diff --git a/mlpp/hidden_layer/hidden_layer.cpp b/mlpp/hidden_layer/hidden_layer.cpp index 2aae58b..119abdb 100644 --- a/mlpp/hidden_layer/hidden_layer.cpp +++ b/mlpp/hidden_layer/hidden_layer.cpp @@ -11,14 +11,14 @@ #include #include -int MLPPHiddenLayer::get_n_hidden() { +int MLPPHiddenLayer::get_n_hidden() const { return n_hidden; } void MLPPHiddenLayer::set_n_hidden(const int val) { n_hidden = val; } -MLPPActivation::ActivationFunction MLPPHiddenLayer::get_activation() { +MLPPActivation::ActivationFunction MLPPHiddenLayer::get_activation() const { return activation; } void MLPPHiddenLayer::set_activation(const MLPPActivation::ActivationFunction val) { @@ -81,28 +81,28 @@ void MLPPHiddenLayer::set_delta(const Ref &val) { delta = val; } -MLPPReg::RegularizationType MLPPHiddenLayer::get_reg() { +MLPPReg::RegularizationType MLPPHiddenLayer::get_reg() const { return reg; } void MLPPHiddenLayer::set_reg(const MLPPReg::RegularizationType val) { reg = val; } -real_t MLPPHiddenLayer::get_lambda() { +real_t MLPPHiddenLayer::get_lambda() const { return lambda; } void MLPPHiddenLayer::set_lambda(const real_t val) { lambda = val; } -real_t MLPPHiddenLayer::get_alpha() { +real_t MLPPHiddenLayer::get_alpha() const { return alpha; } void MLPPHiddenLayer::set_alpha(const real_t val) { alpha = val; } -MLPPUtilities::WeightDistributionType MLPPHiddenLayer::get_weight_init() { +MLPPUtilities::WeightDistributionType MLPPHiddenLayer::get_weight_init() const { return weight_init; } void MLPPHiddenLayer::set_weight_init(const MLPPUtilities::WeightDistributionType val) { diff --git a/mlpp/hidden_layer/hidden_layer.h b/mlpp/hidden_layer/hidden_layer.h index 541ff40..341624c 100644 --- a/mlpp/hidden_layer/hidden_layer.h +++ b/mlpp/hidden_layer/hidden_layer.h @@ -28,7 +28,7 @@ class MLPPHiddenLayer : public Reference { GDCLASS(MLPPHiddenLayer, Reference); public: - int get_n_hidden(); + int get_n_hidden() const; void set_n_hidden(const int val); MLPPActivation::ActivationFunction get_activation(); @@ -58,16 +58,16 @@ public: Ref get_delta(); void set_delta(const Ref &val); - MLPPReg::RegularizationType get_reg(); + MLPPReg::RegularizationType get_reg() const; void set_reg(const MLPPReg::RegularizationType val); - real_t get_lambda(); + real_t get_lambda() const; void set_lambda(const real_t val); - real_t get_alpha(); + real_t get_alpha() const; void set_alpha(const real_t val); - MLPPUtilities::WeightDistributionType get_weight_init(); + MLPPUtilities::WeightDistributionType get_weight_init() const; void set_weight_init(const MLPPUtilities::WeightDistributionType val); void forward_pass(); diff --git a/mlpp/lin_alg/lin_alg.cpp b/mlpp/lin_alg/lin_alg.cpp index 8f8fccd..dbc3d49 100644 --- a/mlpp/lin_alg/lin_alg.cpp +++ b/mlpp/lin_alg/lin_alg.cpp @@ -41,6 +41,25 @@ std::vector> MLPPLinAlg::gaussianNoise(int n, int m) { return A; } +Ref MLPPLinAlg::gaussian_noise(int n, int m) { + std::random_device rd; + std::default_random_engine generator(rd()); + std::normal_distribution distribution(0, 1); // Standard normal distribution. Mean of 0, std of 1. + + Ref A; + A.instance(); + A->resize(Size2i(m, n)); + + int a_data_size = A->data_size(); + real_t *a_ptr = A->ptrw(); + + for (int i = 0; i < a_data_size; ++i) { + a_ptr[i] = distribution(generator); + } + + return A; +} + std::vector> MLPPLinAlg::addition(std::vector> A, std::vector> B) { std::vector> C; C.resize(A.size()); diff --git a/mlpp/lin_alg/lin_alg.h b/mlpp/lin_alg/lin_alg.h index b8ef3bb..0fdcc8f 100644 --- a/mlpp/lin_alg/lin_alg.h +++ b/mlpp/lin_alg/lin_alg.h @@ -27,6 +27,7 @@ public: bool linearIndependenceChecker(std::vector> A); std::vector> gaussianNoise(int n, int m); + Ref gaussian_noise(int n, int m); std::vector> addition(std::vector> A, std::vector> B); std::vector> subtraction(std::vector> A, std::vector> B); diff --git a/mlpp/wgan/wgan.cpp b/mlpp/wgan/wgan.cpp index b16b7a5..ebfac30 100644 --- a/mlpp/wgan/wgan.cpp +++ b/mlpp/wgan/wgan.cpp @@ -11,15 +11,19 @@ #include "../regularization/reg.h" #include "../utilities/utilities.h" +#include "core/object/method_bind_ext.gen.inc" + #include #include -std::vector> MLPPWGAN::generate_example(int n) { +Ref MLPPWGAN::generate_example(int n) { MLPPLinAlg alg; - return model_set_test_generator(alg.gaussianNoise(n, k)); + + return model_set_test_generator(alg.gaussian_noise(n, k)); } -void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) { +/* +void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) { //MLPPCost mlpp_cost; MLPPLinAlg alg; real_t cost_prev = 0; @@ -30,7 +34,7 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) { const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter. while (true) { - cost_prev = cost(y_hat, alg.onevec(n)); + cost_prev = cost(y_hat, alg.onevecv(n)); std::vector> generatorInputSet; std::vector> discriminatorInputSet; @@ -70,8 +74,8 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) { forward_pass(); - if (UI) { - handle_ui(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevec(n)); + if (ui) { + handle_ui(epoch, cost_prev, MLPPWGAN::y_hat, alg.onevecv(n)); } epoch++; @@ -80,186 +84,239 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool UI) { } } } +*/ real_t MLPPWGAN::score() { MLPPLinAlg alg; MLPPUtilities util; forward_pass(); - return util.performance(y_hat, alg.onevec(n)); + return util.performance_vec(y_hat, alg.onevecv(n)); } -void MLPPWGAN::save(std::string fileName) { +void MLPPWGAN::save(const String &file_name) { MLPPUtilities util; + + /* if (!network.empty()) { - util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1); + util.saveParameters(file_name, network[0].weights, network[0].bias, 0, 1); for (uint32_t i = 1; i < network.size(); i++) { util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1); } - util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1); + util.saveParameters(file_name, outputLayer->weights, outputLayer->bias, 1, network.size() + 1); } else { - util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1); + util.saveParameters(file_name, outputLayer->weights, outputLayer->bias, 0, network.size() + 1); } + */ } -void MLPPWGAN::add_layer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) { +void MLPPWGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) { MLPPLinAlg alg; + + Ref layer; + layer.instance(); + + layer->set_n_hidden(n_hidden); + layer->set_activation(activation); + layer->set_weight_init(weight_init); + layer->set_reg(reg); + layer->set_lambda(lambda); + layer->set_alpha(alpha); + if (network.empty()) { - network.push_back(MLPPOldHiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha)); - network[0].forwardPass(); + layer->set_input(alg.gaussian_noise(n, k)); } else { - network.push_back(MLPPOldHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha)); - network[network.size() - 1].forwardPass(); + layer->set_input(network.write[network.size() - 1]->get_a()); } + + network.push_back(layer); + layer->forward_pass(); } -void MLPPWGAN::add_output_layer(std::string weightInit, std::string reg, real_t lambda, real_t alpha) { +void MLPPWGAN::add_output_layer(MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) { MLPPLinAlg alg; - if (!network.empty()) { - outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, "Linear", "WassersteinLoss", network[network.size() - 1].a, weightInit, "WeightClipping", -0.01, 0.01); - } else { // Should never happen. - outputLayer = new MLPPOldOutputLayer(k, "Linear", "WassersteinLoss", alg.gaussianNoise(n, k), weightInit, "WeightClipping", -0.01, 0.01); + + ERR_FAIL_COND(network.empty()); + + if (!output_layer.is_valid()) { + output_layer.instance(); } + + output_layer->set_n_hidden(network[network.size() - 1]->get_n_hidden()); + output_layer->set_activation(MLPPActivation::ACTIVATION_FUNCTION_LINEAR); + output_layer->set_cost(MLPPCost::COST_TYPE_WASSERSTEIN_LOSS); + output_layer->set_input(network.write[network.size() - 1]->get_a()); + output_layer->set_weight_init(weight_init); + output_layer->set_lambda(lambda); + output_layer->set_alpha(alpha); } -MLPPWGAN::MLPPWGAN(real_t k, std::vector> outputSet) : - outputSet(outputSet), n(outputSet.size()), k(k) { +MLPPWGAN::MLPPWGAN(real_t p_k, const Ref &p_output_set) { + output_set = p_output_set; + n = p_output_set->size().y; + k = p_k; } MLPPWGAN::MLPPWGAN() { + n = 0; + k = 0; } MLPPWGAN::~MLPPWGAN() { - delete outputLayer; } -std::vector> MLPPWGAN::model_set_test_generator(std::vector> X) { +Ref MLPPWGAN::model_set_test_generator(const Ref &X) { if (!network.empty()) { - network[0].input = X; - network[0].forwardPass(); + network.write[0]->set_input(X); + network.write[0]->forward_pass(); - for (uint32_t i = 1; i <= network.size() / 2; i++) { - network[i].input = network[i - 1].a; - network[i].forwardPass(); + for (int i = 1; i <= network.size() / 2; ++i) { + network.write[i]->set_input(network.write[i - 1]->get_a()); + network.write[i]->forward_pass(); } } - return network[network.size() / 2].a; + + return network.write[network.size() / 2]->get_a(); } -std::vector MLPPWGAN::model_set_test_discriminator(std::vector> X) { +Ref MLPPWGAN::model_set_test_discriminator(const Ref &X) { if (!network.empty()) { for (uint32_t i = network.size() / 2 + 1; i < network.size(); i++) { if (i == network.size() / 2 + 1) { - network[i].input = X; + network.write[i]->set_input(X); } else { - network[i].input = network[i - 1].a; + network.write[i]->set_input(network.write[i - 1]->get_a()); } - network[i].forwardPass(); + network.write[i]->forward_pass(); } - outputLayer->input = network[network.size() - 1].a; + + output_layer->set_input(network.write[network.size() - 1]->get_a()); } - outputLayer->forwardPass(); - return outputLayer->a; + + output_layer->forward_pass(); + + return output_layer->get_a(); } -real_t MLPPWGAN::cost(std::vector y_hat, std::vector y) { +real_t MLPPWGAN::cost(const Ref &y_hat, const Ref &y) { MLPPReg regularization; - class MLPPCost cost; - real_t totalRegTerm = 0; + MLPPCost mlpp_cost; - auto cost_function = outputLayer->cost_map[outputLayer->cost]; - if (!network.empty()) { - for (uint32_t i = 0; i < network.size() - 1; i++) { - totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg); - } + real_t total_reg_term = 0; + + for (int i = 0; i < network.size() - 1; ++i) { + Ref layer = network[i]; + + total_reg_term += regularization.reg_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()); } - return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg); + + total_reg_term += regularization.reg_termm(output_layer->get_weights(), output_layer->get_lambda(), output_layer->get_alpha(), output_layer->get_reg()); + + return mlpp_cost.run_cost_norm_vector(output_layer->get_cost(), y_hat, y) + total_reg_term; } void MLPPWGAN::forward_pass() { MLPPLinAlg alg; - if (!network.empty()) { - network[0].input = alg.gaussianNoise(n, k); - network[0].forwardPass(); - for (uint32_t i = 1; i < network.size(); i++) { - network[i].input = network[i - 1].a; - network[i].forwardPass(); + if (!network.empty()) { + Ref layer = network[0]; + + layer->set_input(alg.gaussian_noise(n, k)); + layer->forward_pass(); + + for (int i = 1; i < network.size(); i++) { + layer = network[i]; + + layer->set_input(network.write[i - 1]->get_a()); + layer->forward_pass(); } - outputLayer->input = network[network.size() - 1].a; + + output_layer->set_input(network.write[network.size() - 1]->get_a()); } else { // Should never happen, though. - outputLayer->input = alg.gaussianNoise(n, k); + output_layer->set_input(alg.gaussian_noise(n, k)); } - outputLayer->forwardPass(); - y_hat = outputLayer->a; + + output_layer->forward_pass(); + + y_hat->set_from_mlpp_vector(output_layer->get_a()); } -void MLPPWGAN::update_discriminator_parameters(std::vector>> hiddenLayerUpdations, std::vector outputLayerUpdation, real_t learning_rate) { +void MLPPWGAN::update_discriminator_parameters(Vector> hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate) { MLPPLinAlg alg; - outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation); - outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n; + output_layer->set_weights(alg.subtractionnv(output_layer->get_weights(), output_layer_updation)); + output_layer->set_bias(output_layer->get_bias() - learning_rate * alg.sum_elementsv(output_layer->get_delta()) / n); if (!network.empty()) { - network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]); - network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta)); + Ref layer = network[network.size() - 1]; - for (uint32_t i = network.size() - 2; i > network.size() / 2; i--) { - network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]); - network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta)); + layer->set_weights(alg.subtractionm(layer->get_weights(), hidden_layer_updations[0])); + layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / n, layer->get_delta()))); + + for (int i = network.size() - 2; i > network.size() / 2; i--) { + layer = network[i]; + + 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()))); } } } -void MLPPWGAN::update_generator_parameters(std::vector>> hiddenLayerUpdations, real_t learning_rate) { +void MLPPWGAN::update_generator_parameters(Vector> 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 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> MLPPWGAN::compute_discriminator_gradients(std::vector y_hat, std::vector outputSet) { - class MLPPCost cost; +MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(const Ref &y_hat, const Ref &output_set) { + MLPPCost mlpp_cost; MLPPActivation avn; MLPPLinAlg alg; MLPPReg regularization; - std::vector>> 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 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 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> 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 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> hiddenLayerWGradl = alg.matmult(alg.transpose(network[i].input), network[i].delta); + layer = network[i]; + Ref 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>> MLPPWGAN::compute_generator_gradients(std::vector y_hat, std::vector outputSet) { +/* +Vector> MLPPWGAN::compute_generator_gradients(const Ref &y_hat, const Ref &output_set) { class MLPPCost cost; MLPPActivation avn; MLPPLinAlg alg; @@ -267,14 +324,15 @@ std::vector>> MLPPWGAN::compute_generator_gradie std::vector>> 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 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 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> 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>> 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 y_hat, std::vector outputSet) { - MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet)); +void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref &y_hat, const Ref &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 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); } } } diff --git a/mlpp/wgan/wgan.h b/mlpp/wgan/wgan.h index 341f20a..78557a7 100644 --- a/mlpp/wgan/wgan.h +++ b/mlpp/wgan/wgan.h @@ -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 #include #include @@ -28,40 +33,46 @@ class MLPPWGAN : public Reference { GDCLASS(MLPPWGAN, Reference); public: - std::vector> generate_example(int n); - void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false); + Ref 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> outputSet); + MLPPWGAN(real_t k, const Ref &output_set); MLPPWGAN(); ~MLPPWGAN(); protected: - std::vector> model_set_test_generator(std::vector> X); // Evaluator for the generator of the WGAN. - std::vector model_set_test_discriminator(std::vector> X); // Evaluator for the discriminator of the WGAN. + Ref model_set_test_generator(const Ref &X); // Evaluator for the generator of the WGAN. + Ref model_set_test_discriminator(const Ref &X); // Evaluator for the discriminator of the WGAN. - real_t cost(std::vector y_hat, std::vector y); + real_t cost(const Ref &y_hat, const Ref &y); void forward_pass(); - void update_discriminator_parameters(std::vector>> hiddenLayerUpdations, std::vector outputLayerUpdation, real_t learning_rate); - void update_generator_parameters(std::vector>> hiddenLayerUpdations, real_t learning_rate); - std::tuple>>, std::vector> compute_discriminator_gradients(std::vector y_hat, std::vector outputSet); - std::vector>> compute_generator_gradients(std::vector y_hat, std::vector outputSet); + void update_discriminator_parameters(Vector> hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate); + void update_generator_parameters(Vector> hidden_layer_updations, real_t learning_rate); - void handle_ui(int epoch, real_t cost_prev, std::vector y_hat, std::vector outputSet); + struct DiscriminatorGradientResult { + Vector> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. + Ref output_w_grad; + }; + + DiscriminatorGradientResult compute_discriminator_gradients(const Ref &y_hat, const Ref &output_set); + Vector> compute_generator_gradients(const Ref &y_hat, const Ref &output_set); + + void handle_ui(int epoch, real_t cost_prev, const Ref &y_hat, const Ref &output_set); static void _bind_methods(); - std::vector> outputSet; - std::vector y_hat; + Ref output_set; + Ref y_hat; - std::vector network; - MLPPOldOutputLayer *outputLayer; + Vector> network; + Ref output_layer; int n; int k;