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656 lines
28 KiB
C++
656 lines
28 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 "core/object/method_bind_ext.gen.inc"
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#include <cmath>
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#include <iostream>
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Ref<MLPPMatrix> MLPPWGAN::generate_example(int n) {
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MLPPLinAlg alg;
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return model_set_test_generator(alg.gaussian_noise(n, k));
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}
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/*
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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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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.onevecv(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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std::vector<real_t> y_hat;
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std::vector<real_t> 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 = 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 = 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 = 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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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 = 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 = compute_generator_gradients(y_hat, outputSet);
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cumulativeGeneratorHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeGeneratorHiddenLayerWGrad);
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update_generator_parameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate);
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forward_pass();
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if (ui) {
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handle_ui(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevecv(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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*/
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real_t MLPPWGAN::score() {
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MLPPLinAlg alg;
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MLPPUtilities util;
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forward_pass();
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return util.performance_vec(y_hat, alg.onevecv(n));
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}
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void MLPPWGAN::save(const String &file_name) {
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MLPPUtilities util;
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/*
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if (!network.empty()) {
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util.saveParameters(file_name, network[0].weights, network[0].bias, 0, 1);
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for (uint32_t 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(file_name, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
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} else {
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util.saveParameters(file_name, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
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}
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*/
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}
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void MLPPWGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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Ref<MLPPHiddenLayer> layer;
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layer.instance();
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layer->set_n_hidden(n_hidden);
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layer->set_activation(activation);
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layer->set_weight_init(weight_init);
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layer->set_reg(reg);
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layer->set_lambda(lambda);
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layer->set_alpha(alpha);
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if (network.empty()) {
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layer->set_input(alg.gaussian_noise(n, k));
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} else {
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layer->set_input(network.write[network.size() - 1]->get_a());
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}
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network.push_back(layer);
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layer->forward_pass();
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}
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void MLPPWGAN::add_output_layer(MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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ERR_FAIL_COND(network.empty());
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if (!output_layer.is_valid()) {
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output_layer.instance();
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}
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output_layer->set_n_hidden(network[network.size() - 1]->get_n_hidden());
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output_layer->set_activation(MLPPActivation::ACTIVATION_FUNCTION_LINEAR);
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output_layer->set_cost(MLPPCost::COST_TYPE_WASSERSTEIN_LOSS);
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output_layer->set_input(network.write[network.size() - 1]->get_a());
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output_layer->set_weight_init(weight_init);
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output_layer->set_lambda(lambda);
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output_layer->set_alpha(alpha);
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}
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MLPPWGAN::MLPPWGAN(real_t p_k, const Ref<MLPPMatrix> &p_output_set) {
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output_set = p_output_set;
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n = p_output_set->size().y;
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k = p_k;
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}
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MLPPWGAN::MLPPWGAN() {
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n = 0;
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k = 0;
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}
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MLPPWGAN::~MLPPWGAN() {
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}
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Ref<MLPPMatrix> MLPPWGAN::model_set_test_generator(const Ref<MLPPMatrix> &X) {
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if (!network.empty()) {
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network.write[0]->set_input(X);
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network.write[0]->forward_pass();
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for (int i = 1; i <= network.size() / 2; ++i) {
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network.write[i]->set_input(network.write[i - 1]->get_a());
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network.write[i]->forward_pass();
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}
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}
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return network.write[network.size() / 2]->get_a();
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}
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Ref<MLPPVector> MLPPWGAN::model_set_test_discriminator(const Ref<MLPPMatrix> &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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network.write[i]->set_input(X);
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} else {
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network.write[i]->set_input(network.write[i - 1]->get_a());
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}
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network.write[i]->forward_pass();
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}
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output_layer->set_input(network.write[network.size() - 1]->get_a());
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}
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output_layer->forward_pass();
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return output_layer->get_a();
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}
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real_t MLPPWGAN::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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MLPPReg regularization;
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MLPPCost mlpp_cost;
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real_t total_reg_term = 0;
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for (int i = 0; i < network.size() - 1; ++i) {
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Ref<MLPPHiddenLayer> layer = network[i];
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total_reg_term += regularization.reg_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg());
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}
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total_reg_term += regularization.reg_termm(output_layer->get_weights(), output_layer->get_lambda(), output_layer->get_alpha(), output_layer->get_reg());
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return mlpp_cost.run_cost_norm_vector(output_layer->get_cost(), y_hat, y) + total_reg_term;
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}
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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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Ref<MLPPHiddenLayer> layer = network[0];
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layer->set_input(alg.gaussian_noise(n, k));
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layer->forward_pass();
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for (int i = 1; i < network.size(); i++) {
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layer = network[i];
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layer->set_input(network.write[i - 1]->get_a());
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layer->forward_pass();
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}
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output_layer->set_input(network.write[network.size() - 1]->get_a());
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} else { // Should never happen, though.
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output_layer->set_input(alg.gaussian_noise(n, k));
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}
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output_layer->forward_pass();
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y_hat->set_from_mlpp_vector(output_layer->get_a());
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}
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void MLPPWGAN::update_discriminator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
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MLPPLinAlg alg;
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output_layer->set_weights(alg.subtractionnv(output_layer->get_weights(), output_layer_updation));
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output_layer->set_bias(output_layer->get_bias() - learning_rate * alg.sum_elementsv(output_layer->get_delta()) / n);
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if (!network.empty()) {
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Ref<MLPPHiddenLayer> layer = network[network.size() - 1];
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layer->set_weights(alg.subtractionm(layer->get_weights(), hidden_layer_updations[0]));
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layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / n, layer->get_delta())));
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for (int i = network.size() - 2; i > network.size() / 2; i--) {
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layer = network[i];
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layer->set_weights(alg.subtractionm(layer->get_weights(), hidden_layer_updations[(network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / n, layer->get_delta())));
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}
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}
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}
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void MLPPWGAN::update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate) {
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MLPPLinAlg 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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Ref<MLPPHiddenLayer> layer = network[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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layer->set_weights(alg.subtractionm(layer->get_weights(), hidden_layer_updations[(network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / n, layer->get_delta())));
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}
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}
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}
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MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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DiscriminatorGradientResult data;
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output_layer->set_delta(alg.hadamard_productnv(mlpp_cost.run_cost_deriv_vector(output_layer->get_cost(), y_hat, output_set), avn.run_activation_deriv_vector(output_layer->get_activation(), output_layer->get_z())));
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data.output_w_grad = alg.mat_vec_multv(alg.transposem(output_layer->get_input()), output_layer->get_delta());
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data.output_w_grad = alg.additionnv(data.output_w_grad, regularization.reg_deriv_termv(output_layer->get_weights(), output_layer->get_lambda(), output_layer->get_alpha(), output_layer->get_reg()));
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if (!network.empty()) {
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Ref<MLPPHiddenLayer> layer = network[network.size() - 1];
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layer->set_delta(alg.hadamard_productm(alg.outer_product(output_layer->get_delta(), output_layer->get_weights()), avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z())));
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Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_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:" << layer.weights.size() << "x" << layer.weights[0].size() << std::endl;
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for (uint32_t i = network.size() - 2; i > network.size() / 2; i--) {
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layer = network[i];
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Ref<MLPPHiddenLayer> next_layer = network[i + 1];
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layer->set_delta(alg.hadamard_productm(alg.matmultm(next_layer->get_delta(), alg.transposem(next_layer->get_weights())), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_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 data;
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}
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/*
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Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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class MLPPCost cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
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auto costDeriv = output_layer->costDeriv_map[output_layer->cost];
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auto outputAvn = output_layer->activation_map[output_layer->activation];
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output_layer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(output_layer->z, 1));
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std::vector<real_t> outputWGrad = alg.mat_vec_mult(alg.transpose(output_layer->input), output_layer->delta);
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outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(output_layer->weights, output_layer->lambda, output_layer->alpha, output_layer->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(output_layer->delta, output_layer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
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std::vector<std::vector<real_t>> 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 (uint32_t i = network.size() - 2; i >= 0; i--) {
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auto hiddenLayerAvnl = 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.*hiddenLayerAvnl)(network[i].z, 1));
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std::vector<std::vector<real_t>> hiddenLayerWGradl = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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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.
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}
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}
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return cumulativeHiddenLayerWGrad;
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}
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*/
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void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, output_set));
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std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
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MLPPUtilities::print_ui_vb(output_layer->get_weights(), output_layer->get_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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Ref<MLPPHiddenLayer> layer = network[i];
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std::cout << "Layer " << i + 1 << ": " << std::endl;
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MLPPUtilities::print_ui_vib(layer->get_weights(), layer->get_bias(), 0);
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}
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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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MLPPWGANOld::MLPPWGANOld(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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MLPPWGANOld::~MLPPWGANOld() {
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delete outputLayer;
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}
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std::vector<std::vector<real_t>> MLPPWGANOld::generateExample(int n) {
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MLPPLinAlg alg;
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return modelSetTestGenerator(alg.gaussianNoise(n, k));
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}
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void MLPPWGANOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCost 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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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<real_t>> generatorInputSet;
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std::vector<std::vector<real_t>> discriminatorInputSet;
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std::vector<real_t> y_hat;
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std::vector<real_t> outputSet;
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// Training of the discriminator.
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for (int i = 0; i < CRITIC_INTERATIONS; i++) {
|
|
generatorInputSet = alg.gaussianNoise(n, k);
|
|
discriminatorInputSet = modelSetTestGenerator(generatorInputSet);
|
|
discriminatorInputSet.insert(discriminatorInputSet.end(), MLPPWGANOld::outputSet.begin(), MLPPWGANOld::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) {
|
|
MLPPWGANOld::UI(epoch, cost_prev, MLPPWGANOld::y_hat, alg.onevec(n));
|
|
}
|
|
|
|
epoch++;
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
real_t MLPPWGANOld::score() {
|
|
MLPPLinAlg alg;
|
|
MLPPUtilities util;
|
|
forwardPass();
|
|
return util.performance(y_hat, alg.onevec(n));
|
|
}
|
|
|
|
void MLPPWGANOld::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 MLPPWGANOld::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 MLPPWGANOld::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>> MLPPWGANOld::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> MLPPWGANOld::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 MLPPWGANOld::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 MLPPWGANOld::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 MLPPWGANOld::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 MLPPWGANOld::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>> MLPPWGANOld::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>>> MLPPWGANOld::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 MLPPWGANOld::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);
|
|
}
|
|
}
|
|
}
|