From cd1101dfe252e1c4701c0804f4c00c4f1ed6be6c Mon Sep 17 00:00:00 2001
From: Michael Mandl <mandlm@molez.net>
Date: Sat, 31 Oct 2015 14:58:49 +0100
Subject: [PATCH] Learning from and displaying of digit samples

---
 gui/NeuroUI/mnistloader.cpp | 19 +++++++++----------
 gui/NeuroUI/mnistloader.h   |  8 ++++----
 gui/NeuroUI/netlearner.cpp  | 24 ++++++++++++++++--------
 3 files changed, 29 insertions(+), 22 deletions(-)

diff --git a/gui/NeuroUI/mnistloader.cpp b/gui/NeuroUI/mnistloader.cpp
index 4eac8bb..67c4c1f 100644
--- a/gui/NeuroUI/mnistloader.cpp
+++ b/gui/NeuroUI/mnistloader.cpp
@@ -1,16 +1,6 @@
 #include "mnistloader.h"
 
 #include <fstream>
-#include <functional>
-#include <memory>
-#include <list>
-
-#include <intrin.h>
-
-MnistLoader::MnistLoader()
-{
-
-}
 
 void MnistLoader::load(const std::string &databaseFileName, const std::string &labelsFileName)
 {
@@ -18,6 +8,13 @@ void MnistLoader::load(const std::string &databaseFileName, const std::string &l
     loadLabels(labelsFileName);
 }
 
+const MnistLoader::MnistSample &MnistLoader::getRandomSample() const
+{
+    size_t sampleIndex = (std::rand() * (samples.size() - 1)) / RAND_MAX;
+
+    return *(samples[sampleIndex].get());
+}
+
 void MnistLoader::loadDatabase(const std::string &fileName)
 {
     std::ifstream databaseFile;
@@ -43,6 +40,8 @@ void MnistLoader::loadDatabase(const std::string &fileName)
         throw std::runtime_error("unexpected sample size loading MNIST database");
     }
 
+    samples.reserve(samples.size() + sampleCount);
+
     for (int32_t sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
     {
         std::unique_ptr<MnistSample> sample = std::make_unique<MnistSample>();
diff --git a/gui/NeuroUI/mnistloader.h b/gui/NeuroUI/mnistloader.h
index 482931c..d2a1e2b 100644
--- a/gui/NeuroUI/mnistloader.h
+++ b/gui/NeuroUI/mnistloader.h
@@ -2,7 +2,7 @@
 #define MNISTLOADER_H
 
 #include <string>
-#include <list>
+#include <vector>
 #include <memory>
 #include <inttypes.h>
 
@@ -26,13 +26,13 @@ public:
     using MnistSample = Sample<SampleWidth, SampleHeight>;
 
 private:
-    std::list<std::unique_ptr<MnistSample>> samples;
+    std::vector<std::unique_ptr<MnistSample>> samples;
 
 public:
-    MnistLoader();
-
     void load(const std::string &databaseFileName, const std::string &labelsFileName);
 
+    const MnistSample &getRandomSample() const;
+
 private:
     void loadDatabase(const std::string &fileName);
     void loadLabels(const std::string &fileName);
diff --git a/gui/NeuroUI/netlearner.cpp b/gui/NeuroUI/netlearner.cpp
index 0b96baf..b0956e1 100644
--- a/gui/NeuroUI/netlearner.cpp
+++ b/gui/NeuroUI/netlearner.cpp
@@ -12,30 +12,38 @@ void NetLearner::run()
         QElapsedTimer timer;
 
         emit logMessage("Loading training data...");
-        emit progress(0.0);
 
         MnistLoader mnistLoader;
         mnistLoader.load("../NeuroUI/MNIST Database/train-images.idx3-ubyte",
                          "../NeuroUI/MNIST Database/train-labels.idx1-ubyte");
 
         emit logMessage("done");
-        emit progress(0.0);
 
-        return;
-
-        Net digitClassifier({32*32, 16*16, 32, 1});
+        Net digitClassifier({28*28, 256, 1});
 
         timer.start();
 
-        size_t numIterations = 10000;
+        size_t numIterations = 100000;
         for (size_t iteration = 0; iteration < numIterations; ++iteration)
         {
+            auto trainingSample = mnistLoader.getRandomSample();
+
+            QImage trainingImage(trainingSample.data, 28, 28, QImage::Format_Grayscale8);
+            emit sampleImageLoaded(trainingImage);
+
             std::vector<double> targetValues =
             {
-                //trainingSample.first / 10.0
+                trainingSample.label / 10.0
             };
 
-            //digitClassifier.feedForward(trainingSample.second);
+            std::vector<double> trainingData;
+            trainingData.reserve(28*28);
+            for (const uint8_t &val : trainingSample.data)
+            {
+                trainingData.push_back(val / 255.0);
+            }
+
+            digitClassifier.feedForward(trainingData);
 
             std::vector<double> outputValues = digitClassifier.getOutput();