); MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_mat_tmp, output_row_mat_tmp));
std::cout << "Layer 1:" << std::endl; PLOG_MSG("Layer 1:");
MLPPUtilities::UI(_weights1, _bias1); MLPPUtilities::print_ui_mb(_weights1, _bias1);
std::cout << "Layer 2:" << std::endl; PLOG_MSG("Layer 2:");
MLPPUtilities::UI(_weights2, _bias2); MLPPUtilities::print_ui_mb(_weights2, _bias2);
} }
epoch++; epoch++;
@ -206,58 +223,53 @@ void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
// Creating the mini-batches // Creating the mini-batches
int n_mini_batch = _n / mini_batch_size; int n_mini_batch = _n / mini_batch_size;
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch); MLPPUtilities::CreateMiniBatchMMBatch batches = MLPPUtilities::create_mini_batchesmm(_input_set, _output_set, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
while (true) { while (true) {
for (int i = 0; i < n_mini_batch; i++) { for (int i = 0; i < n_mini_batch; i++) {
std::vector<std::vector<real_t>> y_hat = evaluatem(inputMiniBatches[i]); Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
Ref<MLPPMatrix> current_output_mini_batch = batches.output_sets[i];
auto propagate_res = propagatem(inputMiniBatches[i]); Ref<MLPPMatrix> y_hat = evaluatem(current_input_mini_batch);
auto z2 = std::get<0>(propagate_res);
auto a2 = std::get<1>(propagate_res);
cost_prev = cost(y_hat, outputMiniBatches[i]); PropagateMResult prop_res = propagatem(current_input_mini_batch);
cost_prev = cost(y_hat, current_output_mini_batch);
// Calculating the errors // Calculating the errors
std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputMiniBatches[i]); Ref<MLPPMatrix> error = alg.subtractionm(y_hat, current_output_mini_batch);
// Calculating the weight/bias gradients for layer 2 // Calculating the weight/bias gradients for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error); Ref<MLPPMatrix> D2_1 = alg.matmultm(alg.transposem(prop_res.a2), error);
// weights and bias updation for layser 2 // weights and bias updation for layser 2
_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1)); _weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, D2_1));
//_reg _weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
// Bias Updation for layer 2 // Bias Updation for layer 2
_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error)); _bias2 = alg.subtract_matrix_rows(_bias2, alg.scalar_multiplym(learning_rate, error));
//Calculating the weight/bias for layer 1 //Calculating the weight/bias for layer 1
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2)); Ref<MLPPMatrix> D1_1 = alg.matmultm(error, alg.transposem(_weights2));
Ref<MLPPMatrix> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivm(prop_res.z2));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(current_input_mini_batch), D1_2);
std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
// weight an bias updation for layer 1 // weight an bias updation for layer 1
_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3)); _weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
//_reg _weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2)); _bias1 = alg.subtract_matrix_rows(_bias1, alg.scalar_multiplym(learning_rate, D1_2));
y_hat = evaluatem(inputMiniBatches[i]); y_hat = evaluatem(current_input_mini_batch);
if (ui) { if (ui) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i])); MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
std::cout << "Layer 1:" << std::endl; PLOG_MSG("Layer 1:");
MLPPUtilities::UI(_weights1, _bias1); MLPPUtilities::print_ui_mb(_weights1, _bias1);
std::cout << "Layer 2:" << std::endl; PLOG_MSG("Layer 2:");
MLPPUtilities::UI(_weights2, _bias2); MLPPUtilities::print_ui_mb(_weights2, _bias2);
} }
} }
@ -274,17 +286,17 @@ void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
real_t MLPPSoftmaxNet::score() { real_t MLPPSoftmaxNet::score() {
MLPPUtilities util; MLPPUtilities util;
return util.performance(_y_hat, _output_set); return util.performance_mat(_y_hat, _output_set);
} }
void MLPPSoftmaxNet::save(std::string fileName) { void MLPPSoftmaxNet::save(const String &file_name) {
MLPPUtilities util; MLPPUtilities util;
util.saveParameters(fileName, _weights1, _bias1, false, 1); //util.saveParameters(fileName, _weights1, _bias1, false, 1);
util.saveParameters(fileName, _weights2, _bias2, true, 2); //util.saveParameters(fileName, _weights2, _bias2, true, 2);
} }
std::vector<std::vector<real_t>> MLPPSoftmaxNet::get_embeddings() { Ref<MLPPMatrix> MLPPSoftmaxNet::get_embeddings() {
return _weights1; return _weights1;
} }
@ -301,23 +313,37 @@ void MLPPSoftmaxNet::initialize() {
_initialized = true; _initialized = true;
} }
MLPPSoftmaxNet::MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) { MLPPSoftmaxNet::MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
_input_set = p_input_set; _input_set = p_input_set;
_output_set = p_output_set; _output_set = p_output_set;
_n = p_input_set.size(); _n = p_input_set->size().y;
_k = p_input_set[0].size(); _k = p_input_set->size().x;
_n_hidden = p_n_hidden; _n_hidden = p_n_hidden;
_n_class = p_output_set[0].size(); _n_class = p_output_set->size().x;
_reg = p_reg; _reg = p_reg;
_lambda = p_lambda; _lambda = p_lambda;
_alpha = p_alpha; _alpha = p_alpha;
_y_hat.resize(_n); _y_hat.instance();
_y_hat->resize(Size2i(0, _n));
_weights1 = MLPPUtilities::weightInitialization(_k, _n_hidden); MLPPUtilities utils;
_weights2 = MLPPUtilities::weightInitialization(_n_hidden, _n_class);
_bias1 = MLPPUtilities::biasInitialization(_n_hidden); _weights1.instance();
_bias2 = MLPPUtilities::biasInitialization(_n_class); _weights1->resize(Size2i(_n_hidden, _k));
utils.weight_initializationm(_weights1);
_weights2.instance();
_weights2->resize(Size2i(_n_class, _n_hidden));
utils.weight_initializationm(_weights2);
_bias1.instance();
_bias1->resize(_n_hidden);
utils.bias_initializationv(_bias1);
_bias2.instance();
_bias2->resize(_n_class);
utils.bias_initializationv(_bias2);
_initialized = true; _initialized = true;
} }
@ -328,62 +354,65 @@ MLPPSoftmaxNet::MLPPSoftmaxNet() {
MLPPSoftmaxNet::~MLPPSoftmaxNet() { MLPPSoftmaxNet::~MLPPSoftmaxNet() {
} }
real_t MLPPSoftmaxNet::cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) { real_t MLPPSoftmaxNet::cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
MLPPReg regularization; MLPPReg regularization;
MLPPData data; MLPPData data;
class MLPPCost cost; MLPPCost mlpp_cost;
//_reg return mlpp_cost.cross_entropym(y_hat, y) + regularization.reg_termm(_weights1, _lambda, _alpha, _reg) + regularization.reg_termm(_weights2, _lambda, _alpha, _reg);
return cost.CrossEntropy(y_hat, y) + regularization.regTerm(_weights1, _lambda, _alpha, "None") + regularization.regTerm(_weights2, _lambda, _alpha, "None");
} }
std::vector<real_t> MLPPSoftmaxNet::evaluatev(std::vector<real_t> x) { Ref<MLPPVector> MLPPSoftmaxNet::evaluatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1); Ref<MLPPVector> z2 = alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights1), x), _bias1);
std::vector<real_t> a2 = avn.sigmoid(z2); Ref<MLPPVector> a2 = avn.sigmoid_normv(z2);
return avn.adjSoftmax(alg.addition(alg.mat_vec_mult(alg.transpose(_weights2), a2), _bias2)); return avn.adj_softmax_normv(alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights2), a2), _bias2));
} }
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPSoftmaxNet::propagatev(std::vector<real_t> x) { MLPPSoftmaxNet::PropagateVResult MLPPSoftmaxNet::propagatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1); PropagateVResult res;
std::vector<real_t> a2 = avn.sigmoid(z2);
return { z2, a2 }; res.z2 = alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights1), x), _bias1);
res.a2 = avn.sigmoid_normv(res.z2);
return res;
} }
std::vector<std::vector<real_t>> MLPPSoftmaxNet::evaluatem(std::vector<std::vector<real_t>> X) { Ref<MLPPMatrix> MLPPSoftmaxNet::evaluatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1); Ref<MLPPMatrix> z2 = alg.mat_vec_addv(alg.matmultm(X, _weights1), _bias1);
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2); Ref<MLPPMatrix> a2 = avn.sigmoid_normm(z2);
return avn.adjSoftmax(alg.mat_vec_add(alg.matmult(a2, _weights2), _bias2)); return avn.adj_softmax_normm(alg.mat_vec_addv(alg.matmultm(a2, _weights2), _bias2));
} }
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPSoftmaxNet::propagatem(std::vector<std::vector<real_t>> X) { MLPPSoftmaxNet::PropagateMResult MLPPSoftmaxNet::propagatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1); MLPPSoftmaxNet::PropagateMResult res;
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
return { z2, a2 }; res.z2 = alg.mat_vec_addv(alg.matmultm(X, _weights1), _bias1);
res.a2 = avn.sigmoid_normm(res.z2);
return res;
} }
void MLPPSoftmaxNet::forward_pass() { void MLPPSoftmaxNet::forward_pass() {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
_z2 = alg.mat_vec_add(alg.matmult(_input_set, _weights1), _bias1); _z2 = alg.mat_vec_addv(alg.matmultm(_input_set, _weights1), _bias1);
_a2 = avn.sigmoid(_z2); _a2 = avn.sigmoid_normm(_z2);
_y_hat = avn.adjSoftmax(alg.mat_vec_add(alg.matmult(_a2, _weights2), _bias2)); _y_hat = avn.adj_softmax_normm(alg.mat_vec_addv(alg.matmultm(_a2, _weights2), _bias2));
} }
void MLPPSoftmaxNet::_bind_methods() { void MLPPSoftmaxNet::_bind_methods() {

54
mlpp/softmax_net/softmax_net.h
View File

@ -16,9 +16,6 @@
#include "../regularization/reg.h" #include "../regularization/reg.h"
#include <string>
#include <vector>
class MLPPSoftmaxNet : public Reference { class MLPPSoftmaxNet : public Reference {
GDCLASS(MLPPSoftmaxNet, Reference); GDCLASS(MLPPSoftmaxNet, Reference);
@ -40,8 +37,8 @@ public:
void set_alpha(const real_t val); void set_alpha(const real_t val);
*/ */
std::vector<real_t> model_test(std::vector<real_t> x); Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X); Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false); 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 sgd(real_t learning_rate, int max_epoch, bool ui = false);
@ -49,44 +46,55 @@ public:
real_t score(); real_t score();
void save(std::string fileName); void save(const String &file_name);
std::vector<std::vector<real_t>> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings. Ref<MLPPMatrix> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
bool is_initialized(); bool is_initialized();
void initialize(); void initialize();
MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5); MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
//MLPPSoftmaxNet(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);
MLPPSoftmaxNet(); MLPPSoftmaxNet();
~MLPPSoftmaxNet(); ~MLPPSoftmaxNet();
protected: protected:
real_t cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y); real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
std::vector<real_t> evaluatev(std::vector<real_t> x); Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
std::tuple<std::vector<real_t>, std::vector<real_t>> propagatev(std::vector<real_t> x);
std::vector<std::vector<real_t>> evaluatem(std::vector<std::vector<real_t>> X); struct PropagateVResult {
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagatem(std::vector<std::vector<real_t>> X); 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(); void forward_pass();
static void _bind_methods(); static void _bind_methods();
std::vector<std::vector<real_t>> _input_set; Ref<MLPPMatrix> _input_set;
std::vector<std::vector<real_t>> _output_set; Ref<MLPPMatrix> _output_set;
std::vector<std::vector<real_t>> _y_hat; Ref<MLPPMatrix> _y_hat;
std::vector<std::vector<real_t>> _weights1; Ref<MLPPMatrix> _weights1;
std::vector<std::vector<real_t>> _weights2; Ref<MLPPMatrix> _weights2;
std::vector<real_t> _bias1; Ref<MLPPVector> _bias1;
std::vector<real_t> _bias2; Ref<MLPPVector> _bias2;
std::vector<std::vector<real_t>> _z2; Ref<MLPPMatrix> _z2;
std::vector<std::vector<real_t>> _a2; Ref<MLPPMatrix> _a2;
int _n; int _n;
int _k; int _k;

4
test/mlpp_tests.cpp
View File

@ -562,9 +562,9 @@ void MLPPTests::test_soft_max_network(bool ui) {
alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector())); alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector()));
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl; std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
MLPPSoftmaxNet model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1); MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1);
model.gradient_descent(0.01, 100000, ui); model.gradient_descent(0.01, 100000, ui);
alg.printMatrix(model.model_set_test(dt->get_input()->to_std_vector())); PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
} }
void MLPPTests::test_autoencoder(bool ui) { void MLPPTests::test_autoencoder(bool ui) {