From 2c83feb410c60e4440c1756bed1ccc566d10e5d7 Mon Sep 17 00:00:00 2001 From: novak_99 Date: Tue, 18 Jan 2022 22:26:57 -0800 Subject: [PATCH] Added new optimizers for Neural Nets. --- MLPP/ANN/ANN.cpp | 358 +++++++++++++++++++++++++++++++++++++++-- MLPP/ANN/ANN.hpp | 6 + MLPP/LinAlg/LinAlg.cpp | 25 +++ MLPP/LinAlg/LinAlg.hpp | 6 + MLPP/LinReg/LinReg.cpp | 321 ------------------------------------ MLPP/LinReg/LinReg.hpp | 9 +- README.md | 22 ++- a.out | Bin 2086160 -> 2086880 bytes main.cpp | 8 +- 9 files changed, 408 insertions(+), 347 deletions(-) diff --git a/MLPP/ANN/ANN.cpp b/MLPP/ANN/ANN.cpp index 753b3d9..19eaf6d 100644 --- a/MLPP/ANN/ANN.cpp +++ b/MLPP/ANN/ANN.cpp @@ -114,7 +114,158 @@ namespace MLPP { forwardPass(); } - void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + void ANN::Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool NAG, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> v_hidden; + + std::vector v_output; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + + if(!network.empty() && v_hidden.empty()){ // Initing our tensor + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); + } + + if(v_output.empty()){ + v_output.resize(outputWGrad.size()); + } + + if(NAG){ // "Aposterori" calculation + updateParameters(v_hidden, v_output, 0); // DON'T update bias. + } + + v_hidden = alg.addition(alg.scalarMultiply(gamma, v_hidden), alg.scalarMultiply(learning_rate/n, cumulativeHiddenLayerWGrad)); + + v_output = alg.addition(alg.scalarMultiply(gamma, v_output), alg.scalarMultiply(learning_rate/n, outputWGrad)); + + updateParameters(v_hidden, v_output, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void ANN::Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> v_hidden; + + std::vector v_output; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + + if(!network.empty() && v_hidden.empty()){ // Initing our tensor + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); + } + + if(v_output.empty()){ + v_output.resize(outputWGrad.size()); + } + + v_hidden = alg.addition(v_hidden, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)); + + v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2)); + + std::vector>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden)))); + std::vector outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output)))); + + updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void ANN::Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> v_hidden; + + std::vector v_output; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + + if(!network.empty() && v_hidden.empty()){ // Initing our tensor + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); + } + + if(v_output.empty()){ + v_output.resize(outputWGrad.size()); + } + + v_hidden = alg.addition(alg.scalarMultiply(1 - b1, v_hidden), alg.scalarMultiply(b1, alg.exponentiate(cumulativeHiddenLayerWGrad, 2))); + + v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2)); + + std::vector>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden)))); + std::vector outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output)))); + + updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + +void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ class Cost cost; LinAlg alg; @@ -139,18 +290,9 @@ namespace MLPP { cost_prev = Cost(y_hat, outputMiniBatches[i]); auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); - if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor - m_hidden.resize(cumulativeHiddenLayerWGrad.size()); - v_hidden.resize(cumulativeHiddenLayerWGrad.size()); - for(int i = 0; i < cumulativeHiddenLayerWGrad.size(); i++){ - m_hidden[i].resize(cumulativeHiddenLayerWGrad[i].size()); - v_hidden[i].resize(cumulativeHiddenLayerWGrad[i].size()); - for(int j = 0; j < cumulativeHiddenLayerWGrad[i].size(); j++){ - m_hidden[i][j].resize(cumulativeHiddenLayerWGrad[i][j].size()); - v_hidden[i][j].resize(cumulativeHiddenLayerWGrad[i][j].size()); - } - } + m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad); + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); } if(m_output.empty() && v_output.empty()){ @@ -185,6 +327,198 @@ namespace MLPP { forwardPass(); } + void ANN::Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> m_hidden; + std::vector>> u_hidden; + + std::vector m_output; + std::vector u_output; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + if(!network.empty() && m_hidden.empty() && u_hidden.empty()){ // Initing our tensor + m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad); + u_hidden = alg.resize(u_hidden, cumulativeHiddenLayerWGrad); + } + + if(m_output.empty() && u_output.empty()){ + m_output.resize(outputWGrad.size()); + u_output.resize(outputWGrad.size()); + } + + m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad)); + u_hidden = alg.max(alg.scalarMultiply(b2, u_hidden), alg.abs(cumulativeHiddenLayerWGrad)); + + m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad)); + u_output = alg.max(alg.scalarMultiply(b2, u_output), alg.abs(outputWGrad)); + + std::vector>> m_hidden_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m_hidden); + + std::vector m_output_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m_output); + + std::vector>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, u_hidden))); + std::vector outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, u_output))); + + + updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void ANN::Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> m_hidden; + std::vector>> v_hidden; + std::vector>> m_hidden_final; + + std::vector m_output; + std::vector v_output; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor + m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad); + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); + } + + if(m_output.empty() && v_output.empty()){ + m_output.resize(outputWGrad.size()); + v_output.resize(outputWGrad.size()); + } + + m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad)); + v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2))); + + + m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad)); + v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2))); + + std::vector>> m_hidden_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m_hidden); + std::vector>> v_hidden_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v_hidden); + std::vector>> m_hidden_final = alg.addition(alg.scalarMultiply(b1, m_hidden_hat), alg.scalarMultiply((1 - b1)/(1 - pow(b1, epoch)), cumulativeHiddenLayerWGrad)); + + std::vector m_output_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m_output); + std::vector v_output_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v_output); + std::vector m_output_final = alg.addition(alg.scalarMultiply(b1, m_output_hat), alg.scalarMultiply((1 - b1)/(1 - pow(b1, epoch)), outputWGrad)); + + std::vector>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden_final, alg.scalarAdd(e, alg.sqrt(v_hidden_hat)))); + std::vector outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output_final, alg.scalarAdd(e, alg.sqrt(v_output_hat)))); + + + updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void ANN::AMSGrad(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + class Cost cost; + LinAlg alg; + + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + // always evaluate the result + // always do forward pass only ONCE at end. + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector>> m_hidden; + std::vector>> v_hidden; + + std::vector>> v_hidden_hat; + + std::vector m_output; + std::vector v_output; + + std::vector v_output_hat; + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = modelSetTest(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]); + if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor + m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad); + v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad); + v_hidden_hat = alg.resize(v_hidden_hat, cumulativeHiddenLayerWGrad); + } + + if(m_output.empty() && v_output.empty()){ + m_output.resize(outputWGrad.size()); + v_output.resize(outputWGrad.size()); + v_output_hat.resize(outputWGrad.size()); + } + + m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad)); + v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2))); + + m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad)); + v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2))); + + v_hidden_hat = alg.max(v_hidden_hat, v_hidden); + + v_output_hat = alg.max(v_output_hat, v_output); + + std::vector>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden, alg.scalarAdd(e, alg.sqrt(v_hidden_hat)))); + std::vector outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output, alg.scalarAdd(e, alg.sqrt(v_output_hat)))); + + + updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too. + y_hat = modelSetTest(inputMiniBatches[i]); + + if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + double ANN::score(){ Utilities util; forwardPass(); diff --git a/MLPP/ANN/ANN.hpp b/MLPP/ANN/ANN.hpp index 64b1e75..db21cd8 100644 --- a/MLPP/ANN/ANN.hpp +++ b/MLPP/ANN/ANN.hpp @@ -24,7 +24,13 @@ class ANN{ double modelTest(std::vector x); void gradientDescent(double learning_rate, int max_epoch, bool UI = 1); void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1); + void Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool NAG, bool UI = 1); + void Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI = 1); + void Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI = 1); void Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void AMSGrad(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); double score(); void save(std::string fileName); diff --git a/MLPP/LinAlg/LinAlg.cpp b/MLPP/LinAlg/LinAlg.cpp index 1fec7ac..ada8411 100644 --- a/MLPP/LinAlg/LinAlg.cpp +++ b/MLPP/LinAlg/LinAlg.cpp @@ -1130,4 +1130,29 @@ namespace MLPP{ } return A; } + + std::vector>> LinAlg::resize(std::vector>> A, std::vector>> B){ + A.resize(B.size()); + for(int i = 0; i < B.size(); i++){ + A[i].resize(B[i].size()); + for(int j = 0; j < B[i].size(); j++){ + A[i][j].resize(B[i][j].size()); + } + } + return A; + } + + std::vector>> LinAlg::max(std::vector>> A, std::vector>> B){ + for(int i = 0; i < A.size(); i++){ + A[i] = max(A[i], B[i]); + } + return A; + } + + std::vector>> LinAlg::abs(std::vector>> A){ + for(int i = 0; i < A.size(); i++){ + A[i] = abs(A[i]); + } + return A; + } } \ No newline at end of file diff --git a/MLPP/LinAlg/LinAlg.hpp b/MLPP/LinAlg/LinAlg.hpp index 5f9402a..a81e7ce 100644 --- a/MLPP/LinAlg/LinAlg.hpp +++ b/MLPP/LinAlg/LinAlg.hpp @@ -210,6 +210,12 @@ namespace MLPP{ std::vector>> resize(std::vector>> A, std::vector>> B); + std::vector>> hadamard_product(std::vector>> A, std::vector>> B); + + std::vector>> max(std::vector>> A, std::vector>> B); + + std::vector>> abs(std::vector>> A); + private: }; diff --git a/MLPP/LinReg/LinReg.cpp b/MLPP/LinReg/LinReg.cpp index 272410b..ca80066 100644 --- a/MLPP/LinReg/LinReg.cpp +++ b/MLPP/LinReg/LinReg.cpp @@ -166,327 +166,6 @@ namespace MLPP{ forwardPass(); } - void LinReg::Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Momentum. - std::vector v = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad)); - - weights = alg.subtraction(weights, v); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::NAG(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Momentum. - std::vector v = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - weights = alg.subtraction(weights, alg.scalarMultiply(gamma, v)); // "Aposterori" calculation - - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad)); - - weights = alg.subtraction(weights, v); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Adagrad. - std::vector v = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - v = alg.hadamard_product(weight_grad, weight_grad); - - weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v))))); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI){ - // Adagrad upgrade. Momentum is applied. - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Adagrad. - std::vector v = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - v = alg.addition(alg.scalarMultiply(b1, v), alg.scalarMultiply(1 - b1, alg.hadamard_product(weight_grad, weight_grad))); - - weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v))))); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Adam. - std::vector m = alg.zerovec(weights.size()); - - std::vector v = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); - v = alg.addition(alg.scalarMultiply(b2, v), alg.scalarMultiply(1 - b2, alg.exponentiate(weight_grad, 2))); - - std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); - std::vector v_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v); - - weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, alg.scalarAdd(e, alg.sqrt(v_hat))))); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - std::vector m = alg.zerovec(weights.size()); - - std::vector u = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); - u = alg.max(alg.scalarMultiply(b2, u), alg.abs(weight_grad)); - - std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); - - weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, u))); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - - void LinReg::Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ - LinAlg alg; - Reg regularization; - double cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n/mini_batch_size; - auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - - // Initializing necessary components for Adam. - std::vector m = alg.zerovec(weights.size()); - std::vector v = alg.zerovec(weights.size()); - std::vector m_final = alg.zerovec(weights.size()); - while(true){ - for(int i = 0; i < n_mini_batch; i++){ - std::vector y_hat = Evaluate(inputMiniBatches[i]); - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight gradients - std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); - std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); - std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final - - m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); - v = alg.addition(alg.scalarMultiply(b2, v), alg.scalarMultiply(1 - b2, alg.exponentiate(weight_grad, 2))); - m_final = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply((1 - b1)/(1 - pow(b1, epoch)), weight_grad)); - - std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); - std::vector v_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v); - - weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_final, alg.scalarAdd(e, alg.sqrt(v_hat))))); - - // Calculating the bias gradients - bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal - y_hat = Evaluate(inputMiniBatches[i]); - - if(UI) { - Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - Utilities::UI(weights, bias); - } - } - epoch++; - if(epoch > max_epoch) { break; } - } - forwardPass(); - } - void LinReg::normalEquation(){ LinAlg alg; Stat stat; diff --git a/MLPP/LinReg/LinReg.hpp b/MLPP/LinReg/LinReg.hpp index dbf484a..56b5ef8 100644 --- a/MLPP/LinReg/LinReg.hpp +++ b/MLPP/LinReg/LinReg.hpp @@ -20,14 +20,7 @@ namespace MLPP{ void NewtonRaphson(double learning_rate, int max_epoch, bool UI); void gradientDescent(double learning_rate, int max_epoch, bool UI = 1); void SGD(double learning_rate, int max_epoch, bool UI = 1); - // void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1); - // void Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI = 1); - // void NAG(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI = 1); - // void Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI = 1); - // void Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI = 1); - // void Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); - // void Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); - // void Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1); void normalEquation(); double score(); void save(std::string fileName); diff --git a/README.md b/README.md index d4677d5..ddfc545 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,23 @@ The result will be the model's predictions for the entire dataset. - Arcsch - Arsech - Arcoth - 2. Possible Loss Functions + 2. Possible Optimization Algorithms + - Batch Gradient Descent + - Mini-Batch Gradient Descent + - Stochastic Gradient Descent + - Gradient Descent with Momentum + - Nesterov Accelerated Gradient + - Adagrad Optimizer + - Adadelta Optimizer + - Adam Optimizer + - Adamax Optimizer + - Nadam Optimizer + - AMSGrad Optimizer + - 2nd Order Newton-Raphson Optimizer* + - Normal Equation* + + * Only available for linear regression + 3. Possible Loss Functions - MSE - RMSE - MAE @@ -99,11 +115,11 @@ The result will be the model's predictions for the entire dataset. - Log Loss - Cross Entropy - Hinge Loss - 3. Possible Regularization Methods + 4. Possible Regularization Methods - Lasso - Ridge - ElasticNet - 4. Possible Weight Initialization Methods + 5. 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zRp&9F=MSI_Y2mRKZ)M1lB;Mj^{y~gKL=`476*FIt$uXthmoZf=98;^b{tlWOK;kz~ zAU-Vx6OT+L$9?f>R4?Mw?Wt6FOgF_P-4~IbLYw#MrrwVJ_q!Ye?zhn=d(haq)mU}U zh=shnTJJ?|#3Wy=)*EP16^>5)8MAal46f;vevO8o#L=x&SPWZE!|}#Gu^8=CxC`dB zHFz0;DQK01KGjy~J*jVvo=e+nkSAAEa9u3=a)Gak+-Ga_BKl>mUQF=R#RhYnf$bwP_ivv;Tu)36jS!Q!?F5d-)SjFY7Scv{E7xn5^{|}P~K9K+b diff --git a/main.cpp b/main.cpp index 8e63a10..f345e77 100644 --- a/main.cpp +++ b/main.cpp @@ -364,10 +364,12 @@ int main() { std::vector> inputSet = {{0,0,1,1}, {0,1,0,1}}; std::vector outputSet = {0,1,1,0}; ANN ann(alg.transpose(inputSet), outputSet); - //ann.addLayer(10, "RELU", "Default", "Ridge", 0.0001); - ann.addLayer(10, "RELU", "Default", "XavierNormal"); + //ann.addLayer(10, "RELU"); + ann.addLayer(10, "Sigmoid"); ann.addOutputLayer("Sigmoid", "LogLoss"); - ann.Adam(0.1, 800, 2, 0.9, 0.999, 1e-8, 1); + //ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1); + //ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1); + ann.Momentum(0.1, 8000, 2, 0.9, true, 1); //ann.MBGD(0.1, 1000, 2, 1); alg.printVector(ann.modelSetTest(alg.transpose(inputSet))); std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;