diff --git a/SCsub b/SCsub index 83201b1..7d7f19f 100644 --- a/SCsub +++ b/SCsub @@ -54,6 +54,7 @@ sources = [ "mlpp/output_layer/output_layer_old.cpp", "mlpp/multi_output_layer/multi_output_layer_old.cpp", "mlpp/hidden_layer/hidden_layer_old.cpp", + "mlpp/mlp/mlp_old.cpp", "test/mlpp_tests.cpp", ] diff --git a/mlpp/mlp/mlp.cpp b/mlpp/mlp/mlp.cpp index ab500ad..9cec667 100644 --- a/mlpp/mlp/mlp.cpp +++ b/mlpp/mlp/mlp.cpp @@ -503,275 +503,3 @@ void MLPPMLP::_bind_methods() { ClassDB::bind_method(D_METHOD("score"), &MLPPMLP::score); ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPMLP::save); } - -// ======= OLD ======= - -MLPPMLPOld::MLPPMLPOld(std::vector> p_inputSet, std::vector p_outputSet, int p_n_hidden, std::string p_reg, real_t p_lambda, real_t p_alpha) { - inputSet = p_inputSet; - outputSet = p_outputSet; - n_hidden = p_n_hidden; - n = p_inputSet.size(); - k = p_inputSet[0].size(); - reg = p_reg; - lambda = p_lambda; - alpha = p_alpha; - - MLPPActivation avn; - y_hat.resize(n); - - weights1 = MLPPUtilities::weightInitialization(k, n_hidden); - weights2 = MLPPUtilities::weightInitialization(n_hidden); - bias1 = MLPPUtilities::biasInitialization(n_hidden); - bias2 = MLPPUtilities::biasInitialization(); -} - -std::vector MLPPMLPOld::modelSetTest(std::vector> X) { - return Evaluate(X); -} - -real_t MLPPMLPOld::modelTest(std::vector x) { - return Evaluate(x); -} - -void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) { - MLPPActivation avn; - MLPPLinAlg alg; - MLPPReg regularization; - real_t cost_prev = 0; - int epoch = 1; - forwardPass(); - - while (true) { - cost_prev = Cost(y_hat, outputSet); - - // Calculating the errors - std::vector error = alg.subtraction(y_hat, outputSet); - - // Calculating the weight/bias gradients for layer 2 - - std::vector D2_1 = alg.mat_vec_mult(alg.transpose(a2), error); - - // weights and bias updation for layer 2 - weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1)); - weights2 = regularization.regWeights(weights2, lambda, alpha, reg); - - bias2 -= learning_rate * alg.sum_elements(error) / n; - - // Calculating the weight/bias for layer 1 - - std::vector> D1_1; - D1_1.resize(n); - - D1_1 = alg.outerProduct(error, weights2); - - std::vector> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); - - std::vector> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2); - - // weight an bias updation for layer 1 - weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / n, D1_3)); - weights1 = regularization.regWeights(weights1, lambda, alpha, reg); - - bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2)); - - forwardPass(); - - // UI PORTION - if (UI) { - MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet)); - std::cout << "Layer 1:" << std::endl; - MLPPUtilities::UI(weights1, bias1); - std::cout << "Layer 2:" << std::endl; - MLPPUtilities::UI(weights2, bias2); - } - epoch++; - - if (epoch > max_epoch) { - break; - } - } -} - -void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) { - MLPPActivation avn; - MLPPLinAlg alg; - MLPPReg regularization; - real_t cost_prev = 0; - int epoch = 1; - - while (true) { - std::random_device rd; - std::default_random_engine generator(rd()); - std::uniform_int_distribution distribution(0, int(n - 1)); - int outputIndex = distribution(generator); - - real_t y_hat = Evaluate(inputSet[outputIndex]); - auto propagate_result = propagate(inputSet[outputIndex]); - auto z2 = std::get<0>(propagate_result); - auto a2 = std::get<1>(propagate_result); - cost_prev = Cost({ y_hat }, { outputSet[outputIndex] }); - real_t error = y_hat - outputSet[outputIndex]; - - // Weight updation for layer 2 - std::vector D2_1 = alg.scalarMultiply(error, a2); - weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1)); - weights2 = regularization.regWeights(weights2, lambda, alpha, reg); - - // Bias updation for layer 2 - bias2 -= learning_rate * error; - - // Weight updation for layer 1 - std::vector D1_1 = alg.scalarMultiply(error, weights2); - std::vector D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); - std::vector> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2); - - weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3)); - weights1 = regularization.regWeights(weights1, lambda, alpha, reg); - // Bias updation for layer 1 - - bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2)); - - y_hat = Evaluate(inputSet[outputIndex]); - if (UI) { - MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] })); - std::cout << "Layer 1:" << std::endl; - MLPPUtilities::UI(weights1, bias1); - std::cout << "Layer 2:" << std::endl; - MLPPUtilities::UI(weights2, bias2); - } - epoch++; - - if (epoch > max_epoch) { - break; - } - } - forwardPass(); -} - -void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) { - MLPPActivation avn; - MLPPLinAlg alg; - MLPPReg regularization; - real_t cost_prev = 0; - int epoch = 1; - - // Creating the mini-batches - int n_mini_batch = n / mini_batch_size; - auto minibatches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch); - auto inputMiniBatches = std::get<0>(minibatches); - auto outputMiniBatches = std::get<1>(minibatches); - - while (true) { - for (int i = 0; i < n_mini_batch; i++) { - std::vector y_hat = Evaluate(inputMiniBatches[i]); - auto propagate_result = propagate(inputMiniBatches[i]); - auto z2 = std::get<0>(propagate_result); - auto a2 = std::get<1>(propagate_result); - - cost_prev = Cost(y_hat, outputMiniBatches[i]); - - // Calculating the errors - std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); - - // Calculating the weight/bias gradients for layer 2 - - std::vector D2_1 = alg.mat_vec_mult(alg.transpose(a2), error); - - // weights and bias updation for layser 2 - weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D2_1)); - weights2 = regularization.regWeights(weights2, lambda, alpha, reg); - - // Calculating the bias gradients for layer 2 - //real_t b_gradient = alg.sum_elements(error); - - // Bias Updation for layer 2 - bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); - - //Calculating the weight/bias for layer 1 - - std::vector> D1_1 = alg.outerProduct(error, weights2); - - std::vector> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); - - std::vector> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2); - - // weight an bias updation for layer 1 - weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_3)); - weights1 = regularization.regWeights(weights1, lambda, alpha, reg); - - bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_2)); - - y_hat = Evaluate(inputMiniBatches[i]); - - if (UI) { - MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); - std::cout << "Layer 1:" << std::endl; - MLPPUtilities::UI(weights1, bias1); - std::cout << "Layer 2:" << std::endl; - MLPPUtilities::UI(weights2, bias2); - } - } - epoch++; - if (epoch > max_epoch) { - break; - } - } - forwardPass(); -} - -real_t MLPPMLPOld::score() { - MLPPUtilities util; - return util.performance(y_hat, outputSet); -} - -void MLPPMLPOld::save(std::string fileName) { - MLPPUtilities util; - util.saveParameters(fileName, weights1, bias1, false, 1); - util.saveParameters(fileName, weights2, bias2, true, 2); -} - -real_t MLPPMLPOld::Cost(std::vector y_hat, std::vector y) { - MLPPReg regularization; - class MLPPCost cost; - return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg); -} - -std::vector MLPPMLPOld::Evaluate(std::vector> X) { - MLPPLinAlg alg; - MLPPActivation avn; - std::vector> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1); - std::vector> a2 = avn.sigmoid(z2); - return avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2))); -} - -std::tuple>, std::vector>> MLPPMLPOld::propagate(std::vector> X) { - MLPPLinAlg alg; - MLPPActivation avn; - std::vector> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1); - std::vector> a2 = avn.sigmoid(z2); - return { z2, a2 }; -} - -real_t MLPPMLPOld::Evaluate(std::vector x) { - MLPPLinAlg alg; - MLPPActivation avn; - std::vector z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1); - std::vector a2 = avn.sigmoid(z2); - return avn.sigmoid(alg.dot(weights2, a2) + bias2); -} - -std::tuple, std::vector> MLPPMLPOld::propagate(std::vector x) { - MLPPLinAlg alg; - MLPPActivation avn; - std::vector z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1); - std::vector a2 = avn.sigmoid(z2); - return { z2, a2 }; -} - -void MLPPMLPOld::forwardPass() { - MLPPLinAlg alg; - MLPPActivation avn; - z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1); - a2 = avn.sigmoid(z2); - y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2))); -} diff --git a/mlpp/mlp/mlp.h b/mlpp/mlp/mlp.h index 5b87798..a23ae9d 100644 --- a/mlpp/mlp/mlp.h +++ b/mlpp/mlp/mlp.h @@ -102,47 +102,4 @@ protected: int _initialized; }; -class MLPPMLPOld { -public: - MLPPMLPOld(std::vector> inputSet, std::vector outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); - std::vector modelSetTest(std::vector> X); - real_t modelTest(std::vector x); - void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); - void SGD(real_t learning_rate, int max_epoch, bool UI = false); - void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false); - real_t score(); - void save(std::string fileName); - -private: - real_t Cost(std::vector y_hat, std::vector y); - - std::vector Evaluate(std::vector> X); - std::tuple>, std::vector>> propagate(std::vector> X); - real_t Evaluate(std::vector x); - std::tuple, std::vector> propagate(std::vector x); - void forwardPass(); - - std::vector> inputSet; - std::vector outputSet; - std::vector y_hat; - - std::vector> weights1; - std::vector weights2; - - std::vector bias1; - real_t bias2; - - std::vector> z2; - std::vector> a2; - - int n; - int k; - int n_hidden; - - // Regularization Params - std::string reg; - real_t lambda; /* Regularization Parameter */ - real_t alpha; /* This is the controlling param for Elastic Net*/ -}; - #endif /* MLP_hpp */ diff --git a/mlpp/mlp/mlp_old.cpp b/mlpp/mlp/mlp_old.cpp new file mode 100644 index 0000000..325f0e5 --- /dev/null +++ b/mlpp/mlp/mlp_old.cpp @@ -0,0 +1,288 @@ +// +// MLP.cpp +// +// Created by Marc Melikyan on 11/4/20. +// + +#include "mlp_old.h" + +#include "core/log/logger.h" + +#include "../activation/activation.h" +#include "../cost/cost.h" +#include "../lin_alg/lin_alg.h" +#include "../regularization/reg.h" +#include "../utilities/utilities.h" + +#include +#include + +MLPPMLPOld::MLPPMLPOld(std::vector> p_inputSet, std::vector p_outputSet, int p_n_hidden, std::string p_reg, real_t p_lambda, real_t p_alpha) { + inputSet = p_inputSet; + outputSet = p_outputSet; + n_hidden = p_n_hidden; + n = p_inputSet.size(); + k = p_inputSet[0].size(); + reg = p_reg; + lambda = p_lambda; + alpha = p_alpha; + + MLPPActivation avn; + y_hat.resize(n); + + weights1 = MLPPUtilities::weightInitialization(k, n_hidden); + weights2 = MLPPUtilities::weightInitialization(n_hidden); + bias1 = MLPPUtilities::biasInitialization(n_hidden); + bias2 = MLPPUtilities::biasInitialization(); +} + +std::vector MLPPMLPOld::modelSetTest(std::vector> X) { + return Evaluate(X); +} + +real_t MLPPMLPOld::modelTest(std::vector x) { + return Evaluate(x); +} + +void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) { + MLPPActivation avn; + MLPPLinAlg alg; + MLPPReg regularization; + real_t cost_prev = 0; + int epoch = 1; + forwardPass(); + + while (true) { + cost_prev = Cost(y_hat, outputSet); + + // Calculating the errors + std::vector error = alg.subtraction(y_hat, outputSet); + + // Calculating the weight/bias gradients for layer 2 + + std::vector D2_1 = alg.mat_vec_mult(alg.transpose(a2), error); + + // weights and bias updation for layer 2 + weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1)); + weights2 = regularization.regWeights(weights2, lambda, alpha, reg); + + bias2 -= learning_rate * alg.sum_elements(error) / n; + + // Calculating the weight/bias for layer 1 + + std::vector> D1_1; + D1_1.resize(n); + + D1_1 = alg.outerProduct(error, weights2); + + std::vector> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); + + std::vector> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2); + + // weight an bias updation for layer 1 + weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / n, D1_3)); + weights1 = regularization.regWeights(weights1, lambda, alpha, reg); + + bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2)); + + forwardPass(); + + // UI PORTION + if (UI) { + MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet)); + std::cout << "Layer 1:" << std::endl; + MLPPUtilities::UI(weights1, bias1); + std::cout << "Layer 2:" << std::endl; + MLPPUtilities::UI(weights2, bias2); + } + epoch++; + + if (epoch > max_epoch) { + break; + } + } +} + +void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) { + MLPPActivation avn; + MLPPLinAlg alg; + MLPPReg regularization; + real_t cost_prev = 0; + int epoch = 1; + + while (true) { + std::random_device rd; + std::default_random_engine generator(rd()); + std::uniform_int_distribution distribution(0, int(n - 1)); + int outputIndex = distribution(generator); + + real_t y_hat = Evaluate(inputSet[outputIndex]); + auto propagate_result = propagate(inputSet[outputIndex]); + auto z2 = std::get<0>(propagate_result); + auto a2 = std::get<1>(propagate_result); + cost_prev = Cost({ y_hat }, { outputSet[outputIndex] }); + real_t error = y_hat - outputSet[outputIndex]; + + // Weight updation for layer 2 + std::vector D2_1 = alg.scalarMultiply(error, a2); + weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1)); + weights2 = regularization.regWeights(weights2, lambda, alpha, reg); + + // Bias updation for layer 2 + bias2 -= learning_rate * error; + + // Weight updation for layer 1 + std::vector D1_1 = alg.scalarMultiply(error, weights2); + std::vector D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); + std::vector> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2); + + weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3)); + weights1 = regularization.regWeights(weights1, lambda, alpha, reg); + // Bias updation for layer 1 + + bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2)); + + y_hat = Evaluate(inputSet[outputIndex]); + if (UI) { + MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] })); + std::cout << "Layer 1:" << std::endl; + MLPPUtilities::UI(weights1, bias1); + std::cout << "Layer 2:" << std::endl; + MLPPUtilities::UI(weights2, bias2); + } + epoch++; + + if (epoch > max_epoch) { + break; + } + } + forwardPass(); +} + +void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) { + MLPPActivation avn; + MLPPLinAlg alg; + MLPPReg regularization; + real_t cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n / mini_batch_size; + auto minibatches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + auto inputMiniBatches = std::get<0>(minibatches); + auto outputMiniBatches = std::get<1>(minibatches); + + while (true) { + for (int i = 0; i < n_mini_batch; i++) { + std::vector y_hat = Evaluate(inputMiniBatches[i]); + auto propagate_result = propagate(inputMiniBatches[i]); + auto z2 = std::get<0>(propagate_result); + auto a2 = std::get<1>(propagate_result); + + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + // Calculating the errors + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight/bias gradients for layer 2 + + std::vector D2_1 = alg.mat_vec_mult(alg.transpose(a2), error); + + // weights and bias updation for layser 2 + weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D2_1)); + weights2 = regularization.regWeights(weights2, lambda, alpha, reg); + + // Calculating the bias gradients for layer 2 + //real_t b_gradient = alg.sum_elements(error); + + // Bias Updation for layer 2 + bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); + + //Calculating the weight/bias for layer 1 + + std::vector> D1_1 = alg.outerProduct(error, weights2); + + std::vector> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true)); + + std::vector> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2); + + // weight an bias updation for layer 1 + weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_3)); + weights1 = regularization.regWeights(weights1, lambda, alpha, reg); + + bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_2)); + + y_hat = Evaluate(inputMiniBatches[i]); + + if (UI) { + MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + std::cout << "Layer 1:" << std::endl; + MLPPUtilities::UI(weights1, bias1); + std::cout << "Layer 2:" << std::endl; + MLPPUtilities::UI(weights2, bias2); + } + } + epoch++; + if (epoch > max_epoch) { + break; + } + } + forwardPass(); +} + +real_t MLPPMLPOld::score() { + MLPPUtilities util; + return util.performance(y_hat, outputSet); +} + +void MLPPMLPOld::save(std::string fileName) { + MLPPUtilities util; + util.saveParameters(fileName, weights1, bias1, false, 1); + util.saveParameters(fileName, weights2, bias2, true, 2); +} + +real_t MLPPMLPOld::Cost(std::vector y_hat, std::vector y) { + MLPPReg regularization; + class MLPPCost cost; + return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg); +} + +std::vector MLPPMLPOld::Evaluate(std::vector> X) { + MLPPLinAlg alg; + MLPPActivation avn; + std::vector> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1); + std::vector> a2 = avn.sigmoid(z2); + return avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2))); +} + +std::tuple>, std::vector>> MLPPMLPOld::propagate(std::vector> X) { + MLPPLinAlg alg; + MLPPActivation avn; + std::vector> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1); + std::vector> a2 = avn.sigmoid(z2); + return { z2, a2 }; +} + +real_t MLPPMLPOld::Evaluate(std::vector x) { + MLPPLinAlg alg; + MLPPActivation avn; + std::vector z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1); + std::vector a2 = avn.sigmoid(z2); + return avn.sigmoid(alg.dot(weights2, a2) + bias2); +} + +std::tuple, std::vector> MLPPMLPOld::propagate(std::vector x) { + MLPPLinAlg alg; + MLPPActivation avn; + std::vector z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1); + std::vector a2 = avn.sigmoid(z2); + return { z2, a2 }; +} + +void MLPPMLPOld::forwardPass() { + MLPPLinAlg alg; + MLPPActivation avn; + z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1); + a2 = avn.sigmoid(z2); + y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2))); +} diff --git a/mlpp/mlp/mlp_old.h b/mlpp/mlp/mlp_old.h new file mode 100644 index 0000000..93290ad --- /dev/null +++ b/mlpp/mlp/mlp_old.h @@ -0,0 +1,70 @@ + +#ifndef MLPP_MLP_OLD_H +#define MLPP_MLP_OLD_H + +// +// MLP.hpp +// +// Created by Marc Melikyan on 11/4/20. +// + +#include "core/containers/vector.h" +#include "core/math/math_defs.h" +#include "core/string/ustring.h" +#include "core/variant/variant.h" + +#include "core/object/reference.h" + +#include "../regularization/reg.h" + +#include "../lin_alg/mlpp_matrix.h" +#include "../lin_alg/mlpp_vector.h" + +#include +#include +#include + +class MLPPMLPOld { +public: + MLPPMLPOld(std::vector> inputSet, std::vector outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); + std::vector modelSetTest(std::vector> X); + real_t modelTest(std::vector x); + void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); + void SGD(real_t learning_rate, int max_epoch, bool UI = false); + void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false); + real_t score(); + void save(std::string fileName); + +private: + real_t Cost(std::vector y_hat, std::vector y); + + std::vector Evaluate(std::vector> X); + std::tuple>, std::vector>> propagate(std::vector> X); + real_t Evaluate(std::vector x); + std::tuple, std::vector> propagate(std::vector x); + void forwardPass(); + + std::vector> inputSet; + std::vector outputSet; + std::vector y_hat; + + std::vector> weights1; + std::vector weights2; + + std::vector bias1; + real_t bias2; + + std::vector> z2; + std::vector> a2; + + int n; + int k; + int n_hidden; + + // Regularization Params + std::string reg; + real_t lambda; /* Regularization Parameter */ + real_t alpha; /* This is the controlling param for Elastic Net*/ +}; + +#endif /* MLP_hpp */ diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp index 2245013..998e63f 100644 --- a/test/mlpp_tests.cpp +++ b/test/mlpp_tests.cpp @@ -47,6 +47,7 @@ #include "../mlpp/uni_lin_reg/uni_lin_reg.h" #include "../mlpp/wgan/wgan.h" +#include "../mlpp/mlp/mlp_old.h" #include "../mlpp/wgan/wgan_old.h" Vector dstd_vec_to_vec(const std::vector &in) {