mirror of https://github.com/Relintai/pmlpp.git
415 lines
16 KiB
C++
415 lines
16 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 "core/log/logger.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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Ref<MLPPMatrix> MLPPWGAN::get_output_set() {
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return _output_set;
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}
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void MLPPWGAN::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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_n = 0;
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if (_output_set.is_valid()) {
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_n = _output_set->size().y;
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}
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}
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int MLPPWGAN::get_k() const {
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return _k;
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}
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void MLPPWGAN::set_k(const int val) {
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_k = val;
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}
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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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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.onevecnv(_n));
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Ref<MLPPMatrix> generator_input_set;
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Ref<MLPPMatrix> discriminator_input_set;
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discriminator_input_set.instance();
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Ref<MLPPVector> ly_hat;
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Ref<MLPPVector> loutput_set;
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// Training of the discriminator.
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for (int i = 0; i < CRITIC_INTERATIONS; i++) {
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generator_input_set = alg.gaussian_noise(_n, _k);
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discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_input_set));
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discriminator_input_set->add_rows_mlpp_matrix(_output_set); // Fake + real inputs.
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ly_hat = model_set_test_discriminator(discriminator_input_set);
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loutput_set = alg.scalar_multiplynv(-1, alg.onevecnv(_n)); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
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Ref<MLPPVector> output_set_real = alg.onevecnv(_n);
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loutput_set->add_mlpp_vector(output_set_real); // Fake + real output scores.
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DiscriminatorGradientResult discriminator_gradient_results = compute_discriminator_gradients(ly_hat, loutput_set);
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Vector<Ref<MLPPMatrix>> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad;
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Ref<MLPPVector> output_discriminator_w_grad = discriminator_gradient_results.output_w_grad;
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cumulative_discriminator_hidden_layer_w_grad = alg.scalar_multiply_vm(learning_rate / _n, cumulative_discriminator_hidden_layer_w_grad);
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output_discriminator_w_grad = alg.scalar_multiplynv(learning_rate / _n, output_discriminator_w_grad);
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update_discriminator_parameters(cumulative_discriminator_hidden_layer_w_grad, output_discriminator_w_grad, learning_rate);
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}
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// Training of the generator.
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generator_input_set = alg.gaussian_noise(_n, _k);
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discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_input_set));
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ly_hat = model_set_test_discriminator(discriminator_input_set);
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loutput_set = alg.onevecnv(_n);
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Vector<Ref<MLPPMatrix>> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(_y_hat, loutput_set);
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cumulative_generator_hidden_layer_w_grad = alg.scalar_multiply_vm(learning_rate / _n, cumulative_generator_hidden_layer_w_grad);
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update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate);
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forward_pass();
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if (ui) {
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handle_ui(epoch, cost_prev, _y_hat, alg.onevecnv(_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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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.onevecnv(_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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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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_y_hat.instance();
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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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_y_hat.instance();
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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 (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.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_termv(_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.subtractionnm(layer->get_weights(), hidden_layer_updations[0]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(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.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(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.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(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_multnv(alg.transposenm(_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_productnm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(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 (int 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_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(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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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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Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
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Ref<MLPPVector> cost_deriv_vector = cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, output_set);
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Ref<MLPPVector> activation_deriv_vector = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z());
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_output_layer->set_delta(alg.hadamard_productnv(cost_deriv_vector, activation_deriv_vector));
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Ref<MLPPVector> output_w_grad = alg.mat_vec_multnv(alg.transposenm(_output_layer->get_input()), _output_layer->get_delta());
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output_w_grad = alg.additionnv(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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Ref<MLPPMatrix> activation_deriv_matrix = avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z());
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layer->set_delta(alg.hadamard_productnm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), activation_deriv_matrix));
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Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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cumulative_hidden_layer_w_grad.push_back(alg.additionnm(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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for (int i = _network.size() - 2; i >= 0; i--) {
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layer = _network[i];
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Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
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activation_deriv_matrix = avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z());
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layer->set_delta(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), activation_deriv_matrix));
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hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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cumulative_hidden_layer_w_grad.push_back(alg.additionnm(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 cumulative_hidden_layer_w_grad;
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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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PLOG_MSG("Layer " + itos(_network.size() + 1) + ":");
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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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PLOG_MSG("Layer " + itos(i + 1) + ":");
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MLPPUtilities::print_ui_mb(layer->get_weights(), layer->get_bias());
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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_output_set"), &MLPPWGAN::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPWGAN::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_k"), &MLPPWGAN::get_k);
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ClassDB::bind_method(D_METHOD("set_k", "val"), &MLPPWGAN::set_k);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "k"), "set_k", "get_k");
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ClassDB::bind_method(D_METHOD("generate_example", "n"), &MLPPWGAN::generate_example);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPWGAN::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPWGAN::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPWGAN::save);
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ClassDB::bind_method(D_METHOD("add_layer", "activation", "weight_init", "reg", "lambda", "alpha"), &MLPPWGAN::add_layer, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::REGULARIZATION_TYPE_NONE, 0.5, 0.5);
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ClassDB::bind_method(D_METHOD("add_output_layer", "weight_init", "reg", "lambda", "alpha"), &MLPPWGAN::add_output_layer, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::REGULARIZATION_TYPE_NONE, 0.5, 0.5);
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}
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