Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2025-03-13 22:48:50 +01:00
Code Issues Packages Projects Releases Wiki Activity

Renamed MLPPMLP to MLPPMLPOld.

Browse Source
This commit is contained in:
Relintai 2023-02-04 15:59:27 +01:00
parent 817b1e3b72
commit abe878ee72
3 changed files with 17 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
mlpp/mlp/mlp.cpp
View File

@ -16,7 +16,7 @@
#include <random> #include <random>
MLPPMLP::MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) : MLPPMLPOld::MLPPMLPOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) { inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
MLPPActivation avn; MLPPActivation avn;
y_hat.resize(n); y_hat.resize(n);
@ -27,15 +27,15 @@ MLPPMLP::MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t>
bias2 = MLPPUtilities::biasInitialization(); bias2 = MLPPUtilities::biasInitialization();
} }
std::vector<real_t> MLPPMLP::modelSetTest(std::vector<std::vector<real_t>> X) { std::vector<real_t> MLPPMLPOld::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X); return Evaluate(X);
} }
real_t MLPPMLP::modelTest(std::vector<real_t> x) { real_t MLPPMLPOld::modelTest(std::vector<real_t> x) {
return Evaluate(x); return Evaluate(x);
} }
void MLPPMLP::gradientDescent(real_t learning_rate, int max_epoch, bool UI) { void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
@ -94,7 +94,7 @@ void MLPPMLP::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
} }
} }
void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) { void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
@ -148,7 +148,7 @@ void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) {
forwardPass(); forwardPass();
} }
void MLPPMLP::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) { void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
@ -214,24 +214,24 @@ void MLPPMLP::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
forwardPass(); forwardPass();
} }
real_t MLPPMLP::score() { real_t MLPPMLPOld::score() {
MLPPUtilities util; MLPPUtilities util;
return util.performance(y_hat, outputSet); return util.performance(y_hat, outputSet);
} }
void MLPPMLP::save(std::string fileName) { void MLPPMLPOld::save(std::string fileName) {
MLPPUtilities util; MLPPUtilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1); util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2); util.saveParameters(fileName, weights2, bias2, 1, 2);
} }
real_t MLPPMLP::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) { real_t MLPPMLPOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
MLPPReg regularization; MLPPReg regularization;
class MLPPCost cost; class MLPPCost cost;
return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg); return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
} }
std::vector<real_t> MLPPMLP::Evaluate(std::vector<std::vector<real_t>> X) { std::vector<real_t> MLPPMLPOld::Evaluate(std::vector<std::vector<real_t>> 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); std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
@ -239,7 +239,7 @@ std::vector<real_t> MLPPMLP::Evaluate(std::vector<std::vector<real_t>> X) {
return avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2))); return avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
} }
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPMLP::propagate(std::vector<std::vector<real_t>> X) { std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPMLPOld::propagate(std::vector<std::vector<real_t>> 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); std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
@ -247,7 +247,7 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
return { z2, a2 }; return { z2, a2 };
} }
real_t MLPPMLP::Evaluate(std::vector<real_t> x) { real_t MLPPMLPOld::Evaluate(std::vector<real_t> 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); std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
@ -255,7 +255,7 @@ real_t MLPPMLP::Evaluate(std::vector<real_t> x) {
return avn.sigmoid(alg.dot(weights2, a2) + bias2); return avn.sigmoid(alg.dot(weights2, a2) + bias2);
} }
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLP::propagate(std::vector<real_t> x) { std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLPOld::propagate(std::vector<real_t> 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); std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
@ -263,7 +263,7 @@ std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLP::propagate(std::vec
return { z2, a2 }; return { z2, a2 };
} }
void MLPPMLP::forwardPass() { void MLPPMLPOld::forwardPass() {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1); z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);

7
mlpp/mlp/mlp.h
View File

@ -14,11 +14,9 @@
#include <string> #include <string>
#include <vector> #include <vector>
class MLPPMLPOld {
class MLPPMLP {
public: public:
MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); MLPPMLPOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X); std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
real_t modelTest(std::vector<real_t> x); real_t modelTest(std::vector<real_t> x);
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
@ -59,5 +57,4 @@ private:
real_t alpha; /* This is the controlling param for Elastic Net*/ real_t alpha; /* This is the controlling param for Elastic Net*/
}; };
#endif /* MLP_hpp */ #endif /* MLP_hpp */

2
test/mlpp_tests.cpp
View File

@ -393,7 +393,7 @@ void MLPPTests::test_mlp(bool ui) {
inputSet = alg.transpose(inputSet); inputSet = alg.transpose(inputSet);
std::vector<real_t> outputSet = { 0, 1, 1, 0 }; std::vector<real_t> outputSet = { 0, 1, 1, 0 };
MLPPMLP model(inputSet, outputSet, 2); MLPPMLPOld model(inputSet, outputSet, 2);
model.gradientDescent(0.1, 10000, ui); model.gradientDescent(0.1, 10000, ui);
alg.printVector(model.modelSetTest(inputSet)); alg.printVector(model.modelSetTest(inputSet));
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;