mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
Duplicate MLPPWGANOld as MLPPWGAN.
This commit is contained in:
parent
2177632163
commit
8c3671fc8f
@ -14,6 +14,291 @@
|
|||||||
#include <cmath>
|
#include <cmath>
|
||||||
#include <iostream>
|
#include <iostream>
|
||||||
|
|
||||||
|
|
||||||
|
MLPPWGAN::MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet) :
|
||||||
|
outputSet(outputSet), n(outputSet.size()), k(k) {
|
||||||
|
}
|
||||||
|
|
||||||
|
MLPPWGAN::~MLPPWGAN() {
|
||||||
|
delete outputLayer;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::vector<real_t>> MLPPWGAN::generateExample(int n) {
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
return modelSetTestGenerator(alg.gaussianNoise(n, k));
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||||||
|
class MLPPCost cost;
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
real_t cost_prev = 0;
|
||||||
|
int epoch = 1;
|
||||||
|
forwardPass();
|
||||||
|
|
||||||
|
const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter.
|
||||||
|
|
||||||
|
while (true) {
|
||||||
|
cost_prev = Cost(y_hat, alg.onevec(n));
|
||||||
|
|
||||||
|
std::vector<std::vector<real_t>> generatorInputSet;
|
||||||
|
std::vector<std::vector<real_t>> discriminatorInputSet;
|
||||||
|
|
||||||
|
std::vector<real_t> y_hat;
|
||||||
|
std::vector<real_t> outputSet;
|
||||||
|
|
||||||
|
// Training of the discriminator.
|
||||||
|
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.
|
||||||
|
|
||||||
|
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
|
||||||
|
std::vector<real_t> outputSetReal = alg.onevec(n);
|
||||||
|
outputSet.insert(outputSet.end(), outputSetReal.begin(), outputSetReal.end()); // Fake + real output scores.
|
||||||
|
|
||||||
|
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);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Training of the generator.
|
||||||
|
generatorInputSet = alg.gaussianNoise(n, k);
|
||||||
|
discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
|
||||||
|
y_hat = modelSetTestDiscriminator(discriminatorInputSet);
|
||||||
|
outputSet = alg.onevec(n);
|
||||||
|
|
||||||
|
std::vector<std::vector<std::vector<real_t>>> cumulativeGeneratorHiddenLayerWGrad = computeGeneratorGradients(y_hat, outputSet);
|
||||||
|
cumulativeGeneratorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeGeneratorHiddenLayerWGrad);
|
||||||
|
updateGeneratorParameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate);
|
||||||
|
|
||||||
|
forwardPass();
|
||||||
|
if (UI) {
|
||||||
|
MLPPWGAN::UI(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevec(n));
|
||||||
|
}
|
||||||
|
|
||||||
|
epoch++;
|
||||||
|
if (epoch > max_epoch) {
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
real_t MLPPWGAN::score() {
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
MLPPUtilities util;
|
||||||
|
forwardPass();
|
||||||
|
return util.performance(y_hat, alg.onevec(n));
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::save(std::string fileName) {
|
||||||
|
MLPPUtilities util;
|
||||||
|
if (!network.empty()) {
|
||||||
|
util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
|
||||||
|
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);
|
||||||
|
} else {
|
||||||
|
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::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));
|
||||||
|
network[0].forwardPass();
|
||||||
|
} else {
|
||||||
|
network.push_back(MLPPOldHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
|
||||||
|
network[network.size() - 1].forwardPass();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::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);
|
||||||
|
} else { // Should never happen.
|
||||||
|
outputLayer = new MLPPOldOutputLayer(k, "Linear", "WassersteinLoss", alg.gaussianNoise(n, k), weightInit, "WeightClipping", -0.01, 0.01);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::vector<real_t>> MLPPWGAN::modelSetTestGenerator(std::vector<std::vector<real_t>> X) {
|
||||||
|
if (!network.empty()) {
|
||||||
|
network[0].input = X;
|
||||||
|
network[0].forwardPass();
|
||||||
|
|
||||||
|
for (uint32_t i = 1; i <= network.size() / 2; i++) {
|
||||||
|
network[i].input = network[i - 1].a;
|
||||||
|
network[i].forwardPass();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return network[network.size() / 2].a;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<real_t> MLPPWGAN::modelSetTestDiscriminator(std::vector<std::vector<real_t>> X) {
|
||||||
|
if (!network.empty()) {
|
||||||
|
for (uint32_t i = network.size() / 2 + 1; i < network.size(); i++) {
|
||||||
|
if (i == network.size() / 2 + 1) {
|
||||||
|
network[i].input = X;
|
||||||
|
} else {
|
||||||
|
network[i].input = network[i - 1].a;
|
||||||
|
}
|
||||||
|
network[i].forwardPass();
|
||||||
|
}
|
||||||
|
outputLayer->input = network[network.size() - 1].a;
|
||||||
|
}
|
||||||
|
outputLayer->forwardPass();
|
||||||
|
return outputLayer->a;
|
||||||
|
}
|
||||||
|
|
||||||
|
real_t MLPPWGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||||
|
MLPPReg regularization;
|
||||||
|
class MLPPCost cost;
|
||||||
|
real_t totalRegTerm = 0;
|
||||||
|
|
||||||
|
auto cost_function = outputLayer->cost_map[outputLayer->cost];
|
||||||
|
if (!network.empty()) {
|
||||||
|
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);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::forwardPass() {
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
if (!network.empty()) {
|
||||||
|
network[0].input = alg.gaussianNoise(n, k);
|
||||||
|
network[0].forwardPass();
|
||||||
|
|
||||||
|
for (uint32_t i = 1; i < network.size(); i++) {
|
||||||
|
network[i].input = network[i - 1].a;
|
||||||
|
network[i].forwardPass();
|
||||||
|
}
|
||||||
|
outputLayer->input = network[network.size() - 1].a;
|
||||||
|
} else { // Should never happen, though.
|
||||||
|
outputLayer->input = alg.gaussianNoise(n, k);
|
||||||
|
}
|
||||||
|
outputLayer->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) {
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
|
||||||
|
outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
|
||||||
|
outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
|
||||||
|
|
||||||
|
if (!network.empty()) {
|
||||||
|
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 (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));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
|
||||||
|
if (!network.empty()) {
|
||||||
|
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]);
|
||||||
|
network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
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) {
|
||||||
|
class MLPPCost cost;
|
||||||
|
MLPPActivation avn;
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
MLPPReg regularization;
|
||||||
|
|
||||||
|
std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
|
||||||
|
|
||||||
|
auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
|
||||||
|
auto outputAvn = outputLayer->activation_map[outputLayer->activation];
|
||||||
|
outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
|
||||||
|
std::vector<real_t> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
|
||||||
|
outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
|
||||||
|
|
||||||
|
if (!network.empty()) {
|
||||||
|
auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
|
||||||
|
|
||||||
|
network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
|
||||||
|
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.
|
||||||
|
|
||||||
|
//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 (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(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 };
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
|
||||||
|
class MLPPCost cost;
|
||||||
|
MLPPActivation avn;
|
||||||
|
MLPPLinAlg alg;
|
||||||
|
MLPPReg regularization;
|
||||||
|
|
||||||
|
std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
|
||||||
|
|
||||||
|
auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
|
||||||
|
auto outputAvn = outputLayer->activation_map[outputLayer->activation];
|
||||||
|
outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
|
||||||
|
std::vector<real_t> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
|
||||||
|
outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
|
||||||
|
if (!network.empty()) {
|
||||||
|
auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
|
||||||
|
network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
|
||||||
|
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 (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;
|
||||||
|
}
|
||||||
|
|
||||||
|
void MLPPWGAN::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);
|
||||||
|
if (!network.empty()) {
|
||||||
|
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);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// ======== OLD ==========
|
||||||
|
|
||||||
MLPPWGANOld::MLPPWGANOld(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) {
|
outputSet(outputSet), n(outputSet.size()), k(k) {
|
||||||
}
|
}
|
||||||
|
|||||||
@ -15,6 +15,42 @@
|
|||||||
#include <tuple>
|
#include <tuple>
|
||||||
#include <vector>
|
#include <vector>
|
||||||
|
|
||||||
|
class MLPPWGAN {
|
||||||
|
public:
|
||||||
|
MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
|
||||||
|
~MLPPWGAN();
|
||||||
|
std::vector<std::vector<real_t>> generateExample(int n);
|
||||||
|
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
|
||||||
|
real_t score();
|
||||||
|
void save(std::string fileName);
|
||||||
|
|
||||||
|
void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||||
|
void addOutputLayer(std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||||
|
|
||||||
|
private:
|
||||||
|
std::vector<std::vector<real_t>> modelSetTestGenerator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
|
||||||
|
std::vector<real_t> modelSetTestDiscriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the WGAN.
|
||||||
|
|
||||||
|
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||||
|
|
||||||
|
void forwardPass();
|
||||||
|
void updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
|
||||||
|
void updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
|
||||||
|
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> computeDiscriminatorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||||
|
std::vector<std::vector<std::vector<real_t>>> computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||||
|
|
||||||
|
void UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||||
|
|
||||||
|
std::vector<std::vector<real_t>> outputSet;
|
||||||
|
std::vector<real_t> y_hat;
|
||||||
|
|
||||||
|
std::vector<MLPPOldHiddenLayer> network;
|
||||||
|
MLPPOldOutputLayer *outputLayer;
|
||||||
|
|
||||||
|
int n;
|
||||||
|
int k;
|
||||||
|
};
|
||||||
|
|
||||||
class MLPPWGANOld {
|
class MLPPWGANOld {
|
||||||
public:
|
public:
|
||||||
MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet);
|
MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet);
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user