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Work on porting WGAN.
This commit is contained in:
parent
35f4a01bac
commit
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@ -11,14 +11,14 @@
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#include <iostream>
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#include <random>
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int MLPPHiddenLayer::get_n_hidden() {
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int MLPPHiddenLayer::get_n_hidden() const {
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return n_hidden;
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}
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void MLPPHiddenLayer::set_n_hidden(const int val) {
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n_hidden = val;
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}
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MLPPActivation::ActivationFunction MLPPHiddenLayer::get_activation() {
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MLPPActivation::ActivationFunction MLPPHiddenLayer::get_activation() const {
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return activation;
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}
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void MLPPHiddenLayer::set_activation(const MLPPActivation::ActivationFunction val) {
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@ -81,28 +81,28 @@ void MLPPHiddenLayer::set_delta(const Ref<MLPPMatrix> &val) {
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delta = val;
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}
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MLPPReg::RegularizationType MLPPHiddenLayer::get_reg() {
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MLPPReg::RegularizationType MLPPHiddenLayer::get_reg() const {
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return reg;
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}
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void MLPPHiddenLayer::set_reg(const MLPPReg::RegularizationType val) {
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reg = val;
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}
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real_t MLPPHiddenLayer::get_lambda() {
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real_t MLPPHiddenLayer::get_lambda() const {
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return lambda;
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}
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void MLPPHiddenLayer::set_lambda(const real_t val) {
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lambda = val;
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}
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real_t MLPPHiddenLayer::get_alpha() {
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real_t MLPPHiddenLayer::get_alpha() const {
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return alpha;
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}
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void MLPPHiddenLayer::set_alpha(const real_t val) {
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alpha = val;
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}
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MLPPUtilities::WeightDistributionType MLPPHiddenLayer::get_weight_init() {
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MLPPUtilities::WeightDistributionType MLPPHiddenLayer::get_weight_init() const {
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return weight_init;
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}
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void MLPPHiddenLayer::set_weight_init(const MLPPUtilities::WeightDistributionType val) {
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@ -28,7 +28,7 @@ class MLPPHiddenLayer : public Reference {
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GDCLASS(MLPPHiddenLayer, Reference);
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public:
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int get_n_hidden();
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int get_n_hidden() const;
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void set_n_hidden(const int val);
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MLPPActivation::ActivationFunction get_activation();
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@ -58,16 +58,16 @@ public:
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Ref<MLPPMatrix> get_delta();
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void set_delta(const Ref<MLPPMatrix> &val);
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MLPPReg::RegularizationType get_reg();
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MLPPReg::RegularizationType get_reg() const;
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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real_t get_lambda() const;
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void set_lambda(const real_t val);
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real_t get_alpha();
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real_t get_alpha() const;
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void set_alpha(const real_t val);
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MLPPUtilities::WeightDistributionType get_weight_init();
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MLPPUtilities::WeightDistributionType get_weight_init() const;
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void set_weight_init(const MLPPUtilities::WeightDistributionType val);
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void forward_pass();
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@ -41,6 +41,25 @@ std::vector<std::vector<real_t>> MLPPLinAlg::gaussianNoise(int n, int m) {
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return A;
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}
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Ref<MLPPMatrix> MLPPLinAlg::gaussian_noise(int n, int m) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::normal_distribution<real_t> distribution(0, 1); // Standard normal distribution. Mean of 0, std of 1.
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Ref<MLPPMatrix> A;
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A.instance();
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A->resize(Size2i(m, n));
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int a_data_size = A->data_size();
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real_t *a_ptr = A->ptrw();
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for (int i = 0; i < a_data_size; ++i) {
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a_ptr[i] = distribution(generator);
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}
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return A;
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}
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std::vector<std::vector<real_t>> MLPPLinAlg::addition(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
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std::vector<std::vector<real_t>> C;
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C.resize(A.size());
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@ -27,6 +27,7 @@ public:
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bool linearIndependenceChecker(std::vector<std::vector<real_t>> A);
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std::vector<std::vector<real_t>> gaussianNoise(int n, int m);
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Ref<MLPPMatrix> gaussian_noise(int n, int m);
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std::vector<std::vector<real_t>> addition(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B);
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std::vector<std::vector<real_t>> subtraction(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B);
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@ -11,15 +11,19 @@
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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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std::vector<std::vector<real_t>> MLPPWGAN::generate_example(int n) {
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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.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 MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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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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@ -30,7 +34,7 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter.
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while (true) {
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cost_prev = cost(y_hat, alg.onevec(n));
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cost_prev = cost(y_hat, alg.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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@ -70,8 +74,8 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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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.onevec(n));
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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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@ -80,186 +84,239 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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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(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 MLPPWGAN::save(std::string fileName) {
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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(fileName, network[0].weights, network[0].bias, 0, 1);
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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(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
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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(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
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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, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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void MLPPWGAN::add_layer(int n_hidden, 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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network.push_back(MLPPOldHiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha));
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network[0].forwardPass();
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layer->set_input(alg.gaussian_noise(n, k));
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} else {
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network.push_back(MLPPOldHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
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network[network.size() - 1].forwardPass();
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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(std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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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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if (!network.empty()) {
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outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, "Linear", "WassersteinLoss", network[network.size() - 1].a, weightInit, "WeightClipping", -0.01, 0.01);
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} else { // Should never happen.
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outputLayer = new MLPPOldOutputLayer(k, "Linear", "WassersteinLoss", alg.gaussianNoise(n, k), weightInit, "WeightClipping", -0.01, 0.01);
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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 k, std::vector<std::vector<real_t>> outputSet) :
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outputSet(outputSet), n(outputSet.size()), k(k) {
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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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delete outputLayer;
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}
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std::vector<std::vector<real_t>> MLPPWGAN::model_set_test_generator(std::vector<std::vector<real_t>> X) {
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Ref<MLPPMatrix> MLPPWGAN::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> MLPPWGAN::model_set_test_discriminator(std::vector<std::vector<real_t>> X) {
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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[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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outputLayer->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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outputLayer->forwardPass();
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return outputLayer->a;
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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(std::vector<real_t> y_hat, std::vector<real_t> y) {
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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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class MLPPCost cost;
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real_t totalRegTerm = 0;
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MLPPCost mlpp_cost;
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auto cost_function = outputLayer->cost_map[outputLayer->cost];
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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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}
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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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return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
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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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network[0].input = alg.gaussianNoise(n, k);
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network[0].forwardPass();
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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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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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outputLayer->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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outputLayer->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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outputLayer->forwardPass();
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y_hat = outputLayer->a;
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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(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
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void MLPPWGAN::update_discriminator_parameters(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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outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
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outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
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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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network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]);
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network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta));
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Ref<MLPPHiddenLayer> layer = network[network.size() - 1];
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for (uint32_t i = network.size() - 2; i > network.size() / 2; i--) {
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network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
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network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
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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(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate) {
|
||||
void MLPPWGAN::update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
if (!network.empty()) {
|
||||
for (uint32_t i = network.size() / 2; i >= 0; i--) {
|
||||
for (int i = network.size() / 2; i >= 0; i--) {
|
||||
Ref<MLPPHiddenLayer> layer = network[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));
|
||||
layer->set_weights(alg.subtractionm(layer->get_weights(), hidden_layer_updations[(network.size() - 2) - i + 1]));
|
||||
layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / n, layer->get_delta())));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPWGAN::compute_discriminator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
|
||||
class MLPPCost cost;
|
||||
MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_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.
|
||||
DiscriminatorGradientResult data;
|
||||
|
||||
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));
|
||||
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())));
|
||||
|
||||
data.output_w_grad = alg.mat_vec_multv(alg.transposem(output_layer->get_input()), output_layer->get_delta());
|
||||
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()));
|
||||
|
||||
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(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);
|
||||
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())));
|
||||
Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_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.
|
||||
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.
|
||||
|
||||
//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;
|
||||
//std::cout << "WEIGHTS SECOND:" << layer.weights.size() << "x" << layer.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);
|
||||
layer = network[i];
|
||||
Ref<MLPPHiddenLayer> next_layer = network[i + 1];
|
||||
|
||||
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.
|
||||
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())));
|
||||
|
||||
hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
|
||||
|
||||
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.
|
||||
}
|
||||
}
|
||||
return { cumulativeHiddenLayerWGrad, outputWGrad };
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
|
||||
/*
|
||||
Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
|
||||
class MLPPCost cost;
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
@ -267,14 +324,15 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::compute_generator_gradie
|
||||
|
||||
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));
|
||||
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, outputSet), (avn.*outputAvn)(output_layer->z, 1));
|
||||
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));
|
||||
|
||||
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));
|
||||
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.
|
||||
|
||||
@ -285,17 +343,24 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGAN::compute_generator_gradie
|
||||
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 MLPPWGAN::handle_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));
|
||||
void MLPPWGAN::handle_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));
|
||||
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);
|
||||
|
||||
MLPPUtilities::print_ui_vb(output_layer->get_weights(), output_layer->get_bias());
|
||||
|
||||
if (!network.empty()) {
|
||||
for (uint32_t i = network.size() - 1; i >= 0; i--) {
|
||||
for (int i = network.size() - 1; i >= 0; i--) {
|
||||
Ref<MLPPHiddenLayer> layer = network[i];
|
||||
|
||||
std::cout << "Layer " << i + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(network[i].weights, network[i].bias);
|
||||
|
||||
MLPPUtilities::print_ui_vib(layer->get_weights(), layer->get_bias(), 0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -20,6 +20,11 @@
|
||||
#include "../hidden_layer/hidden_layer.h"
|
||||
#include "../output_layer/output_layer.h"
|
||||
|
||||
#include "../activation/activation.h"
|
||||
#include "../cost/cost.h"
|
||||
#include "../regularization/reg.h"
|
||||
#include "../utilities/utilities.h"
|
||||
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
@ -28,40 +33,46 @@ class MLPPWGAN : public Reference {
|
||||
GDCLASS(MLPPWGAN, Reference);
|
||||
|
||||
public:
|
||||
std::vector<std::vector<real_t>> generate_example(int n);
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false);
|
||||
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 fileName);
|
||||
void save(const String &file_name);
|
||||
|
||||
void add_layer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(std::string weightInit = "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);
|
||||
|
||||
MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
|
||||
MLPPWGAN(real_t k, const Ref<MLPPMatrix> &output_set);
|
||||
|
||||
MLPPWGAN();
|
||||
~MLPPWGAN();
|
||||
|
||||
protected:
|
||||
std::vector<std::vector<real_t>> model_set_test_generator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
|
||||
std::vector<real_t> model_set_test_discriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the WGAN.
|
||||
Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the WGAN.
|
||||
Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the WGAN.
|
||||
|
||||
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>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
|
||||
void update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, 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> outputSet);
|
||||
std::vector<std::vector<std::vector<real_t>>> compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||
void update_discriminator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
||||
void update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate);
|
||||
|
||||
void handle_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||
struct DiscriminatorGradientResult {
|
||||
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
Ref<MLPPVector> output_w_grad;
|
||||
};
|
||||
|
||||
DiscriminatorGradientResult 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 handle_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>> outputSet;
|
||||
std::vector<real_t> y_hat;
|
||||
Ref<MLPPMatrix> output_set;
|
||||
Ref<MLPPVector> y_hat;
|
||||
|
||||
std::vector<MLPPOldHiddenLayer> network;
|
||||
MLPPOldOutputLayer *outputLayer;
|
||||
Vector<Ref<MLPPHiddenLayer>> network;
|
||||
Ref<MLPPOutputLayer> output_layer;
|
||||
|
||||
int n;
|
||||
int k;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user