mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
286 lines
13 KiB
C++
286 lines
13 KiB
C++
//
|
|
// GAN.cpp
|
|
//
|
|
// Created by Marc Melikyan on 11/4/20.
|
|
//
|
|
|
|
#include "gan.h"
|
|
#include "../activation/activation.h"
|
|
#include "../cost/cost.h"
|
|
#include "../lin_alg/lin_alg.h"
|
|
#include "../regularization/reg.h"
|
|
#include "../utilities/utilities.h"
|
|
|
|
#include <cmath>
|
|
#include <iostream>
|
|
|
|
|
|
MLPPGAN::MLPPGAN(double k, std::vector<std::vector<double>> outputSet) :
|
|
outputSet(outputSet), n(outputSet.size()), k(k) {
|
|
}
|
|
|
|
MLPPGAN::~MLPPGAN() {
|
|
delete outputLayer;
|
|
}
|
|
|
|
std::vector<std::vector<double>> MLPPGAN::generateExample(int n) {
|
|
MLPPLinAlg alg;
|
|
return modelSetTestGenerator(alg.gaussianNoise(n, k));
|
|
}
|
|
|
|
void MLPPGAN::gradientDescent(double learning_rate, int max_epoch, bool UI) {
|
|
class MLPPCost cost;
|
|
MLPPLinAlg alg;
|
|
double cost_prev = 0;
|
|
int epoch = 1;
|
|
forwardPass();
|
|
|
|
while (true) {
|
|
cost_prev = Cost(y_hat, alg.onevec(n));
|
|
|
|
// Training of the discriminator.
|
|
|
|
std::vector<std::vector<double>> generatorInputSet = alg.gaussianNoise(n, k);
|
|
std::vector<std::vector<double>> discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
|
|
discriminatorInputSet.insert(discriminatorInputSet.end(), outputSet.begin(), outputSet.end()); // Fake + real inputs.
|
|
|
|
std::vector<double> y_hat = modelSetTestDiscriminator(discriminatorInputSet);
|
|
std::vector<double> outputSet = alg.zerovec(n);
|
|
std::vector<double> outputSetReal = alg.onevec(n);
|
|
outputSet.insert(outputSet.end(), outputSetReal.begin(), outputSetReal.end()); // Fake + real output scores.
|
|
|
|
auto [cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad] = computeDiscriminatorGradients(y_hat, outputSet);
|
|
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<double>>> cumulativeGeneratorHiddenLayerWGrad = computeGeneratorGradients(y_hat, outputSet);
|
|
cumulativeGeneratorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeGeneratorHiddenLayerWGrad);
|
|
updateGeneratorParameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate);
|
|
|
|
forwardPass();
|
|
if (UI) {
|
|
MLPPGAN::UI(epoch, cost_prev, MLPPGAN::y_hat, alg.onevec(n));
|
|
}
|
|
|
|
epoch++;
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
double MLPPGAN::score() {
|
|
MLPPLinAlg alg;
|
|
Utilities util;
|
|
forwardPass();
|
|
return util.performance(y_hat, alg.onevec(n));
|
|
}
|
|
|
|
void MLPPGAN::save(std::string fileName) {
|
|
Utilities 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, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
|
|
} else {
|
|
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
|
|
}
|
|
}
|
|
|
|
void MLPPGAN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, double lambda, double alpha) {
|
|
MLPPLinAlg alg;
|
|
if (network.empty()) {
|
|
network.push_back(MLPPHiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha));
|
|
network[0].forwardPass();
|
|
} else {
|
|
network.push_back(MLPPHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
|
|
network[network.size() - 1].forwardPass();
|
|
}
|
|
}
|
|
|
|
void MLPPGAN::addOutputLayer(std::string weightInit, std::string reg, double lambda, double alpha) {
|
|
MLPPLinAlg alg;
|
|
if (!network.empty()) {
|
|
outputLayer = new OutputLayer(network[network.size() - 1].n_hidden, "Sigmoid", "LogLoss", network[network.size() - 1].a, weightInit, reg, lambda, alpha);
|
|
} else {
|
|
outputLayer = new OutputLayer(k, "Sigmoid", "LogLoss", alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha);
|
|
}
|
|
}
|
|
|
|
std::vector<std::vector<double>> MLPPGAN::modelSetTestGenerator(std::vector<std::vector<double>> X) {
|
|
if (!network.empty()) {
|
|
network[0].input = X;
|
|
network[0].forwardPass();
|
|
|
|
for (int 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<double> MLPPGAN::modelSetTestDiscriminator(std::vector<std::vector<double>> X) {
|
|
if (!network.empty()) {
|
|
for (int 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;
|
|
}
|
|
|
|
double MLPPGAN::Cost(std::vector<double> y_hat, std::vector<double> y) {
|
|
Reg regularization;
|
|
class MLPPCost cost;
|
|
double totalRegTerm = 0;
|
|
|
|
auto cost_function = outputLayer->cost_map[outputLayer->cost];
|
|
if (!network.empty()) {
|
|
for (int 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 MLPPGAN::forwardPass() {
|
|
MLPPLinAlg alg;
|
|
if (!network.empty()) {
|
|
network[0].input = alg.gaussianNoise(n, k);
|
|
network[0].forwardPass();
|
|
|
|
for (int 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 MLPPGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double 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 (int 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 MLPPGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, double learning_rate) {
|
|
MLPPLinAlg alg;
|
|
|
|
if (!network.empty()) {
|
|
for (int 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<double>>>, std::vector<double>> MLPPGAN::computeDiscriminatorGradients(std::vector<double> y_hat, std::vector<double> outputSet) {
|
|
class MLPPCost cost;
|
|
MLPPActivation avn;
|
|
MLPPLinAlg alg;
|
|
Reg regularization;
|
|
|
|
std::vector<std::vector<std::vector<double>>> 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<double> 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<double>> 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 (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<double>> 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.
|
|
}
|
|
}
|
|
return { cumulativeHiddenLayerWGrad, outputWGrad };
|
|
}
|
|
|
|
std::vector<std::vector<std::vector<double>>> MLPPGAN::computeGeneratorGradients(std::vector<double> y_hat, std::vector<double> outputSet) {
|
|
class MLPPCost cost;
|
|
MLPPActivation avn;
|
|
MLPPLinAlg alg;
|
|
Reg regularization;
|
|
|
|
std::vector<std::vector<std::vector<double>>> 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<double> 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<double>> 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<double>> 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.
|
|
}
|
|
}
|
|
return cumulativeHiddenLayerWGrad;
|
|
}
|
|
|
|
void MLPPGAN::UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet) {
|
|
Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
|
|
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
|
|
Utilities::UI(outputLayer->weights, outputLayer->bias);
|
|
if (!network.empty()) {
|
|
for (int i = network.size() - 1; i >= 0; i--) {
|
|
std::cout << "Layer " << i + 1 << ": " << std::endl;
|
|
Utilities::UI(network[i].weights, network[i].bias);
|
|
}
|
|
}
|
|
}
|