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Now MLPPGAN uses engine classes.
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parent
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commit
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283
mlpp/gan/gan.cpp
283
mlpp/gan/gan.cpp
@ -11,6 +11,8 @@
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include "core/log/logger.h"
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#include <cmath>
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#include <iostream>
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@ -37,10 +39,10 @@ void MLPPGAN::set_k(const int val) {
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}
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*/
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std::vector<std::vector<real_t>> MLPPGAN::generate_example(int n) {
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Ref<MLPPMatrix> MLPPGAN::generate_example(int n) {
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MLPPLinAlg alg;
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return model_set_test_generator(alg.gaussianNoise(n, _k));
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return model_set_test_generator(alg.gaussian_noise(n, _k));
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}
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void MLPPGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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@ -52,41 +54,39 @@ void MLPPGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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forward_pass();
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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.onevecv(_n));
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// Training of the discriminator.
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std::vector<std::vector<real_t>> generator_input_set = alg.gaussianNoise(_n, _k);
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std::vector<std::vector<real_t>> discriminator_input_set = model_set_test_generator(generator_input_set);
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discriminator_input_set.insert(discriminator_input_set.end(), _output_set.begin(), _output_set.end()); // Fake + real inputs.
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Ref<MLPPMatrix> generator_input_set = alg.gaussian_noise(_n, _k);
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Ref<MLPPMatrix> discriminator_input_set = 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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std::vector<real_t> y_hat = model_set_test_discriminator(discriminator_input_set);
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std::vector<real_t> _output_set = alg.zerovec(_n);
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std::vector<real_t> _output_setReal = alg.onevec(_n);
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_output_set.insert(_output_set.end(), _output_setReal.begin(), _output_setReal.end()); // Fake + real output scores.
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Ref<MLPPVector> y_hat = model_set_test_discriminator(discriminator_input_set);
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Ref<MLPPVector> output_set = alg.zerovecv(_n);
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Ref<MLPPVector> output_set_real = alg.onevecv(_n);
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output_set->add_mlpp_vector(output_set_real); // Fake + real output scores.
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auto dgrads = compute_discriminator_gradients(y_hat, _output_set);
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auto cumulative_discriminator_hidden_layer_w_grad = std::get<0>(dgrads);
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auto outputDiscriminatorWGrad = std::get<1>(dgrads);
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ComputeDiscriminatorGradientsResult dgrads = compute_discriminator_gradients(y_hat, _output_set);
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cumulative_discriminator_hidden_layer_w_grad = alg.scalarMultiply(learning_rate / _n, cumulative_discriminator_hidden_layer_w_grad);
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outputDiscriminatorWGrad = alg.scalarMultiply(learning_rate / _n, outputDiscriminatorWGrad);
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update_discriminator_parameters(cumulative_discriminator_hidden_layer_w_grad, outputDiscriminatorWGrad, learning_rate);
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dgrads.cumulative_hidden_layer_w_grad = alg.scalar_multiply_vm(learning_rate / _n, dgrads.cumulative_hidden_layer_w_grad);
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dgrads.output_w_grad = alg.scalar_multiplynv(learning_rate / _n, dgrads.output_w_grad);
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update_discriminator_parameters(dgrads.cumulative_hidden_layer_w_grad, dgrads.output_w_grad, learning_rate);
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// Training of the generator.
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generator_input_set = alg.gaussianNoise(_n, _k);
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generator_input_set = alg.gaussian_noise(_n, _k);
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discriminator_input_set = model_set_test_generator(generator_input_set);
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y_hat = model_set_test_discriminator(discriminator_input_set);
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_output_set = alg.onevec(_n);
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_output_set = alg.onevecv(_n);
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std::vector<std::vector<std::vector<real_t>>> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(y_hat, _output_set);
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cumulative_generator_hidden_layer_w_grad = alg.scalarMultiply(learning_rate / _n, cumulative_generator_hidden_layer_w_grad);
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Vector<Ref<MLPPMatrix>> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(y_hat, _output_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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print_ui(epoch, cost_prev, _y_hat, alg.onevec(_n));
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print_ui(epoch, cost_prev, _y_hat, alg.onevecv(_n));
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}
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epoch++;
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@ -103,46 +103,54 @@ real_t MLPPGAN::score() {
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forward_pass();
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return util.performance(_y_hat, alg.onevec(_n));
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return util.performance_vec(_y_hat, alg.onevecv(_n));
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}
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void MLPPGAN::save(std::string fileName) {
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void MLPPGAN::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(fileName, _network[0].weights, _network[0].bias, false, 1);
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util.saveParameters(file_name, _network[0].weights, _network[0].bias, false, 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, true, i + 1);
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util.saveParameters(file_name, _network[i].weights, _network[i].bias, true, i + 1);
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}
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util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
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util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
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} else {
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util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
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util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
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}
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*/
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}
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void MLPPGAN::add_layer(int n_hidden, std::string activation, std::string weight_init, std::string reg, real_t lambda, real_t alpha) {
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void MLPPGAN::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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if (_network.empty()) {
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_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, alg.gaussianNoise(_n, _k), weight_init, reg, lambda, alpha));
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_network[0].forwardPass();
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Ref<MLPPHiddenLayer> layer = Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, alg.gaussian_noise(_n, _k), weight_init, reg, lambda, alpha)));
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_network.push_back(layer);
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_network.write[0]->forward_pass();
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} else {
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_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, _network[_network.size() - 1].a, weight_init, reg, lambda, alpha));
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_network[_network.size() - 1].forwardPass();
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Ref<MLPPHiddenLayer> layer = Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha)));
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_network.push_back(layer);
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_network.write[_network.size() - 1]->forward_pass();
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}
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}
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void MLPPGAN::add_output_layer(std::string weight_init, std::string reg, real_t lambda, real_t alpha) {
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void MLPPGAN::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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if (!_network.empty()) {
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_output_layer = new MLPPOldOutputLayer(_network[_network.size() - 1].n_hidden, "Sigmoid", "LogLoss", _network[_network.size() - 1].a, weight_init, reg, lambda, alpha);
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_output_layer = Ref<MLPPOutputLayer>(memnew(MLPPOutputLayer(_network.write[_network.size() - 1]->get_n_hidden(), MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha)));
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} else {
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_output_layer = new MLPPOldOutputLayer(_k, "Sigmoid", "LogLoss", alg.gaussianNoise(_n, _k), weight_init, reg, lambda, alpha);
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_output_layer = Ref<MLPPOutputLayer>(memnew(MLPPOutputLayer(_k, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, alg.gaussian_noise(_n, _k), weight_init, reg, lambda, alpha)));
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}
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}
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MLPPGAN::MLPPGAN(real_t k, std::vector<std::vector<real_t>> output_set) {
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MLPPGAN::MLPPGAN(real_t k, const Ref<MLPPMatrix> &output_set) {
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_output_set = output_set;
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_n = _output_set.size();
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_n = _output_set->size().y;
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_k = k;
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}
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@ -150,183 +158,210 @@ MLPPGAN::MLPPGAN() {
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}
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MLPPGAN::~MLPPGAN() {
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delete _output_layer;
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}
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std::vector<std::vector<real_t>> MLPPGAN::model_set_test_generator(std::vector<std::vector<real_t>> X) {
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Ref<MLPPMatrix> MLPPGAN::model_set_test_generator(const Ref<MLPPMatrix> &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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_network.write[0]->set_input(X);
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_network.write[0]->forward_pass();
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for (uint32_t i = 1; i <= _network.size() / 2; i++) {
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_network[i].input = _network[i - 1].a;
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_network[i].forwardPass();
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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[_network.size() / 2].a;
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return _network.write[_network.size() / 2]->get_a();
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}
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std::vector<real_t> MLPPGAN::model_set_test_discriminator(std::vector<std::vector<real_t>> X) {
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Ref<MLPPVector> MLPPGAN::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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for (int i = _network.size() / 2 + 1; i < _network.size(); i++) {
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if (i == _network.size() / 2 + 1) {
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_network[i].input = X;
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_network.write[i]->set_input(X);
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} else {
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_network[i].input = _network[i - 1].a;
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_network.write[i]->set_input(_network.write[i - 1]->get_a());
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}
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_network[i].forwardPass();
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_network.write[i]->forward_pass();
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}
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_output_layer->input = _network[_network.size() - 1].a;
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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->forwardPass();
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_output_layer->forward_pass();
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return _output_layer->a;
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return _output_layer->get_a();
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}
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real_t MLPPGAN::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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real_t MLPPGAN::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &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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auto cost_function = _output_layer->cost_map[_output_layer->cost];
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MLPPCost mlpp_cost;
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real_t total_reg_term = 0;
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if (!_network.empty()) {
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for (uint32_t i = 0; i < _network.size() - 1; i++) {
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totalRegTerm += regularization.regTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg);
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for (int i = 0; i < _network.size() - 1; i++) {
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total_reg_term += regularization.reg_termm(_network.write[i]->get_weights(), _network.write[i]->get_lambda(), _network.write[i]->get_alpha(), _network.write[i]->get_reg());
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}
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}
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return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg);
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return mlpp_cost.run_cost_norm_vector(_output_layer->get_cost(), y_hat, y) + 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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}
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void MLPPGAN::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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_network[0].forwardPass();
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_network.write[0]->set_input(alg.gaussian_noise(_n, _k));
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_network.write[0]->forward_pass();
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].input = _network[i - 1].a;
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_network[i].forwardPass();
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for (int i = 1; i < _network.size(); 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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_output_layer->input = _network[_network.size() - 1].a;
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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->input = alg.gaussianNoise(_n, _k);
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_output_layer->set_input(alg.gaussian_noise(_n, _k));
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}
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_output_layer->forwardPass();
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_y_hat = _output_layer->a;
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_output_layer->forward_pass();
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_y_hat = _output_layer->get_a();
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}
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void MLPPGAN::update_discriminator_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, std::vector<real_t> output_layer_updation, real_t learning_rate) {
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void MLPPGAN::update_discriminator_parameters(const 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->weights = alg.subtraction(_output_layer->weights, output_layer_updation);
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_output_layer->bias -= learning_rate * alg.sum_elements(_output_layer->delta) / _n;
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_output_layer->set_weights(alg.subtractionnv(_output_layer->get_weights(), output_layer_updation));
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real_t output_layer_bias = _output_layer->get_bias();
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output_layer_bias -= learning_rate * alg.sum_elementsv(_output_layer->get_delta()) / _n;
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_output_layer->set_bias(output_layer_bias);
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if (!_network.empty()) {
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_network[_network.size() - 1].weights = alg.subtraction(_network[_network.size() - 1].weights, hidden_layer_updations[0]);
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_network[_network.size() - 1].bias = alg.subtractMatrixRows(_network[_network.size() - 1].bias, alg.scalarMultiply(learning_rate / _n, _network[_network.size() - 1].delta));
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Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
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for (int i = static_cast<int>(_network.size()) - 2; i > static_cast<int>(_network.size()) / 2; i--) {
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_network[i].weights = alg.subtraction(_network[i].weights, hidden_layer_updations[(_network.size() - 2) - i + 1]);
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_network[i].bias = alg.subtractMatrixRows(_network[i].bias, alg.scalarMultiply(learning_rate / _n, _network[i].delta));
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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 MLPPGAN::update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, real_t learning_rate) {
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void MLPPGAN::update_generator_parameters(const 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 << hidden_layer_updations[(network.size() - 2) - i + 1].size() << "x" << hidden_layer_updations[(network.size() - 2) - i + 1][0].size() << std::endl;
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_network[i].weights = alg.subtraction(_network[i].weights, hidden_layer_updations[(_network.size() - 2) - i + 1]);
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_network[i].bias = alg.subtractMatrixRows(_network[i].bias, alg.scalarMultiply(learning_rate / _n, _network[i].delta));
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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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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPGAN::compute_discriminator_gradients(std::vector<real_t> y_hat, std::vector<real_t> _output_set) {
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class MLPPCost cost;
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MLPPGAN::ComputeDiscriminatorGradientsResult MLPPGAN::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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std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
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ComputeDiscriminatorGradientsResult res;
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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, _output_set), (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));
|
||||
Ref<MLPPVector> cost_deriv = mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, _output_set);
|
||||
Ref<MLPPVector> activ_deriv = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z());
|
||||
|
||||
_output_layer->set_delta(alg.hadamard_productnv(cost_deriv, activ_deriv));
|
||||
|
||||
res.output_w_grad = alg.mat_vec_multv(alg.transposem(_output_layer->get_input()), _output_layer->get_delta());
|
||||
res.output_w_grad = alg.additionnv(res.output_w_grad, regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg()));
|
||||
|
||||
if (!_network.empty()) {
|
||||
auto hiddenLayerAvn = _network[_network.size() - 1].activation_map[_network[_network.size() - 1].activation];
|
||||
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
|
||||
|
||||
_network[_network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(_output_layer->delta, _output_layer->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);
|
||||
Ref<MLPPVector> hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z());
|
||||
|
||||
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.
|
||||
layer->set_delta(alg.hadamard_productm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), hidden_layer_activ_deriv));
|
||||
Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
|
||||
|
||||
//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;
|
||||
res.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.
|
||||
|
||||
for (int i = static_cast<int>(_network.size()) - 2; i > static_cast<int>(_network.size()) / 2; i--) {
|
||||
hiddenLayerAvn = _network[i].activation_map[_network[i].activation];
|
||||
_network[i].delta = alg.hadamard_product(alg.matmult(_network[i + 1].delta, alg.transpose(_network[i + 1].weights)), (avn.*hiddenLayerAvn)(_network[i].z, 1));
|
||||
hiddenLayerWGrad = alg.matmult(alg.transpose(_network[i].input), _network[i].delta);
|
||||
layer = _network[i];
|
||||
Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
|
||||
|
||||
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z());
|
||||
|
||||
layer->set_delta(alg.hadamard_productm(alg.matmultm(next_layer->get_delta(), alg.transposem(next_layer->get_weights())), hidden_layer_activ_deriv));
|
||||
hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
|
||||
|
||||
res.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.
|
||||
}
|
||||
}
|
||||
return { cumulativeHiddenLayerWGrad, outputWGrad };
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> MLPPGAN::compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> _output_set) {
|
||||
class MLPPCost cost;
|
||||
Vector<Ref<MLPPMatrix>> MLPPGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
|
||||
MLPPCost mlpp_cost;
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
|
||||
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
|
||||
auto costDeriv = _output_layer->costDeriv_map[_output_layer->cost];
|
||||
auto outputAvn = _output_layer->activation_map[_output_layer->activation];
|
||||
_output_layer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, _output_set), (avn.*outputAvn)(_output_layer->z, true));
|
||||
std::vector<real_t> outputWGrad = alg.mat_vec_mult(alg.transpose(_output_layer->input), _output_layer->delta);
|
||||
outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg));
|
||||
Ref<MLPPVector> cost_deriv = mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, _output_set);
|
||||
Ref<MLPPVector> activ_deriv = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z());
|
||||
|
||||
_output_layer->set_delta(alg.hadamard_productnv(cost_deriv, activ_deriv));
|
||||
|
||||
Ref<MLPPVector> output_w_grad = alg.mat_vec_multv(alg.transposem(_output_layer->get_input()), _output_layer->get_delta());
|
||||
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()));
|
||||
|
||||
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(_output_layer->delta, _output_layer->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.
|
||||
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
|
||||
|
||||
Ref<MLPPVector> hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z());
|
||||
|
||||
layer->set_delta(alg.hadamard_productnv(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), hidden_layer_activ_deriv));
|
||||
|
||||
Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
|
||||
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.
|
||||
|
||||
for (int i = _network.size() - 2; i >= 0; i--) {
|
||||
hiddenLayerAvn = _network[i].activation_map[_network[i].activation];
|
||||
_network[i].delta = alg.hadamard_product(alg.matmult(_network[i + 1].delta, alg.transpose(_network[i + 1].weights)), (avn.*hiddenLayerAvn)(_network[i].z, true));
|
||||
hiddenLayerWGrad = alg.matmult(alg.transpose(_network[i].input), _network[i].delta);
|
||||
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
layer = _network[i];
|
||||
Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
|
||||
|
||||
hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z());
|
||||
|
||||
layer->set_delta(alg.hadamard_productm(alg.matmultm(next_layer->get_delta(), alg.transposem(next_layer->get_weights())), hidden_layer_activ_deriv));
|
||||
|
||||
hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
|
||||
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.
|
||||
}
|
||||
}
|
||||
|
||||
return cumulativeHiddenLayerWGrad;
|
||||
return cumulative_hidden_layer_w_grad;
|
||||
}
|
||||
|
||||
void MLPPGAN::print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> _output_set) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, _output_set));
|
||||
std::cout << "Layer " << _network.size() + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(_output_layer->weights, _output_layer->bias);
|
||||
void MLPPGAN::print_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, _output_set));
|
||||
|
||||
PLOG_MSG("Layer " + itos(_network.size() + 1) + ": ");
|
||||
MLPPUtilities::print_ui_vb(_output_layer->get_weights(), _output_layer->get_bias());
|
||||
|
||||
if (!_network.empty()) {
|
||||
for (int i = _network.size() - 1; i >= 0; i--) {
|
||||
std::cout << "Layer " << i + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(_network[i].weights, _network[i].bias);
|
||||
Ref<MLPPHiddenLayer> layer = _network[i];
|
||||
|
||||
PLOG_MSG("Layer " + itos(i + 1) + ": ");
|
||||
MLPPUtilities::print_ui_mb(layer->get_weights(), layer->get_bias());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -15,12 +15,8 @@
|
||||
#include "../hidden_layer/hidden_layer.h"
|
||||
#include "../output_layer/output_layer.h"
|
||||
|
||||
#include "../hidden_layer/hidden_layer_old.h"
|
||||
#include "../output_layer/output_layer_old.h"
|
||||
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
#include "../activation/activation.h"
|
||||
#include "../utilities/utilities.h"
|
||||
|
||||
class MLPPGAN : public Reference {
|
||||
GDCLASS(MLPPGAN, Reference);
|
||||
@ -37,45 +33,50 @@ public:
|
||||
void set_k(const int val);
|
||||
*/
|
||||
|
||||
std::vector<std::vector<real_t>> generate_example(int n);
|
||||
Ref<MLPPMatrix> generate_example(int n);
|
||||
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
|
||||
real_t score();
|
||||
|
||||
void save(std::string file_name);
|
||||
void save(const String &file_name);
|
||||
|
||||
void add_layer(int n_hidden, std::string activation, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
|
||||
MLPPGAN(real_t k, std::vector<std::vector<real_t>> output_set);
|
||||
MLPPGAN(real_t k, const Ref<MLPPMatrix> &output_set);
|
||||
|
||||
MLPPGAN();
|
||||
~MLPPGAN();
|
||||
|
||||
protected:
|
||||
std::vector<std::vector<real_t>> model_set_test_generator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the gan.
|
||||
std::vector<real_t> model_set_test_discriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the gan.
|
||||
Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the gan.
|
||||
Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the gan.
|
||||
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
void update_discriminator_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, std::vector<real_t> output_layer_updation, real_t learning_rate);
|
||||
void update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, real_t learning_rate);
|
||||
void update_discriminator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
||||
void update_generator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, real_t learning_rate);
|
||||
|
||||
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> output_set);
|
||||
std::vector<std::vector<std::vector<real_t>>> compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> output_set);
|
||||
struct ComputeDiscriminatorGradientsResult {
|
||||
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
Ref<MLPPVector> output_w_grad;
|
||||
};
|
||||
|
||||
void print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> output_set);
|
||||
ComputeDiscriminatorGradientsResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
Vector<Ref<MLPPMatrix>> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
|
||||
void print_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
Ref<MLPPMatrix> _output_set;
|
||||
Ref<MLPPVector> _y_hat;
|
||||
|
||||
std::vector<MLPPOldHiddenLayer> _network;
|
||||
MLPPOldOutputLayer *_output_layer;
|
||||
Vector<Ref<MLPPHiddenLayer>> _network;
|
||||
Ref<MLPPOutputLayer> _output_layer;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user