mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-31 16:17:10 +01:00
Added MLPPAutoEncoderOld.
This commit is contained in:
parent
3a56ed59e3
commit
e6e50025ad
1
SCsub
1
SCsub
@ -61,6 +61,7 @@ sources = [
|
||||
"mlpp/probit_reg/probit_reg_old.cpp",
|
||||
"mlpp/svc/svc_old.cpp",
|
||||
"mlpp/softmax_reg/softmax_reg_old.cpp",
|
||||
"mlpp/auto_encoder/auto_encoder_old.cpp",
|
||||
|
||||
"test/mlpp_tests.cpp",
|
||||
]
|
||||
|
||||
265
mlpp/auto_encoder/auto_encoder_old.cpp
Normal file
265
mlpp/auto_encoder/auto_encoder_old.cpp
Normal file
@ -0,0 +1,265 @@
|
||||
//
|
||||
// AutoEncoder.cpp
|
||||
//
|
||||
// Created by Marc Melikyan on 11/4/20.
|
||||
//
|
||||
|
||||
#include "auto_encoder_old.h"
|
||||
|
||||
#include "../activation/activation.h"
|
||||
#include "../cost/cost.h"
|
||||
#include "../lin_alg/lin_alg.h"
|
||||
#include "../utilities/utilities.h"
|
||||
|
||||
#include <iostream>
|
||||
#include <random>
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPAutoEncoderOld::modelSetTest(std::vector<std::vector<real_t>> X) {
|
||||
return Evaluate(X);
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPAutoEncoderOld::modelTest(std::vector<real_t> x) {
|
||||
return Evaluate(x);
|
||||
}
|
||||
|
||||
void MLPPAutoEncoderOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
forwardPass();
|
||||
|
||||
while (true) {
|
||||
cost_prev = Cost(y_hat, inputSet);
|
||||
|
||||
// Calculating the errors
|
||||
std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputSet);
|
||||
|
||||
// Calculating the weight/bias gradients for layer 2
|
||||
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
|
||||
|
||||
// weights and bias updation for layer 2
|
||||
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1));
|
||||
|
||||
// Calculating the bias gradients for layer 2
|
||||
bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
|
||||
|
||||
//Calculating the weight/bias for layer 1
|
||||
|
||||
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
|
||||
|
||||
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
|
||||
|
||||
std::vector<std::vector<real_t>> 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));
|
||||
|
||||
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
|
||||
|
||||
forwardPass();
|
||||
|
||||
// UI PORTION
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputSet));
|
||||
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 MLPPAutoEncoderOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
while (true) {
|
||||
std::random_device rd;
|
||||
std::default_random_engine generator(rd());
|
||||
std::uniform_int_distribution<int> distribution(0, int(n - 1));
|
||||
int outputIndex = distribution(generator);
|
||||
|
||||
std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
|
||||
auto prop_res = propagate(inputSet[outputIndex]);
|
||||
auto z2 = std::get<0>(prop_res);
|
||||
auto a2 = std::get<1>(prop_res);
|
||||
|
||||
cost_prev = Cost({ y_hat }, { inputSet[outputIndex] });
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, inputSet[outputIndex]);
|
||||
|
||||
// Weight updation for layer 2
|
||||
std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
|
||||
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
|
||||
|
||||
// Bias updation for layer 2
|
||||
bias2 = alg.subtraction(bias2, alg.scalarMultiply(learning_rate, error));
|
||||
|
||||
// Weight updation for layer 1
|
||||
std::vector<real_t> D1_1 = alg.mat_vec_mult(weights2, error);
|
||||
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
|
||||
std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
|
||||
|
||||
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
|
||||
// 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 }, { inputSet[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 MLPPAutoEncoderOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
std::vector<std::vector<std::vector<real_t>>> inputMiniBatches = MLPPUtilities::createMiniBatches(inputSet, n_mini_batch);
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
|
||||
auto prop_res = propagate(inputMiniBatches[i]);
|
||||
auto z2 = std::get<0>(prop_res);
|
||||
auto a2 = std::get<1>(prop_res);
|
||||
|
||||
cost_prev = Cost(y_hat, inputMiniBatches[i]);
|
||||
|
||||
// Calculating the errors
|
||||
std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight/bias gradients for layer 2
|
||||
|
||||
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
|
||||
|
||||
// weights and bias updation for layer 2
|
||||
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D2_1));
|
||||
|
||||
// Bias Updation for layer 2
|
||||
bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
|
||||
|
||||
//Calculating the weight/bias for layer 1
|
||||
|
||||
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
|
||||
|
||||
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
|
||||
|
||||
std::vector<std::vector<real_t>> 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 / inputMiniBatches[i].size(), D1_3));
|
||||
|
||||
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_2));
|
||||
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputMiniBatches[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 MLPPAutoEncoderOld::score() {
|
||||
MLPPUtilities util;
|
||||
return util.performance(y_hat, inputSet);
|
||||
}
|
||||
|
||||
void MLPPAutoEncoderOld::save(std::string fileName) {
|
||||
MLPPUtilities util;
|
||||
util.saveParameters(fileName, weights1, bias1, 0, 1);
|
||||
util.saveParameters(fileName, weights2, bias2, 1, 2);
|
||||
}
|
||||
|
||||
MLPPAutoEncoderOld::MLPPAutoEncoderOld(std::vector<std::vector<real_t>> pinputSet, int pn_hidden) {
|
||||
inputSet = pinputSet;
|
||||
n_hidden = pn_hidden;
|
||||
n = inputSet.size();
|
||||
k = inputSet[0].size();
|
||||
|
||||
MLPPActivation avn;
|
||||
y_hat.resize(inputSet.size());
|
||||
|
||||
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
|
||||
weights2 = MLPPUtilities::weightInitialization(n_hidden, k);
|
||||
bias1 = MLPPUtilities::biasInitialization(n_hidden);
|
||||
bias2 = MLPPUtilities::biasInitialization(k);
|
||||
}
|
||||
|
||||
real_t MLPPAutoEncoderOld::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
|
||||
class MLPPCost cost;
|
||||
return cost.MSE(y_hat, inputSet);
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPAutoEncoderOld::Evaluate(std::vector<std::vector<real_t>> X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
|
||||
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
||||
return alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
|
||||
}
|
||||
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPAutoEncoderOld::propagate(std::vector<std::vector<real_t>> X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
|
||||
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
||||
return { z2, a2 };
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPAutoEncoderOld::Evaluate(std::vector<real_t> x) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
|
||||
std::vector<real_t> a2 = avn.sigmoid(z2);
|
||||
return alg.addition(alg.mat_vec_mult(alg.transpose(weights2), a2), bias2);
|
||||
}
|
||||
|
||||
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPAutoEncoderOld::propagate(std::vector<real_t> x) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
|
||||
std::vector<real_t> a2 = avn.sigmoid(z2);
|
||||
return { z2, a2 };
|
||||
}
|
||||
|
||||
void MLPPAutoEncoderOld::forwardPass() {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
|
||||
a2 = avn.sigmoid(z2);
|
||||
y_hat = alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
|
||||
}
|
||||
58
mlpp/auto_encoder/auto_encoder_old.h
Normal file
58
mlpp/auto_encoder/auto_encoder_old.h
Normal file
@ -0,0 +1,58 @@
|
||||
|
||||
#ifndef MLPP_AUTO_ENCODER_OLD_H
|
||||
#define MLPP_AUTO_ENCODER_OLD_H
|
||||
|
||||
//
|
||||
// AutoEncoder.hpp
|
||||
//
|
||||
// Created by Marc Melikyan on 11/4/20.
|
||||
//
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
class MLPPAutoEncoderOld {
|
||||
public:
|
||||
std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
|
||||
std::vector<real_t> modelTest(std::vector<real_t> 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);
|
||||
|
||||
MLPPAutoEncoderOld(std::vector<std::vector<real_t>> inputSet, int n_hidden);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
|
||||
|
||||
std::vector<std::vector<real_t>> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
|
||||
std::vector<real_t> Evaluate(std::vector<real_t> x);
|
||||
std::tuple<std::vector<real_t>, std::vector<real_t>> propagate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<std::vector<real_t>> y_hat;
|
||||
|
||||
std::vector<std::vector<real_t>> weights1;
|
||||
std::vector<std::vector<real_t>> weights2;
|
||||
|
||||
std::vector<real_t> bias1;
|
||||
std::vector<real_t> bias2;
|
||||
|
||||
std::vector<std::vector<real_t>> z2;
|
||||
std::vector<std::vector<real_t>> a2;
|
||||
|
||||
int n;
|
||||
int k;
|
||||
int n_hidden;
|
||||
};
|
||||
|
||||
#endif /* AutoEncoder_hpp */
|
||||
@ -47,6 +47,7 @@
|
||||
#include "../mlpp/uni_lin_reg/uni_lin_reg.h"
|
||||
#include "../mlpp/wgan/wgan.h"
|
||||
|
||||
#include "../mlpp/auto_encoder/auto_encoder_old.h"
|
||||
#include "../mlpp/mlp/mlp_old.h"
|
||||
#include "../mlpp/outlier_finder/outlier_finder_old.h"
|
||||
#include "../mlpp/pca/pca_old.h"
|
||||
@ -495,12 +496,13 @@ void MLPPTests::test_soft_max_network(bool ui) {
|
||||
void MLPPTests::test_autoencoder(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
// AUTOENCODER
|
||||
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 } };
|
||||
MLPPAutoEncoder model(alg.transpose(inputSet), 5);
|
||||
model.SGD(0.001, 300000, ui);
|
||||
alg.printMatrix(model.modelSetTest(alg.transpose(inputSet)));
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
|
||||
// AUTOENCODER
|
||||
MLPPAutoEncoderOld model_old(alg.transpose(inputSet), 5);
|
||||
model_old.SGD(0.001, 300000, ui);
|
||||
alg.printMatrix(model_old.modelSetTest(alg.transpose(inputSet)));
|
||||
std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl;
|
||||
}
|
||||
void MLPPTests::test_dynamically_sized_ann(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user