Selamat siang, penduduk setempat.
Saya sudah lama ingin memposting sesuatu yang cocok di Habr, tetapi tidak ada ide.
Dan kemudian saya teringat tentang salah satu proyek saya, yang terlupakan bersama dengan tempat penyimpanannya direkam. Ini adalah model yang disederhanakan dari makalah saya, ketika saya melakukannya, pengetahuan ada di kepala saya. Dan sekarang Anda harus mengingat dan melakukan semuanya lagi.
Secara umum, proyek ini akan memiliki dua gambar berbeda yang akan dikenali oleh program. Kedua gambar tersebut dibuat secara terprogram menggunakan kerangka kerja qt dan c ++.
Totalnya, sekitar 300 baris kode.
Berikut data gambarnya:
Dan kedua:
Mereka berukuran 400 x 300 piksel.
, , , , , 2 , , .
?
-, "y" - , "x" (400 ). , " " , .
2 :
![w (t + 1) = w (t) -k * | x [i] -w (t) |](https://habrastorage.org/getpro/habr/upload_files/f60/98b/cc4/f6098bcc40cb79ffe16f329d40fa4810.svg)
k - , x[i] - "y" , w(t)- ,w(t+1) - . 2 , , ( x->f(x)->x).
:
.
:
k = 0,75 2- :
, , .
k , 10^-6 .
:
sum1 sum2 - "y" - ( )
v-v - V-, v 2533.56, w-v 39032.4 . : w w . .
10 :
, .
Tetapi jika garis lurus digambar di tengah secara horizontal, maka itu mengenali kedua kelas lebih baik daripada neuron dengan kesalahan jenis pertama:
Selanjutnya adalah kode program main.cpp:
#include <iostream>
#include <QtWidgets/QApplication>
#include "MainWindow.h"
int main(int argc,char*argv[])
{
QApplication a(argc, argv);
QWidget qw;
MainWindow mw(&qw);
mw.show();
mw.CreateImage("");
mw.CreateImage2("");
mw.OpenImage("");
mw.OpenImage2("");
return a.exec();
}
Neuron.h:
#pragma once
#include <ctime>
#include <iostream>
#include <vector>
class Neuron
{
public:
std::vector<double> x, y, x0;
double error;
Neuron(std::vector<double> x, int length, int level);
int level, length, min_, max_;
std::vector<double> Send();
void Lerning(int steps);
double Thinking(std::vector<double> xx);
std::vector<double> SendW();
};
Neuron.cpp:
#include "Neuron.h"
Neuron::Neuron(std::vector<double> x,int length,int level)
{
this->x = x;
this->length = length;
if (level == 2)
{
srand(time(NULL));
min_ = 0;
max_ = 300;
for (size_t i = 0; i < length; i++)
{
x0.push_back(min_ + rand() % (max_ - min_ + 1));
y.push_back(x0[i]);
//std::cout << x0[i] << std::endl;
}
}
this->level = level;
this->error = 0.0;
}
std::vector<double> Neuron::Send()
{
if (level == 1)
return this->x;
else
return this->y;
}
std::vector<double> Neuron::SendW()
{
return this->x0;
}
void Neuron::Lerning(int steps)
{
float k = 0.75;
for (size_t j = 0; j < steps; j++)
{
for (size_t i = 0; i < length; i++)
{
x0[i] = x0[i] + k* (x[i]-x0[i]);
}
}
}
double Neuron::Thinking(std::vector<double> xx)
{
error = 0.0;
for (size_t i = 0; i < length; i++)
{
y[i] = abs(xx[i] - x0[i]);
error += y[i];
}
return error;
}
NeuralNet.h
#pragma once
#include "Neuron.h"
//#include <vector>
class NeuralNet
{
public:
std::vector<Neuron*> l1,l2;
void InitNeurons(std::vector<double> x, int length);
void LearnNeurons(int steps, int i);
double TestNeurons(int i, std::vector<double> xx);
};
NeuralNet.cpp
#include "NeuralNet.h"
void NeuralNet::InitNeurons(std::vector<double> x,int length)
{
l1.push_back(new Neuron(x,length,1));
}
void NeuralNet::LearnNeurons(int steps,int i)
{
l2.push_back(new Neuron(l1[i]->Send(), l1[i]->length, 2));
l2[i]->Lerning(steps);
}
double NeuralNet::TestNeurons(int i, std::vector<double> xx)
{
double res = 0.0 ;
res = l2[i]->Thinking(xx);
return res;
}
MainWindow.h
#pragma once
#include <QtWidgets/qmainwindow.h>
#include <QtGui/qpicture.h>
#include <QtGui/qimage.h>
#include <QtGui/qpainter.h>
#include <QtCore/qdebug.h>
#include <vector>
#include <iostream>
#include <fstream> //
#include <iomanip>
#include "NeuralNet.h"
#pragma comment(lib,"Qt5Core.lib")
#pragma comment(lib,"Qt5Widgets.lib")
#pragma comment(lib,"Qt5Gui.lib")
namespace Ui {
class MainWindow;
}
//Q_OBJECT
class MainWindow : public QMainWindow
{
//Q_OBJECT
public:
explicit MainWindow(QWidget* parent = 0);
void CreateImage(QString path);
void CreateImage2(QString path);
void OpenImage(QString path);
void OpenImage2(QString path);
std::vector<double> x1, x2, zeros1, zeros2;
QImage* image_t1, * image_t2 ;
NeuralNet net;
// ~MainWindow();
protected:
void paintEvent(QPaintEvent*); //
private:
Ui::MainWindow* ui;
};
MainWindow.cpp
#include "MainWindow.h"
MainWindow::MainWindow(QWidget* parent)
: QMainWindow(parent)
{
setWindowTitle(tr("Neural"));
setGeometry(0, 0, 1000, 700);
}
void MainWindow::paintEvent(QPaintEvent*)
{
QImage img("testImage.png"); //
QPainter painter(this); // painter,
painter.drawImage(0, 0, img.scaled(this->size())); // 0,0
}
void MainWindow::CreateImage(QString path)
{
QImage image(QSize(400, 300), QImage::Format_RGB32);
QPainter painter(&image);
painter.fillRect(QRectF(0, 0, 400, 300), Qt::white);
painter.setPen(QPen(Qt::black));
painter.drawLine(0, 0, image.width() / 2, image.height());
painter.drawLine(image.width() / 2, image.height(), image.width(), 0);
image.save("testImage.png");
}
void MainWindow::CreateImage2(QString path)
{
QImage image(QSize(400, 300), QImage::Format_RGB32);
QPainter painter(&image);
painter.fillRect(QRectF(0, 0, 400, 300), Qt::white);
painter.setPen(QPen(Qt::black));
painter.drawLine(0, 0, image.width() / 4, image.height());
painter.drawLine(image.width() / 4, image.height() , image.width() / 2, 0);
painter.drawLine(image.width() / 2, 0, 3 * image.width() / 4, image.height());
painter.drawLine(3*image.width() / 4, image.height(), image.width() , 0);
image.save("testImage2.png");
}
void MainWindow::OpenImage(QString path)
{
image_t1 = new QImage("testImage.png");
QPoint qp;
std::ofstream fout("data.txt", std::ios_base::out | std::ios_base::trunc);
for (int i = 0; i < image_t1->width(); i++)
{
for (int j = 0; j < image_t1->height(); j++)
{
qp.setX(i);
qp.setY(j);
if (image_t1->pixel(qp) != 4294967295/* 4278190080*/)
{
x1.push_back(j);
zeros1.push_back(abs(150-j));
//qDebug() << j << " ";
fout << j<< std::endl;
break;
}
}
}
qDebug() <<"size:"<< x1.size() << " ";
int sum = 0;
for (int j = 0; j < zeros1.size(); j++)
{
sum += zeros1[j];
}
qDebug() << "sum1=" << sum << " ";
fout.close();
net.InitNeurons(x1, x1.size());
net.LearnNeurons(10,0);
//net.TestNeurons(0);
for (size_t i = 0; i < net.l2[0]->SendW().size(); i++)
{
qp.setX(i);
qp.setY(int(net.l2[0]->SendW()[i]));
image_t1->setPixel(qp, Qt::red);
}
image_t1->save("testImage1-1-1-1-000000.png");
}
void MainWindow::OpenImage2(QString path)
{
image_t2 = new QImage("testImage2.png");
QPoint qp;
std::ofstream fout("data.txt", std::ios_base::out | std::ios_base::trunc);
for (int i = 0; i < image_t2->width(); i++)
{
for (int j = 0; j < image_t2->height(); j++)
{
qp.setX(i);
qp.setY(j);
if (image_t2->pixel(qp) != 4294967295/* 4278190080*/)
{
x2.push_back(j);
fout << j << std::endl;
zeros2.push_back(abs(150 - j));
//qDebug() << j << " ";
break;
}
}
}
qDebug() << "size:" << x2.size() << " ";
int sum = 0;
for (int j = 0; j < zeros2.size(); j++)
{
sum += zeros2[j];
}
qDebug()<<"sum2=" << sum << " ";
fout.close();
net.InitNeurons(x2, x2.size());
net.LearnNeurons(10,1);
qDebug() <<"v - v"<< net.TestNeurons(0, x1);
qDebug() <<"v - w"<< net.TestNeurons(0, x2);
qDebug() <<"w - v"<< net.TestNeurons(1, x1);
qDebug() <<"w - w"<< net.TestNeurons(1, x2);
for (size_t i = 0; i < net.l2[1]->SendW().size(); i++)
{
qp.setX(i);
qp.setY(int(net.l2[1]->SendW()[i]));
//qDebug() << int(net.l2[1]->SendW()[i]) << " ";// std::endl;
image_t2->setPixel(qp, Qt::red);
}
image_t2->save("testImage2-2-2-2-000000.png");
}
Terima kasih atas perhatiannya!