Removed useless training images, added MNIST database instead

See http://yann.lecun.com/exdb/mnist/

gui/NeuroUI/NeuroUI.pro

@@ -19,7 +19,7 @@ SOURCES += main.cpp\
     ../../Neuron.cpp \
     netlearner.cpp \
     errorplotter.cpp \
-    trainingdataloader.cpp
+    mnistloader.cpp
 
 HEADERS  += neuroui.h \
     ../../Layer.h \
@@ -27,7 +27,7 @@ HEADERS  += neuroui.h \
     ../../Neuron.h \
     netlearner.h \
     errorplotter.h \
-    trainingdataloader.h
+    mnistloader.h
 
 FORMS    += neuroui.ui
 

gui/NeuroUI/mnistloader.cpp

@@ -0,0 +1,12 @@
+#include "mnistloader.h"
+
+MnistLoader::MnistLoader()
+{
+
+}
+
+void MnistLoader::load(const std::string &databaseFileName, const std::string &labelsFileName)
+{
+
+}
+

gui/NeuroUI/mnistloader.h

@@ -0,0 +1,14 @@
+#ifndef MNISTLOADER_H
+#define MNISTLOADER_H
+
+#include <string>
+
+class MnistLoader
+{
+public:
+    MnistLoader();
+
+    void load(const std::string &databaseFileName, const std::string &labelsFileName);
+};
+
+#endif // MNISTLOADER_H

gui/NeuroUI/netlearner.cpp

@@ -1,6 +1,6 @@
 #include "netlearner.h"
 #include "../../Net.h"
-#include "trainingdataloader.h"
+#include "mnistloader.h"
 
 #include <QElapsedTimer>
 #include <QImage>
@@ -14,27 +14,9 @@ void NetLearner::run()
         emit logMessage("Loading training data...");
         emit progress(0.0);
 
-        TrainingDataLoader dataLoader;
+        MnistLoader mnistLoader;
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train0.jpg", 0);
+        mnistLoader.load("../NeuroUI/MNIST Aatabase/train-images.idx3-ubyte",
-        emit progress(0.1);
+                         "../NeuroUI/MNIST Aatabase/train-labels.idx1-ubyte");
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train1.jpg", 1);
-        emit progress(0.2);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train2.jpg", 2);
-        emit progress(0.3);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train3.jpg", 3);
-        emit progress(0.4);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train4.jpg", 4);
-        emit progress(0.5);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train5.jpg", 5);
-        emit progress(0.6);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train6.jpg", 6);
-        emit progress(0.7);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train7.jpg", 7);
-        emit progress(0.8);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train8.jpg", 8);
-        emit progress(0.9);
-        dataLoader.addSamples("../NeuroUI/training data/mnist_train9.jpg", 9);
-        emit progress(1.0);
 
         emit logMessage("done");
         emit progress(0.0);
@@ -46,25 +28,12 @@ void NetLearner::run()
         size_t numIterations = 10000;
         for (size_t iteration = 0; iteration < numIterations; ++iteration)
         {
-            const TrainingDataLoader::Sample &trainingSample = dataLoader.getRandomSample();
-
-            QImage sampleImage(32, 32, QImage::Format_ARGB32);
-            for (unsigned int y = 0; y < 32; ++y)
-            {
-                for (unsigned int x = 0; x < 32; ++x)
-                {
-                    uchar grayValue = trainingSample.second[x + y * 32] * 255;
-                    sampleImage.setPixel(x, y, qRgb(grayValue, grayValue, grayValue));
-                }
-            }
-            emit sampleImageLoaded(sampleImage);
-
             std::vector<double> targetValues =
             {
-                trainingSample.first / 10.0
+                //trainingSample.first / 10.0
             };
 
-            digitClassifier.feedForward(trainingSample.second);
+            //digitClassifier.feedForward(trainingSample.second);
 
             std::vector<double> outputValues = digitClassifier.getOutput();
 

gui/NeuroUI/trainingdataloader.cpp

@@ -1,66 +0,0 @@
-#include "trainingdataloader.h"
-
-#include <sstream>
-
-#include <QImage>
-#include <QColor>
-
-TrainingDataLoader::TrainingDataLoader()
-{
-
-}
-
-void TrainingDataLoader::addSamples(const QString &sourceFile, TrainingDataLoader::SampleId sampleId)
-{
-    QImage sourceImage;
-    if (sourceImage.load(sourceFile) == false)
-    {
-        std::ostringstream errorString;
-        errorString << "error loading " << sourceFile.toStdString();
-
-        throw std::runtime_error(errorString.str());
-    }
-
-    QSize scanWindow(32, 32);
-    QPoint scanPosition(0, 0);
-
-    while (scanPosition.y() + scanWindow.height() < sourceImage.height())
-    {
-        scanPosition.setX(0);
-
-        while (scanPosition.x() + scanWindow.width() < sourceImage.width())
-        {
-            Sample sample;
-            sample.first = sampleId;
-
-            for (int y = 0; y < scanWindow.height(); ++y)
-            {
-                for (int x = 0; x < scanWindow.width(); ++x)
-                {
-                    QRgb pixelColor = sourceImage.pixel(scanPosition.x() + x, scanPosition.y() + y);
-                    uint grayValue = qGray(pixelColor);
-                    sample.second[x + y * scanWindow.height()] = grayValue / 255.0;
-                }
-            }
-
-            m_samples.push_back(sample);
-
-            scanPosition.rx() += scanWindow.width();
-        }
-
-        scanPosition.ry() += scanWindow.height();
-    }
-}
-
-const TrainingDataLoader::Sample &TrainingDataLoader::getRandomSample() const
-{
-    size_t sampleIndex = (std::rand() * m_samples.size()) / RAND_MAX;
-
-    auto it = m_samples.cbegin();
-    for (size_t index = 0; index < sampleIndex; ++index)
-    {
-        it++;
-    }
-    return *it;
-}
-

gui/NeuroUI/trainingdataloader.h

@@ -1,28 +0,0 @@
-#ifndef TRAININGDATALOADER_H
-#define TRAININGDATALOADER_H
-
-#include <utility>
-#include <list>
-#include <string>
-
-#include <QString>
-
-class TrainingDataLoader
-{
-public:
-    using SampleData = double[32*32];
-    using SampleId = unsigned int;
-    using Sample = std::pair<SampleId, SampleData>;
-
-private:
-    std::list<Sample> m_samples;
-
-public:
-    TrainingDataLoader();
-
-    void addSamples(const QString &sourceFile, SampleId sampleId);
-
-    const Sample &getRandomSample() const;
-};
-
-#endif // TRAININGDATALOADER_H