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https://github.com/Relintai/pmlpp.git
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Added MLPPAutoEncoderOld.
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@ -61,6 +61,7 @@ sources = [
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"mlpp/probit_reg/probit_reg_old.cpp",
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"mlpp/svc/svc_old.cpp",
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"mlpp/softmax_reg/softmax_reg_old.cpp",
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"mlpp/auto_encoder/auto_encoder_old.cpp",
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"test/mlpp_tests.cpp",
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]
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265
mlpp/auto_encoder/auto_encoder_old.cpp
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265
mlpp/auto_encoder/auto_encoder_old.cpp
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//
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// AutoEncoder.cpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "auto_encoder_old.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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std::vector<std::vector<real_t>> MLPPAutoEncoderOld::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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std::vector<real_t> MLPPAutoEncoderOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPAutoEncoderOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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while (true) {
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cost_prev = Cost(y_hat, inputSet);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputSet);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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// weights and bias updation for layer 2
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1));
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// Calculating the bias gradients for layer 2
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bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2);
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// weight an bias updation for layer 1
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / n, D1_3));
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
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forwardPass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputSet));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPAutoEncoderOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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while (true) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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int outputIndex = distribution(generator);
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std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
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auto prop_res = propagate(inputSet[outputIndex]);
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auto z2 = std::get<0>(prop_res);
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auto a2 = std::get<1>(prop_res);
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cost_prev = Cost({ y_hat }, { inputSet[outputIndex] });
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std::vector<real_t> error = alg.subtraction(y_hat, inputSet[outputIndex]);
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// Weight updation for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
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// Bias updation for layer 2
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bias2 = alg.subtraction(bias2, alg.scalarMultiply(learning_rate, error));
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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.mat_vec_mult(weights2, error);
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std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
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// Bias updation for layer 1
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bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2));
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y_hat = Evaluate(inputSet[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { inputSet[outputIndex] }));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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void MLPPAutoEncoderOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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std::vector<std::vector<std::vector<real_t>>> inputMiniBatches = MLPPUtilities::createMiniBatches(inputSet, n_mini_batch);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
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auto prop_res = propagate(inputMiniBatches[i]);
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auto z2 = std::get<0>(prop_res);
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auto a2 = std::get<1>(prop_res);
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cost_prev = Cost(y_hat, inputMiniBatches[i]);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputMiniBatches[i]);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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// weights and bias updation for layer 2
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D2_1));
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// Bias Updation for layer 2
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bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
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// weight an bias updation for layer 1
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_3));
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_2));
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y_hat = Evaluate(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputMiniBatches[i]));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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real_t MLPPAutoEncoderOld::score() {
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MLPPUtilities util;
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return util.performance(y_hat, inputSet);
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}
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void MLPPAutoEncoderOld::save(std::string fileName) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights1, bias1, 0, 1);
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util.saveParameters(fileName, weights2, bias2, 1, 2);
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}
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MLPPAutoEncoderOld::MLPPAutoEncoderOld(std::vector<std::vector<real_t>> pinputSet, int pn_hidden) {
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inputSet = pinputSet;
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n_hidden = pn_hidden;
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n = inputSet.size();
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k = inputSet[0].size();
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MLPPActivation avn;
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y_hat.resize(inputSet.size());
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weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
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weights2 = MLPPUtilities::weightInitialization(n_hidden, k);
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bias1 = MLPPUtilities::biasInitialization(n_hidden);
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bias2 = MLPPUtilities::biasInitialization(k);
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}
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real_t MLPPAutoEncoderOld::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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class MLPPCost cost;
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return cost.MSE(y_hat, inputSet);
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}
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std::vector<std::vector<real_t>> MLPPAutoEncoderOld::Evaluate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
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}
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPAutoEncoderOld::propagate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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std::vector<real_t> MLPPAutoEncoderOld::Evaluate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return alg.addition(alg.mat_vec_mult(alg.transpose(weights2), a2), bias2);
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}
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPAutoEncoderOld::propagate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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void MLPPAutoEncoderOld::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
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a2 = avn.sigmoid(z2);
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y_hat = alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
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}
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58
mlpp/auto_encoder/auto_encoder_old.h
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58
mlpp/auto_encoder/auto_encoder_old.h
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#ifndef MLPP_AUTO_ENCODER_OLD_H
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#define MLPP_AUTO_ENCODER_OLD_H
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//
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// AutoEncoder.hpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/math/math_defs.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class MLPPAutoEncoderOld {
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public:
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std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
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std::vector<real_t> modelTest(std::vector<real_t> x);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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real_t score();
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void save(std::string fileName);
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MLPPAutoEncoderOld(std::vector<std::vector<real_t>> inputSet, int n_hidden);
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private:
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real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<std::vector<real_t>> Evaluate(std::vector<std::vector<real_t>> X);
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> Evaluate(std::vector<real_t> x);
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std::tuple<std::vector<real_t>, std::vector<real_t>> propagate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<std::vector<real_t>> y_hat;
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std::vector<std::vector<real_t>> weights1;
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std::vector<std::vector<real_t>> weights2;
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std::vector<real_t> bias1;
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std::vector<real_t> bias2;
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std::vector<std::vector<real_t>> z2;
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std::vector<std::vector<real_t>> a2;
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int n;
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int k;
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int n_hidden;
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};
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#endif /* AutoEncoder_hpp */
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@ -47,6 +47,7 @@
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#include "../mlpp/uni_lin_reg/uni_lin_reg.h"
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#include "../mlpp/wgan/wgan.h"
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#include "../mlpp/auto_encoder/auto_encoder_old.h"
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#include "../mlpp/mlp/mlp_old.h"
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#include "../mlpp/outlier_finder/outlier_finder_old.h"
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#include "../mlpp/pca/pca_old.h"
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@ -495,12 +496,13 @@ void MLPPTests::test_soft_max_network(bool ui) {
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void MLPPTests::test_autoencoder(bool ui) {
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MLPPLinAlg alg;
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// AUTOENCODER
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std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, { 3, 5, 9, 12, 15, 18, 21, 24, 27, 30 } };
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MLPPAutoEncoder model(alg.transpose(inputSet), 5);
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model.SGD(0.001, 300000, ui);
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alg.printMatrix(model.modelSetTest(alg.transpose(inputSet)));
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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// AUTOENCODER
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MLPPAutoEncoderOld model_old(alg.transpose(inputSet), 5);
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model_old.SGD(0.001, 300000, ui);
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alg.printMatrix(model_old.modelSetTest(alg.transpose(inputSet)));
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std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl;
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}
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void MLPPTests::test_dynamically_sized_ann(bool ui) {
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MLPPLinAlg alg;
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