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Fixed warnings in MLPPGAN.

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Relintai 2023-02-10 22:19:06 +01:00
parent e22f26d074
commit d795b55baa
2 changed files with 21 additions and 22 deletions
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40
mlpp/gan/gan.cpp
View File

@ -14,7 +14,6 @@
#include <cmath>
#include <iostream>
MLPPGAN::MLPPGAN(real_t k, std::vector<std::vector<real_t>> outputSet) :
outputSet(outputSet), n(outputSet.size()), k(k) {
}
@ -49,7 +48,10 @@ void MLPPGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
std::vector<real_t> outputSetReal = alg.onevec(n);
outputSet.insert(outputSet.end(), outputSetReal.begin(), outputSetReal.end()); // Fake + real output scores.
auto [cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad] = computeDiscriminatorGradients(y_hat, outputSet);
auto dgrads = computeDiscriminatorGradients(y_hat, outputSet);
auto cumulativeDiscriminatorHiddenLayerWGrad = std::get<0>(dgrads);
auto outputDiscriminatorWGrad = std::get<1>(dgrads);
cumulativeDiscriminatorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeDiscriminatorHiddenLayerWGrad);
outputDiscriminatorWGrad = alg.scalarMultiply(learning_rate / n, outputDiscriminatorWGrad);
updateDiscriminatorParameters(cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad, learning_rate);
@ -78,21 +80,21 @@ void MLPPGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
real_t MLPPGAN::score() {
MLPPLinAlg alg;
MLPPUtilities util;
MLPPUtilities util;
forwardPass();
return util.performance(y_hat, alg.onevec(n));
}
void MLPPGAN::save(std::string fileName) {
MLPPUtilities util;
MLPPUtilities util;
if (!network.empty()) {
util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
for (int i = 1; i < network.size(); i++) {
util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
util.saveParameters(fileName, network[0].weights, network[0].bias, false, 1);
for (uint32_t i = 1; i < network.size(); i++) {
util.saveParameters(fileName, network[i].weights, network[i].bias, true, i + 1);
}
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, true, network.size() + 1);
} else {
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, false, network.size() + 1);
}
}
@ -121,7 +123,7 @@ std::vector<std::vector<real_t>> MLPPGAN::modelSetTestGenerator(std::vector<std:
network[0].input = X;
network[0].forwardPass();
for (int i = 1; i <= network.size() / 2; i++) {
for (uint32_t i = 1; i <= network.size() / 2; i++) {
network[i].input = network[i - 1].a;
network[i].forwardPass();
}
@ -131,7 +133,7 @@ std::vector<std::vector<real_t>> MLPPGAN::modelSetTestGenerator(std::vector<std:
std::vector<real_t> MLPPGAN::modelSetTestDiscriminator(std::vector<std::vector<real_t>> X) {
if (!network.empty()) {
for (int i = network.size() / 2 + 1; i < network.size(); i++) {
for (uint32_t i = network.size() / 2 + 1; i < network.size(); i++) {
if (i == network.size() / 2 + 1) {
network[i].input = X;
} else {
@ -152,7 +154,7 @@ real_t MLPPGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
auto cost_function = outputLayer->cost_map[outputLayer->cost];
if (!network.empty()) {
for (int i = 0; i < network.size() - 1; i++) {
for (uint32_t i = 0; i < network.size() - 1; i++) {
totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
}
}
@ -165,7 +167,7 @@ void MLPPGAN::forwardPass() {
network[0].input = alg.gaussianNoise(n, k);
network[0].forwardPass();
for (int i = 1; i < network.size(); i++) {
for (uint32_t i = 1; i < network.size(); i++) {
network[i].input = network[i - 1].a;
network[i].forwardPass();
}
@ -187,7 +189,7 @@ void MLPPGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector<
network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]);
network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta));
for (int i = network.size() - 2; i > network.size() / 2; i--) {
for (int i = static_cast<int>(network.size()) - 2; i > static_cast<int>(network.size()) / 2; i--) {
network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
}
@ -232,10 +234,10 @@ std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> M
//std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl;
//std::cout << "WEIGHTS SECOND:" << network[network.size() - 1].weights.size() << "x" << network[network.size() - 1].weights[0].size() << std::endl;
for (int i = network.size() - 2; i > network.size() / 2; i--) {
auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
for (int i = static_cast<int>(network.size()) - 2; i > static_cast<int>(network.size()) / 2; i--) {
hiddenLayerAvn = network[i].activation_map[network[i].activation];
network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
}
@ -263,9 +265,9 @@ std::vector<std::vector<std::vector<real_t>>> MLPPGAN::computeGeneratorGradients
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
for (int i = network.size() - 2; i >= 0; i--) {
auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
hiddenLayerAvn = network[i].activation_map[network[i].activation];
network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
}
}

3
mlpp/gan/gan.h
View File

@ -20,8 +20,6 @@
#include <tuple>
#include <vector>
class MLPPGAN {
public:
MLPPGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
@ -58,5 +56,4 @@ private:
int k;
};
#endif /* GAN_hpp */