Removed useless training images, added MNIST database instead

See http://yann.lecun.com/exdb/mnist/
This commit is contained in:
Michael Mandl 2015-10-29 13:06:30 +01:00
parent 5778afa121
commit 83b4562a29
30 changed files with 34 additions and 133 deletions

View file

@ -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;
dataLoader.addSamples("../NeuroUI/training data/mnist_train0.jpg", 0);
emit progress(0.1);
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);
MnistLoader mnistLoader;
mnistLoader.load("../NeuroUI/MNIST Aatabase/train-images.idx3-ubyte",
"../NeuroUI/MNIST Aatabase/train-labels.idx1-ubyte");
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();