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Renamed MLPPWGAN to MLPPWGANOld.

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Relintai 2023-02-05 17:05:46 +01:00
parent dc4da4681b
commit 1d1611bc39
3 changed files with 23 additions and 23 deletions
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38
mlpp/wgan/wgan.cpp
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@ -15,20 +15,20 @@
#include <iostream>
MLPPWGAN::MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet) :
MLPPWGANOld::MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet) :
outputSet(outputSet), n(outputSet.size()), k(k) {
}
MLPPWGAN::~MLPPWGAN() {
MLPPWGANOld::~MLPPWGANOld() {
delete outputLayer;
}
std::vector<std::vector<real_t>> MLPPWGAN::generateExample(int n) {
std::vector<std::vector<real_t>> MLPPWGANOld::generateExample(int n) {
MLPPLinAlg alg;
return modelSetTestGenerator(alg.gaussianNoise(n, k));
}
void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
void MLPPWGANOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
class MLPPCost cost;
MLPPLinAlg alg;
real_t cost_prev = 0;
@ -50,7 +50,7 @@ void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
for (int i = 0; i < CRITIC_INTERATIONS; i++) {
generatorInputSet = alg.gaussianNoise(n, k);
discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
discriminatorInputSet.insert(discriminatorInputSet.end(), MLPPWGAN::outputSet.begin(), MLPPWGAN::outputSet.end()); // Fake + real inputs.
discriminatorInputSet.insert(discriminatorInputSet.end(), MLPPWGANOld::outputSet.begin(), MLPPWGANOld::outputSet.end()); // Fake + real inputs.
y_hat = modelSetTestDiscriminator(discriminatorInputSet);
outputSet = alg.scalarMultiply(-1, alg.onevec(n)); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
@ -75,7 +75,7 @@ void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
forwardPass();
if (UI) {
MLPPWGAN::UI(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevec(n));
MLPPWGANOld::UI(epoch, cost_prev, MLPPWGANOld::y_hat, alg.onevec(n));
}
epoch++;
@ -85,14 +85,14 @@ void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
}
}
real_t MLPPWGAN::score() {
real_t MLPPWGANOld::score() {
MLPPLinAlg alg;
MLPPUtilities util;
forwardPass();
return util.performance(y_hat, alg.onevec(n));
}
void MLPPWGAN::save(std::string fileName) {
void MLPPWGANOld::save(std::string fileName) {
MLPPUtilities util;
if (!network.empty()) {
util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
@ -105,7 +105,7 @@ void MLPPWGAN::save(std::string fileName) {
}
}
void MLPPWGAN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
void MLPPWGANOld::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
MLPPLinAlg alg;
if (network.empty()) {
network.push_back(MLPPOldHiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha));
@ -116,7 +116,7 @@ void MLPPWGAN::addLayer(int n_hidden, std::string activation, std::string weight
}
}
void MLPPWGAN::addOutputLayer(std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
void MLPPWGANOld::addOutputLayer(std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
MLPPLinAlg alg;
if (!network.empty()) {
outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, "Linear", "WassersteinLoss", network[network.size() - 1].a, weightInit, "WeightClipping", -0.01, 0.01);
@ -125,7 +125,7 @@ void MLPPWGAN::addOutputLayer(std::string weightInit, std::string reg, real_t la
}
}
std::vector<std::vector<real_t>> MLPPWGAN::modelSetTestGenerator(std::vector<std::vector<real_t>> X) {
std::vector<std::vector<real_t>> MLPPWGANOld::modelSetTestGenerator(std::vector<std::vector<real_t>> X) {
if (!network.empty()) {
network[0].input = X;
network[0].forwardPass();
@ -138,7 +138,7 @@ std::vector<std::vector<real_t>> MLPPWGAN::modelSetTestGenerator(std::vector<std
return network[network.size() / 2].a;
}
std::vector<real_t> MLPPWGAN::modelSetTestDiscriminator(std::vector<std::vector<real_t>> X) {
std::vector<real_t> MLPPWGANOld::modelSetTestDiscriminator(std::vector<std::vector<real_t>> X) {
if (!network.empty()) {
for (int i = network.size() / 2 + 1; i < network.size(); i++) {
if (i == network.size() / 2 + 1) {
@ -154,7 +154,7 @@ std::vector<real_t> MLPPWGAN::modelSetTestDiscriminator(std::vector<std::vector<
return outputLayer->a;
}
real_t MLPPWGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPWGANOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
MLPPReg regularization;
class MLPPCost cost;
real_t totalRegTerm = 0;
@ -168,7 +168,7 @@ real_t MLPPWGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
}
void MLPPWGAN::forwardPass() {
void MLPPWGANOld::forwardPass() {
MLPPLinAlg alg;
if (!network.empty()) {
network[0].input = alg.gaussianNoise(n, k);
@ -186,7 +186,7 @@ void MLPPWGAN::forwardPass() {
y_hat = outputLayer->a;
}
void MLPPWGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
void MLPPWGANOld::updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
MLPPLinAlg alg;
outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
@ -203,7 +203,7 @@ void MLPPWGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector
}
}
void MLPPWGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
void MLPPWGANOld::updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
MLPPLinAlg alg;
if (!network.empty()) {
@ -216,7 +216,7 @@ void MLPPWGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<rea
}
}
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPWGAN::computeDiscriminatorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPWGANOld::computeDiscriminatorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
class MLPPCost cost;
MLPPActivation avn;
MLPPLinAlg alg;
@ -252,7 +252,7 @@ std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> M
return { cumulativeHiddenLayerWGrad, outputWGrad };
}
std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
std::vector<std::vector<std::vector<real_t>>> MLPPWGANOld::computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
class MLPPCost cost;
MLPPActivation avn;
MLPPLinAlg alg;
@ -281,7 +281,7 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::computeGeneratorGradient
return cumulativeHiddenLayerWGrad;
}
void MLPPWGAN::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
void MLPPWGANOld::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);

6
mlpp/wgan/wgan.h
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@ -17,10 +17,10 @@
class MLPPWGAN {
class MLPPWGANOld {
public:
MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
~MLPPWGAN();
MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet);
~MLPPWGANOld();
std::vector<std::vector<real_t>> generateExample(int n);
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
real_t score();

2
test/mlpp_tests.cpp
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@ -488,7 +488,7 @@ void MLPPTests::test_wgan(bool ui) {
{ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40 }
};
MLPPWGAN gan(2, alg.transpose(outputSet)); // our gan is a wasserstein gan (wgan)
MLPPWGANOld gan(2, alg.transpose(outputSet)); // our gan is a wasserstein gan (wgan)
gan.addLayer(5, "Sigmoid");
gan.addLayer(2, "RELU");
gan.addLayer(5, "Sigmoid");