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https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
Initial cleanup.
This commit is contained in:
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@ -14,31 +14,23 @@
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#include <cmath>
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#include <iostream>
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MLPPWGAN::MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet) :
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outputSet(outputSet), n(outputSet.size()), k(k) {
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}
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MLPPWGAN::~MLPPWGAN() {
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delete outputLayer;
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}
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std::vector<std::vector<real_t>> MLPPWGAN::generateExample(int n) {
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std::vector<std::vector<real_t>> MLPPWGAN::generate_example(int n) {
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MLPPLinAlg alg;
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return modelSetTestGenerator(alg.gaussianNoise(n, k));
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return model_set_test_generator(alg.gaussianNoise(n, k));
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}
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void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCost cost;
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void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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//MLPPCost mlpp_cost;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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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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cost_prev = cost(y_hat, alg.onevec(n));
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std::vector<std::vector<real_t>> generatorInputSet;
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std::vector<std::vector<real_t>> discriminatorInputSet;
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@ -49,36 +41,37 @@ void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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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 = model_set_test_generator(generatorInputSet);
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discriminatorInputSet.insert(discriminatorInputSet.end(), MLPPWGAN::outputSet.begin(), MLPPWGAN::outputSet.end()); // Fake + real inputs.
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y_hat = modelSetTestDiscriminator(discriminatorInputSet);
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y_hat = model_set_test_discriminator(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<real_t> 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 discriminator_gradient_results = computeDiscriminatorGradients(y_hat, outputSet);
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auto discriminator_gradient_results = compute_discriminator_gradients(y_hat, outputSet);
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auto cumulativeDiscriminatorHiddenLayerWGrad = std::get<0>(discriminator_gradient_results);
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auto outputDiscriminatorWGrad = std::get<1>(discriminator_gradient_results);
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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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update_discriminator_parameters(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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discriminatorInputSet = model_set_test_generator(generatorInputSet);
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y_hat = model_set_test_discriminator(discriminatorInputSet);
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outputSet = alg.onevec(n);
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std::vector<std::vector<std::vector<real_t>>> cumulativeGeneratorHiddenLayerWGrad = computeGeneratorGradients(y_hat, outputSet);
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std::vector<std::vector<std::vector<real_t>>> cumulativeGeneratorHiddenLayerWGrad = compute_generator_gradients(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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update_generator_parameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate);
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forward_pass();
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forwardPass();
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if (UI) {
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MLPPWGAN::UI(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevec(n));
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handle_ui(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevec(n));
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}
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epoch++;
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@ -91,7 +84,7 @@ void MLPPWGAN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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real_t MLPPWGAN::score() {
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MLPPLinAlg alg;
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MLPPUtilities util;
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forwardPass();
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forward_pass();
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return util.performance(y_hat, alg.onevec(n));
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}
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@ -108,7 +101,7 @@ void MLPPWGAN::save(std::string fileName) {
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}
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}
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void MLPPWGAN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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void MLPPWGAN::add_layer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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if (network.empty()) {
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network.push_back(MLPPOldHiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha));
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@ -119,7 +112,7 @@ void MLPPWGAN::addLayer(int n_hidden, std::string activation, std::string weight
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}
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}
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void MLPPWGAN::addOutputLayer(std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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void MLPPWGAN::add_output_layer(std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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if (!network.empty()) {
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outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, "Linear", "WassersteinLoss", network[network.size() - 1].a, weightInit, "WeightClipping", -0.01, 0.01);
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@ -128,7 +121,18 @@ void MLPPWGAN::addOutputLayer(std::string weightInit, std::string reg, real_t la
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}
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}
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std::vector<std::vector<real_t>> MLPPWGAN::modelSetTestGenerator(std::vector<std::vector<real_t>> X) {
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MLPPWGAN::MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet) :
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outputSet(outputSet), n(outputSet.size()), k(k) {
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}
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MLPPWGAN::MLPPWGAN() {
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}
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MLPPWGAN::~MLPPWGAN() {
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delete outputLayer;
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}
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std::vector<std::vector<real_t>> MLPPWGAN::model_set_test_generator(std::vector<std::vector<real_t>> 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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@ -141,7 +145,7 @@ std::vector<std::vector<real_t>> MLPPWGAN::modelSetTestGenerator(std::vector<std
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return network[network.size() / 2].a;
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}
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std::vector<real_t> MLPPWGAN::modelSetTestDiscriminator(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> MLPPWGAN::model_set_test_discriminator(std::vector<std::vector<real_t>> X) {
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if (!network.empty()) {
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for (uint32_t i = network.size() / 2 + 1; i < network.size(); i++) {
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if (i == network.size() / 2 + 1) {
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@ -157,7 +161,7 @@ std::vector<real_t> MLPPWGAN::modelSetTestDiscriminator(std::vector<std::vector<
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return outputLayer->a;
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}
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real_t MLPPWGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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real_t MLPPWGAN::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPReg regularization;
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class MLPPCost cost;
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real_t totalRegTerm = 0;
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@ -171,7 +175,7 @@ real_t MLPPWGAN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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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 MLPPWGAN::forwardPass() {
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void MLPPWGAN::forward_pass() {
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MLPPLinAlg alg;
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if (!network.empty()) {
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network[0].input = alg.gaussianNoise(n, k);
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@ -189,7 +193,7 @@ void MLPPWGAN::forwardPass() {
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y_hat = outputLayer->a;
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}
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void MLPPWGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
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void MLPPWGAN::update_discriminator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
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MLPPLinAlg alg;
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outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
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@ -206,7 +210,7 @@ void MLPPWGAN::updateDiscriminatorParameters(std::vector<std::vector<std::vector
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}
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}
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void MLPPWGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
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void MLPPWGAN::update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
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MLPPLinAlg alg;
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if (!network.empty()) {
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@ -219,7 +223,7 @@ void MLPPWGAN::updateGeneratorParameters(std::vector<std::vector<std::vector<rea
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}
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}
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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) {
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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPWGAN::compute_discriminator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
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class MLPPCost cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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@ -255,7 +259,7 @@ std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> M
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return { cumulativeHiddenLayerWGrad, outputWGrad };
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}
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std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
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std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
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class MLPPCost cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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@ -284,8 +288,8 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::computeGeneratorGradient
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return cumulativeHiddenLayerWGrad;
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}
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void MLPPWGAN::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
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std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
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MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);
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if (!network.empty()) {
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@ -296,6 +300,11 @@ void MLPPWGAN::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::v
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}
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}
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void MLPPWGAN::_bind_methods() {
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//ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPWGAN::get_input_set);
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//ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPWGAN::set_input_set);
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//ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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}
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// ======== OLD ==========
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@ -8,6 +8,15 @@
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/containers/vector.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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@ -15,31 +24,38 @@
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#include <tuple>
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#include <vector>
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class MLPPWGAN {
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class MLPPWGAN : public Reference {
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GDCLASS(MLPPWGAN, Reference);
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public:
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MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
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~MLPPWGAN();
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std::vector<std::vector<real_t>> generateExample(int n);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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std::vector<std::vector<real_t>> generate_example(int n);
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void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false);
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real_t score();
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void save(std::string fileName);
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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);
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void addOutputLayer(std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void add_layer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void add_output_layer(std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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private:
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std::vector<std::vector<real_t>> modelSetTestGenerator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
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std::vector<real_t> modelSetTestDiscriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the WGAN.
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MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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MLPPWGAN();
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~MLPPWGAN();
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void forwardPass();
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void updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
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void updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
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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);
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std::vector<std::vector<std::vector<real_t>>> computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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protected:
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std::vector<std::vector<real_t>> model_set_test_generator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
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std::vector<real_t> model_set_test_discriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the WGAN.
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void UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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void forward_pass();
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void update_discriminator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
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void update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> compute_discriminator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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std::vector<std::vector<std::vector<real_t>>> compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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void handle_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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static void _bind_methods();
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std::vector<std::vector<real_t>> outputSet;
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std::vector<real_t> y_hat;
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