mirror of https://github.com/Relintai/pmlpp.git
295 lines
13 KiB
C++
295 lines
13 KiB
C++
//
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// WGAN.cpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "wgan.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <cmath>
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#include <iostream>
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namespace MLPP {
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WGAN::WGAN(double k, std::vector<std::vector<double>> outputSet) :
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outputSet(outputSet), n(outputSet.size()), k(k) {
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}
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WGAN::~WGAN() {
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delete outputLayer;
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}
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std::vector<std::vector<double>> WGAN::generateExample(int n) {
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LinAlg alg;
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return modelSetTestGenerator(alg.gaussianNoise(n, k));
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}
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void WGAN::gradientDescent(double learning_rate, int max_epoch, bool UI) {
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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forwardPass();
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const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter.
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while (true) {
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cost_prev = Cost(y_hat, alg.onevec(n));
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std::vector<std::vector<double>> generatorInputSet;
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std::vector<std::vector<double>> discriminatorInputSet;
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std::vector<double> y_hat;
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std::vector<double> outputSet;
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// Training of the discriminator.
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for (int i = 0; i < CRITIC_INTERATIONS; i++) {
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generatorInputSet = alg.gaussianNoise(n, k);
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discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
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discriminatorInputSet.insert(discriminatorInputSet.end(), WGAN::outputSet.begin(), WGAN::outputSet.end()); // Fake + real inputs.
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y_hat = modelSetTestDiscriminator(discriminatorInputSet);
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outputSet = alg.scalarMultiply(-1, alg.onevec(n)); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
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std::vector<double> outputSetReal = alg.onevec(n);
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outputSet.insert(outputSet.end(), outputSetReal.begin(), outputSetReal.end()); // Fake + real output scores.
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auto [cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad] = computeDiscriminatorGradients(y_hat, outputSet);
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cumulativeDiscriminatorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeDiscriminatorHiddenLayerWGrad);
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outputDiscriminatorWGrad = alg.scalarMultiply(learning_rate / n, outputDiscriminatorWGrad);
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updateDiscriminatorParameters(cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad, learning_rate);
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}
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// Training of the generator.
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generatorInputSet = alg.gaussianNoise(n, k);
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discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
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y_hat = modelSetTestDiscriminator(discriminatorInputSet);
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outputSet = alg.onevec(n);
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std::vector<std::vector<std::vector<double>>> cumulativeGeneratorHiddenLayerWGrad = computeGeneratorGradients(y_hat, outputSet);
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cumulativeGeneratorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeGeneratorHiddenLayerWGrad);
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updateGeneratorParameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate);
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forwardPass();
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if (UI) {
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WGAN::UI(epoch, cost_prev, WGAN::y_hat, alg.onevec(n));
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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double WGAN::score() {
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LinAlg alg;
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Utilities util;
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forwardPass();
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return util.performance(y_hat, alg.onevec(n));
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}
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void WGAN::save(std::string fileName) {
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Utilities util;
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if (!network.empty()) {
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util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
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for (int i = 1; i < network.size(); i++) {
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util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
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}
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
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} else {
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
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}
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}
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void WGAN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, double lambda, double alpha) {
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LinAlg alg;
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if (network.empty()) {
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network.push_back(HiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha));
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network[0].forwardPass();
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} else {
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network.push_back(HiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
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network[network.size() - 1].forwardPass();
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}
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}
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void WGAN::addOutputLayer(std::string weightInit, std::string reg, double lambda, double alpha) {
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LinAlg alg;
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if (!network.empty()) {
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outputLayer = new OutputLayer(network[network.size() - 1].n_hidden, "Linear", "WassersteinLoss", network[network.size() - 1].a, weightInit, "WeightClipping", -0.01, 0.01);
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} else { // Should never happen.
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outputLayer = new OutputLayer(k, "Linear", "WassersteinLoss", alg.gaussianNoise(n, k), weightInit, "WeightClipping", -0.01, 0.01);
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}
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}
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std::vector<std::vector<double>> WGAN::modelSetTestGenerator(std::vector<std::vector<double>> X) {
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if (!network.empty()) {
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network[0].input = X;
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network[0].forwardPass();
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for (int i = 1; i <= network.size() / 2; i++) {
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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}
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return network[network.size() / 2].a;
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}
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std::vector<double> WGAN::modelSetTestDiscriminator(std::vector<std::vector<double>> X) {
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if (!network.empty()) {
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for (int i = network.size() / 2 + 1; i < network.size(); i++) {
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if (i == network.size() / 2 + 1) {
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network[i].input = X;
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} else {
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network[i].input = network[i - 1].a;
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}
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network[i].forwardPass();
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}
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outputLayer->input = network[network.size() - 1].a;
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}
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outputLayer->forwardPass();
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return outputLayer->a;
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}
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double WGAN::Cost(std::vector<double> y_hat, std::vector<double> y) {
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Reg regularization;
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class Cost cost;
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double totalRegTerm = 0;
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auto cost_function = outputLayer->cost_map[outputLayer->cost];
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if (!network.empty()) {
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for (int i = 0; i < network.size() - 1; i++) {
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totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
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}
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}
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return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
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}
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void WGAN::forwardPass() {
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LinAlg alg;
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if (!network.empty()) {
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network[0].input = alg.gaussianNoise(n, k);
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network[0].forwardPass();
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for (int i = 1; i < network.size(); i++) {
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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outputLayer->input = network[network.size() - 1].a;
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} else { // Should never happen, though.
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outputLayer->input = alg.gaussianNoise(n, k);
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}
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outputLayer->forwardPass();
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y_hat = outputLayer->a;
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}
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void WGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate) {
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LinAlg alg;
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outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
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outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
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if (!network.empty()) {
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network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]);
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network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta));
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for (int i = network.size() - 2; i > network.size() / 2; i--) {
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network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
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network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
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}
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}
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}
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void WGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, double learning_rate) {
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LinAlg alg;
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if (!network.empty()) {
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for (int i = network.size() / 2; i >= 0; i--) {
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//std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl;
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//std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl;
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network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
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network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
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}
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}
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}
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std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> WGAN::computeDiscriminatorGradients(std::vector<double> y_hat, std::vector<double> outputSet) {
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class Cost cost;
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Activation avn;
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LinAlg alg;
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Reg regularization;
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std::vector<std::vector<std::vector<double>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
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auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
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auto outputAvn = outputLayer->activation_map[outputLayer->activation];
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outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
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std::vector<double> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
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outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
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if (!network.empty()) {
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auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
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network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
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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.
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//std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl;
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//std::cout << "WEIGHTS SECOND:" << network[network.size() - 1].weights.size() << "x" << network[network.size() - 1].weights[0].size() << std::endl;
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for (int i = network.size() - 2; i > network.size() / 2; i--) {
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auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
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network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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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.
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}
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}
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return { cumulativeHiddenLayerWGrad, outputWGrad };
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}
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std::vector<std::vector<std::vector<double>>> WGAN::computeGeneratorGradients(std::vector<double> y_hat, std::vector<double> outputSet) {
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class Cost cost;
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Activation avn;
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LinAlg alg;
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Reg regularization;
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std::vector<std::vector<std::vector<double>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
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auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
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auto outputAvn = outputLayer->activation_map[outputLayer->activation];
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outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
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std::vector<double> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
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outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
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if (!network.empty()) {
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auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
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network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
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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.
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for (int i = network.size() - 2; i >= 0; i--) {
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auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
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network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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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.
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}
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}
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return cumulativeHiddenLayerWGrad;
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}
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void WGAN::UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet) {
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Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
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Utilities::UI(outputLayer->weights, outputLayer->bias);
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if (!network.empty()) {
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for (int i = network.size() - 1; i >= 0; i--) {
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std::cout << "Layer " << i + 1 << ": " << std::endl;
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Utilities::UI(network[i].weights, network[i].bias);
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}
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}
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}
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} //namespace MLPP
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