Reduced the progress update messages to take load from the UI,

implemented load-or-create in test funtion

Net.cpp

@@ -4,27 +4,14 @@
 #include <iostream>
 #include <fstream>
 
+Net::Net()
+{
+
+}
+
 Net::Net(std::initializer_list<size_t> layerSizes)
 {
-	if (layerSizes.size() < 2)
+    initialize(layerSizes);
-	{
-		throw std::exception("A net needs at least 2 layers");
-	}
-
-	for (size_t numNeurons : layerSizes)
-	{
-		push_back(Layer(numNeurons));
-	}
-
-	for (auto layerIt = begin(); layerIt != end() - 1; ++layerIt)
-	{
-		Layer &currentLayer = *layerIt;
-		const Layer &nextLayer = *(layerIt + 1);
-
-		currentLayer.addBiasNeuron();
-
-		currentLayer.connectTo(nextLayer);
-	}
 }
 
 Net::Net(const std::string &filename)
@@ -32,6 +19,31 @@ Net::Net(const std::string &filename)
     load(filename);
 }
 
+void Net::initialize(std::initializer_list<size_t> layerSizes)
+{
+    clear();
+
+    if (layerSizes.size() < 2)
+    {
+        throw std::exception("A net needs at least 2 layers");
+    }
+
+    for (size_t numNeurons : layerSizes)
+    {
+        push_back(Layer(numNeurons));
+    }
+
+    for (auto layerIt = begin(); layerIt != end() - 1; ++layerIt)
+    {
+        Layer &currentLayer = *layerIt;
+        const Layer &nextLayer = *(layerIt + 1);
+
+        currentLayer.addBiasNeuron();
+
+        currentLayer.connectTo(nextLayer);
+    }
+}
+
 void Net::feedForward(const std::vector<double> &inputValues)
 {
 	Layer &inputLayer = front();

Net.h

@@ -7,9 +7,12 @@
 class Net : public std::vector < Layer >
 {
 public:
+    Net();
 	Net(std::initializer_list<size_t> layerSizes);
     Net(const std::string &filename);
 
+    void initialize(std::initializer_list<size_t> layerSizes);
+
 	void feedForward(const std::vector<double> &inputValues);
 	std::vector<double> getOutput();
 	void backProp(const std::vector<double> &targetValues);

gui/NeuroUI/netlearner.cpp

@@ -9,7 +9,15 @@ void NetLearner::run()
     {
         QElapsedTimer timer;
 
-        Net myNet({2, 3, 1});
+        Net myNet;
+        try
+        {
+            myNet.load("mynet.nnet");
+        }
+        catch (...)
+        {
+            myNet.initialize({2, 3, 1});
+        }
 
         size_t batchSize = 5000;
         size_t batchIndex = 0;
@@ -54,6 +62,7 @@ void NetLearner::run()
 
                 emit logMessage(logString);
                 emit currentNetError(batchMaxError);
+                emit progress((double)iteration / (double)numIterations);
 
                 batchIndex = 0;
                 batchMaxError = 0.0;
@@ -61,8 +70,6 @@ void NetLearner::run()
             }
 
             myNet.backProp(targetValues);
-
-            emit progress((double)iteration / (double)numIterations);
         }
 
         QString timerLogString;