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Fix all warnings for MLPPWGANOld.

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Relintai 2023-02-05 18:14:37 +01:00
parent 1d1611bc39
commit 2177632163
2 changed files with 24 additions and 25 deletions
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46
mlpp/wgan/wgan.cpp
View File

@ -14,7 +14,6 @@
#include <cmath>
#include <iostream>
MLPPWGANOld::MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet) :
outputSet(outputSet), n(outputSet.size()), k(k) {
}
@ -57,7 +56,10 @@ void MLPPWGANOld::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 discriminator_gradient_results = computeDiscriminatorGradients(y_hat, outputSet);
auto cumulativeDiscriminatorHiddenLayerWGrad = std::get<0>(discriminator_gradient_results);
auto outputDiscriminatorWGrad = std::get<1>(discriminator_gradient_results);
cumulativeDiscriminatorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeDiscriminatorHiddenLayerWGrad);
outputDiscriminatorWGrad = alg.scalarMultiply(learning_rate / n, outputDiscriminatorWGrad);
updateDiscriminatorParameters(cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad, learning_rate);
@ -87,16 +89,16 @@ void MLPPWGANOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI)
real_t MLPPWGANOld::score() {
MLPPLinAlg alg;
MLPPUtilities util;
MLPPUtilities util;
forwardPass();
return util.performance(y_hat, alg.onevec(n));
}
void MLPPWGANOld::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++) {
for (uint32_t i = 1; i < network.size(); i++) {
util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
}
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
@ -130,7 +132,7 @@ std::vector<std::vector<real_t>> MLPPWGANOld::modelSetTestGenerator(std::vector<
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();
}
@ -140,7 +142,7 @@ std::vector<std::vector<real_t>> MLPPWGANOld::modelSetTestGenerator(std::vector<
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++) {
for (uint32_t i = network.size() / 2 + 1; i < network.size(); i++) {
if (i == network.size() / 2 + 1) {
network[i].input = X;
} else {
@ -161,7 +163,7 @@ real_t MLPPWGANOld::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);
}
}
@ -174,7 +176,7 @@ void MLPPWGANOld::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();
}
@ -196,7 +198,7 @@ void MLPPWGANOld::updateDiscriminatorParameters(std::vector<std::vector<std::vec
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 (uint32_t i = network.size() - 2; i > 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));
}
@ -207,7 +209,7 @@ void MLPPWGANOld::updateGeneratorParameters(std::vector<std::vector<std::vector<
MLPPLinAlg alg;
if (!network.empty()) {
for (int i = network.size() / 2; i >= 0; i--) {
for (uint32_t i = network.size() / 2; i >= 0; i--) {
//std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl;
//std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl;
network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
@ -241,12 +243,12 @@ 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];
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);
for (uint32_t i = network.size() - 2; i > network.size() / 2; i--) {
auto hiddenLayerAvnl = 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.*hiddenLayerAvnl)(network[i].z, 1));
std::vector<std::vector<real_t>> hiddenLayerWGradl = 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.
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGradl, 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.
}
}
return { cumulativeHiddenLayerWGrad, outputWGrad };
@ -271,11 +273,11 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGANOld::computeGeneratorGradi
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
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];
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);
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.
for (uint32_t i = network.size() - 2; i >= 0; i--) {
auto hiddenLayerAvnl = 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.*hiddenLayerAvnl)(network[i].z, 1));
std::vector<std::vector<real_t>> hiddenLayerWGradl = alg.matmult(alg.transpose(network[i].input), network[i].delta);
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGradl, 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.
}
}
return cumulativeHiddenLayerWGrad;
@ -286,7 +288,7 @@ void MLPPWGANOld::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);
if (!network.empty()) {
for (int i = network.size() - 1; i >= 0; i--) {
for (uint32_t i = network.size() - 1; i >= 0; i--) {
std::cout << "Layer " << i + 1 << ": " << std::endl;
MLPPUtilities::UI(network[i].weights, network[i].bias);
}

3
mlpp/wgan/wgan.h
View File

@ -15,8 +15,6 @@
#include <tuple>
#include <vector>
class MLPPWGANOld {
public:
MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet);
@ -53,5 +51,4 @@ private:
int k;
};
#endif /* WGAN_hpp */