From 47b29071fd1967e5a97634e295eabfcebd6f42fd Mon Sep 17 00:00:00 2001 From: novak_99 Date: Fri, 28 Jan 2022 17:27:35 -0800 Subject: [PATCH] pow --- .DS_Store | Bin 8196 -> 8196 bytes MLPP/.DS_Store | Bin 10244 -> 14340 bytes MLPP/ANN/ANN.cpp | 9 +- {SharedLib => MLPP/GAN}/.DS_Store | Bin 6148 -> 6148 bytes MLPP/GAN/GAN.cpp | 290 ++++++++++++++++++++++++++++++ MLPP/GAN/GAN.hpp | 57 ++++++ MLPP/LinAlg/LinAlg.cpp | 17 ++ MLPP/LinAlg/LinAlg.hpp | 2 + README.md | 2 +- a.out | Bin 68880 -> 2192472 bytes buildSO.sh | 4 +- main.cpp | 26 ++- 12 files changed, 394 insertions(+), 13 deletions(-) rename {SharedLib => MLPP/GAN}/.DS_Store (90%) create mode 100644 MLPP/GAN/GAN.cpp create mode 100644 MLPP/GAN/GAN.hpp diff --git a/.DS_Store b/.DS_Store index 94cb8b0bd08a68a7f8934a182275d904caf45582..ec4cd079eeabb9961e49498e689163f19f12e911 100644 GIT binary patch delta 101 zcmZp1XmOa}&&a(oU^hP__hcS{*~;t;J`A1=&J3;$ehk43uAVvh$w@i+Nem1O0t^g{ z;y`-F=M6OHr6sR F0{{y)8yo-t delta 534 zcmZp1XmOa}&nU1lU^hRbz+@hQ*^{pdeBd-PFf`RsFtRY6d`{47v#yXRlREv$3 z%233R$&k#DII&Q9vVjTvG6_f gWPTMxMuy2YJkpaBRM=T9Kx!u!s7&4bP?((=07t7iYXATM delta 170 zcmZoEXbF&DU|?W$DortDU{C-uIe-{M3-C-V6q~3gIoZI3MH0woFaTmkpg03V8ABpN z2}34BK11H-gA!islNA^vH?woFa4;Hd=23Xcw3%1s2lK=U=92})R5u$59O4jU1{nY} zmm5g9g3R75$oQRkGQWu-E6_-WhRKd5N}L)XB|xsh>>, std::vector> ANN::computeGradients(std::vector y_hat, std::vector outputSet){ + std::cout << "BEGIN" << std::endl; + std::cout << k << std::endl; class Cost cost; Activation avn; LinAlg alg; @@ -630,13 +632,12 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double 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); - 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. + cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. for(int i = network.size() - 2; i >= 0; i--){ auto hiddenLayerAvn = network[i].activation_map[network[i].activation]; - network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, network[i + 1].weights), (avn.*hiddenLayerAvn)(network[i].z, 1)); + network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1)); std::vector> 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. } diff --git a/SharedLib/.DS_Store b/MLPP/GAN/.DS_Store similarity index 90% rename from SharedLib/.DS_Store rename to MLPP/GAN/.DS_Store index df534f8601f6bd1e918ee17f81f7c46890b7a0ac..31a4fc183d92840c236a621ea569b6d027b35c52 100644 GIT binary patch delta 117 zcmZoMXfc=|&e%S&P;8=}B0m!Y0|O%ig8&0VGD87F!NfS-$p#`U{Ok +#include + +namespace MLPP { + GAN::GAN(double k, std::vector> outputSet) + : outputSet(outputSet), n(outputSet.size()), k(k) + { + + } + + GAN::~GAN(){ + delete outputLayer; + } + + std::vector> GAN::generateExample(int n){ + LinAlg alg; + return modelSetTestGenerator(alg.gaussianNoise(n, k)); + } + + void GAN::gradientDescent(double learning_rate, int max_epoch, bool UI){ + class Cost cost; + LinAlg alg; + double cost_prev = 0; + int epoch = 1; + forwardPass(); + + while(true){ + cost_prev = Cost(y_hat, alg.onevec(n)); + + // Training of the discriminator. + + std::vector> generatorInputSet = alg.gaussianNoise(n, k); + std::vector> discriminatorInputSet = modelSetTestGenerator(generatorInputSet); + discriminatorInputSet.insert(discriminatorInputSet.end(), outputSet.begin(), outputSet.end()); // Fake + real inputs. + + std::vector y_hat = modelSetTestDiscriminator(discriminatorInputSet); + std::vector outputSet = alg.zerovec(n); + std::vector outputSetReal = alg.onevec(n); + outputSet.insert(outputSet.end(), outputSetReal.begin(), outputSetReal.end()); // Fake + real output scores. + + auto [cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad] = computeDiscriminatorGradients(y_hat, outputSet); + cumulativeDiscriminatorHiddenLayerWGrad = alg.scalarMultiply(learning_rate/n, cumulativeDiscriminatorHiddenLayerWGrad); + outputDiscriminatorWGrad = alg.scalarMultiply(learning_rate/n, outputDiscriminatorWGrad); + updateDiscriminatorParameters(cumulativeDiscriminatorHiddenLayerWGrad, outputDiscriminatorWGrad, learning_rate); + + // Training of the generator. + generatorInputSet = alg.gaussianNoise(n, k); + discriminatorInputSet = modelSetTestGenerator(generatorInputSet); + y_hat = modelSetTestDiscriminator(discriminatorInputSet); + outputSet = alg.onevec(n); + + std::vector>> cumulativeGeneratorHiddenLayerWGrad = computeGeneratorGradients(y_hat, outputSet); + cumulativeGeneratorHiddenLayerWGrad = alg.scalarMultiply(learning_rate/n, cumulativeGeneratorHiddenLayerWGrad); + updateGeneratorParameters(cumulativeGeneratorHiddenLayerWGrad, learning_rate); + + forwardPass(); + if(UI) { GAN::UI(epoch, cost_prev, GAN::y_hat, alg.onevec(n)); } + + epoch++; + if(epoch > max_epoch) { break; } + } + } + + double GAN::score(){ + LinAlg alg; + Utilities util; + forwardPass(); + return util.performance(y_hat, alg.onevec(n)); + } + + void GAN::save(std::string fileName){ + Utilities util; + if(!network.empty()){ + util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1); + for(int i = 1; i < network.size(); i++){ + util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1); + } + util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1); + } + else{ + util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1); + } + } + + void GAN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, double lambda, double alpha){ + LinAlg alg; + if(network.empty()){ + network.push_back(HiddenLayer(n_hidden, activation, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha)); + network[0].forwardPass(); + } + else{ + network.push_back(HiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha)); + network[network.size() - 1].forwardPass(); + } + } + + void GAN::addOutputLayer(std::string activation, std::string loss, std::string weightInit, std::string reg, double lambda, double alpha){ + LinAlg alg; + if(!network.empty()){ + outputLayer = new OutputLayer(network[network.size() - 1].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha); + } + else{ + outputLayer = new OutputLayer(k, activation, loss, alg.gaussianNoise(n, k), weightInit, reg, lambda, alpha); + } + } + + std::vector> GAN::modelSetTestGenerator(std::vector> X){ + if(!network.empty()){ + network[0].input = X; + network[0].forwardPass(); + + for(int i = 1; i <= network.size()/2; i++){ + network[i].input = network[i - 1].a; + network[i].forwardPass(); + } + } + return network[network.size()/2].a; + } + + std::vector GAN::modelSetTestDiscriminator(std::vector> X){ + if(!network.empty()){ + for(int i = network.size()/2 + 1; i < network.size(); i++){ + if(i == network.size()/2 + 1){ + network[i].input = X; + } + else { network[i].input = network[i - 1].a; } + network[i].forwardPass(); + } + outputLayer->input = network[network.size() - 1].a; + } + outputLayer->forwardPass(); + return outputLayer->a; + } + + double GAN::Cost(std::vector y_hat, std::vector y){ + Reg regularization; + class Cost cost; + double totalRegTerm = 0; + + auto cost_function = outputLayer->cost_map[outputLayer->cost]; + if(!network.empty()){ + for(int i = 0; i < network.size() - 1; i++){ + totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg); + } + } + return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg); + } + + void GAN::forwardPass(){ + LinAlg alg; + if(!network.empty()){ + network[0].input = alg.gaussianNoise(n, k); + network[0].forwardPass(); + + for(int i = 1; i < network.size(); i++){ + network[i].input = network[i - 1].a; + network[i].forwardPass(); + } + outputLayer->input = network[network.size() - 1].a; + } + else{ // Should never happen, though. + outputLayer->input = alg.gaussianNoise(n, k); + } + outputLayer->forwardPass(); + y_hat = outputLayer->a; + } + + void GAN::updateDiscriminatorParameters(std::vector>> hiddenLayerUpdations, std::vector outputLayerUpdation, double learning_rate){ + LinAlg alg; + + outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation); + outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n; + + if(!network.empty()){ + network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]); + network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate/n, network[network.size() - 1].delta)); + + for(int i = network.size() - 2; i > network.size()/2; i--){ + network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]); + network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate/n, network[i].delta)); + } + } + } + + void GAN::updateGeneratorParameters(std::vector>> hiddenLayerUpdations, double learning_rate){ + LinAlg alg; + + if(!network.empty()){ + + for(int i = network.size()/2; i >= 0; i--){ + //std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl; + //std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl; + network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]); + network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate/n, network[i].delta)); + } + } + } + + std::tuple>>, std::vector> GAN::computeDiscriminatorGradients(std::vector y_hat, std::vector outputSet){ + class Cost cost; + Activation avn; + LinAlg alg; + Reg regularization; + + 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)); + + + if(!network.empty()){ + auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation]; + + network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1)); + std::vector> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta); + + cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + + //std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl; + //std::cout << "WEIGHTS SECOND:" << network[network.size() - 1].weights.size() << "x" << network[network.size() - 1].weights[0].size() << std::endl; + + for(int i = network.size() - 2; i > network.size()/2; i--){ + auto 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)); + std::vector> 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. + + } + } + return {cumulativeHiddenLayerWGrad, outputWGrad}; + } + + std::vector>> GAN::computeGeneratorGradients(std::vector y_hat, std::vector outputSet){ + class Cost cost; + Activation avn; + LinAlg alg; + Reg regularization; + + 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)); + if(!network.empty()){ + auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation]; + network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1)); + std::vector> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta); + cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + + for(int i = network.size() - 2; i >= 0; i--){ + auto 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)); + std::vector> 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. + } + } + return cumulativeHiddenLayerWGrad; + } + + void GAN::UI(int epoch, double cost_prev, std::vector y_hat, std::vector outputSet){ + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet)); + std::cout << "Layer " << network.size() + 1 << ": " << std::endl; + Utilities::UI(outputLayer->weights, outputLayer->bias); + if(!network.empty()){ + for(int i = network.size() - 1; i >= 0; i--){ + std::cout << "Layer " << i + 1 << ": " << std::endl; + Utilities::UI(network[i].weights, network[i].bias); + } + } + } +} \ No newline at end of file diff --git a/MLPP/GAN/GAN.hpp b/MLPP/GAN/GAN.hpp new file mode 100644 index 0000000..77f6a7c --- /dev/null +++ b/MLPP/GAN/GAN.hpp @@ -0,0 +1,57 @@ +// +// GAN.hpp +// +// Created by Marc Melikyan on 11/4/20. +// + +#ifndef GAN_hpp +#define GAN_hpp + +#include "HiddenLayer/HiddenLayer.hpp" +#include "OutputLayer/OutputLayer.hpp" + +#include +#include +#include + +namespace MLPP{ + +class GAN{ + public: + GAN(double k, std::vector> outputSet); + ~GAN(); + std::vector> generateExample(int n); + double modelTest(std::vector x); + void gradientDescent(double learning_rate, int max_epoch, bool UI = 1); + double score(); + void save(std::string fileName); + + void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5); + void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5); + + private: + std::vector> modelSetTestGenerator(std::vector> X); // Evaluator for the generator of the gan. + std::vector modelSetTestDiscriminator(std::vector> X); // Evaluator for the discriminator of the gan. + + double Cost(std::vector y_hat, std::vector y); + + void forwardPass(); + void updateDiscriminatorParameters(std::vector>> hiddenLayerUpdations, std::vector outputLayerUpdation, double learning_rate); + void updateGeneratorParameters(std::vector>> hiddenLayerUpdations, double learning_rate); + std::tuple>>, std::vector> computeDiscriminatorGradients(std::vector y_hat, std::vector outputSet); + std::vector>> computeGeneratorGradients(std::vector y_hat, std::vector outputSet); + + void UI(int epoch, double cost_prev, std::vector y_hat, std::vector outputSet); + + std::vector> outputSet; + std::vector y_hat; + + std::vector network; + OutputLayer *outputLayer; + + int n; + int k; + }; +} + +#endif /* GAN_hpp */ \ No newline at end of file diff --git a/MLPP/LinAlg/LinAlg.cpp b/MLPP/LinAlg/LinAlg.cpp index 11ab9cb..7ba008d 100644 --- a/MLPP/LinAlg/LinAlg.cpp +++ b/MLPP/LinAlg/LinAlg.cpp @@ -7,11 +7,28 @@ #include "LinAlg.hpp" #include "Stat/Stat.hpp" #include +#include #include #include namespace MLPP{ + std::vector> LinAlg::gaussianNoise(int n, int m){ + std::random_device rd; + std::default_random_engine generator(rd()); + + std::vector> A; + A.resize(n); + for(int i = 0; i < n; i++){ + A[i].resize(m); + for(int j = 0; j < m; j++){ + std::normal_distribution distribution(0, 1); // Standard normal distribution. Mean of 0, std of 1. + A[i][j] = distribution(generator); + } + } + return A; + } + std::vector> LinAlg::addition(std::vector> A, std::vector> B){ std::vector> C; C.resize(A.size()); diff --git a/MLPP/LinAlg/LinAlg.hpp b/MLPP/LinAlg/LinAlg.hpp index a81e7ce..35a85c5 100644 --- a/MLPP/LinAlg/LinAlg.hpp +++ b/MLPP/LinAlg/LinAlg.hpp @@ -16,6 +16,8 @@ namespace MLPP{ // MATRIX FUNCTIONS + std::vector> gaussianNoise(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/README.md b/README.md index b1cff6c..99001c9 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ Begin by downloading the header files for the ML++ library. You can do this by c ``` git clone https://github.com/novak-99/MLPP ``` -Next, execute the "./buildSO.sh" shell script: +Next, execute the "buildSO.sh" shell script: ``` sudo ./buildSO.sh ``` diff --git a/a.out b/a.out index baca3a5d0b4c1be51d37a313acb70a2728d5ccc4..3dee519a13e00a32ec332ec0078cd4c61afc6385 100755 GIT binary patch literal 2192472 zcmeEv33!}U)&B&D1`sD@t$-3)%Oa#oKZ;dIp(BG32uq->Erflu0?{G{Qqn1z4g>L{ z`D|Gv0zy;{1`9I+yo%`N< z?z!il?Vh{5_kHrizkmAqNTg!@NF=fz{wCmW?&l(rMC(_PibTGIzc~J8&1#r_Q0(~F zk%u!k{9i@*vFsD^%tZsUW*r@yd2|>=>u(XxjOcSEa`^`u<-b|8E@zN@jd>s;@dW(@O%!@SAwtUqKhv)`+_rVelb3C%jUwrBP{>; z9Q>~s_pDjX7yR(-3r?GL_628r4}it^zV&Oxx7_LwpNnw>@l)@N;F&S~=;^Z#Jp8bu zY|Sl%u1KAOw)$tEDIRAN)kuSH-}9xw=koY^9XjZ*6|v+{JktY)GHX_`T*LT!QJ>&L zR+xMa;`5rZiEuvyrFBL9#IyWYr$51dGH2GT^G=y{(Iw}f`n~gJ&AIqO+fRj+e=WYM zjcgvCgLF~MGeiA6?Uaj8@#`z!{O79gDt%Qpi_bxPKBT66e{|@TB*AL*EU$%7C6?4&7={%Usn(jE4AdHUCp zSA#$4V|1g)=kUk0@UrTZ^>#y=dhGt2gq{;m!+tJDf7vei!ZIPRVgXQ8__TnYrrmen 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