Renamed a few things, started working on back-propagation

Layer.cpp

@@ -18,8 +18,7 @@ void Layer::setOutputValues(const std::vector<double> & outputValues)
 	auto neuronIt = begin();
 	for (const double &value : outputValues)
 	{
-		neuronIt->setOutputValue(value);
-		neuronIt++;
+		(neuronIt++)->setOutputValue(value);
 	}
 }
 
@@ -48,6 +47,6 @@ void Layer::connectTo(const Layer & nextLayer)
 {
 	for (Neuron &neuron : *this)
 	{
-		neuron.createOutputWeights(nextLayer.size());
+		neuron.createRandomOutputWeights(nextLayer.size());
 	}
 }

Net.cpp

@@ -17,7 +17,8 @@ Net::Net(std::initializer_list<unsigned int> layerSizes)
 		Layer &currentLayer = *layerIt;
 		const Layer &nextLayer = *(layerIt + 1);
 
-		currentLayer.push_back(Neuron(1.0));
+		Neuron biasNeuron(1.0);
+		currentLayer.push_back(biasNeuron);
 
 		currentLayer.connectTo(nextLayer);
 	}
@@ -43,7 +44,7 @@ void Net::feedForward(const std::vector<double> &inputValues)
 	}
 }
 
-std::vector<double> Net::getResult()
+std::vector<double> Net::getOutput()
 {
 	std::vector<double> result;
 
@@ -65,7 +66,7 @@ void Net::backProp(const std::vector<double> &targetValues)
 		throw std::exception("The number of target values has to match the output layer size");
 	}
 
-	std::vector<double> resultValues = getResult();
+	std::vector<double> resultValues = getOutput();
 
 	double rmsError = 0.0;
 	for (unsigned int i = 0; i < resultValues.size(); ++i)

Net.h

@@ -10,6 +10,6 @@ public:
 	Net(std::initializer_list<unsigned int> layerSizes);
 
 	void feedForward(const std::vector<double> &inputValues);
-	std::vector<double> getResult();
+	std::vector<double> getOutput();
 	void backProp(const std::vector<double> &targetValues);
 };

Neuro.cpp

@@ -9,18 +9,23 @@ int main()
 	{
 		std::cout << "Neuro running" << std::endl;
 
-		Net myNet({ 3, 4, 2 });
+		std::vector<double> inputValues = { 1.0, 4.0, 5.0 };
+		std::vector<double> targetValues = { 3.0 };
 
-		myNet.feedForward({ 1.0, 2.0, 3.0 });
+		Net myNet({ inputValues.size(), 4, targetValues.size() });
 
-		std::vector<double> result = myNet.getResult();
+		myNet.feedForward(inputValues);
+
+		std::vector<double> outputValues = myNet.getOutput();
 
 		std::cout << "Result: ";
-		for (double &value : result)
+		for (double &value : outputValues)
 		{
 			std::cout << value << " ";
 		}
 		std::cout << std::endl;
+
+		myNet.backProp(targetValues);
 	}
 	catch (std::exception &ex)
 	{

Neuro.vcxproj

@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="utf-8"?>
-<Project DefaultTargets="Build" ToolsVersion="12.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
   <ItemGroup Label="ProjectConfigurations">
     <ProjectConfiguration Include="Debug|Win32">
       <Configuration>Debug</Configuration>
@@ -19,13 +19,13 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>true</UseDebugLibraries>
-    <PlatformToolset>v120</PlatformToolset>
+    <PlatformToolset>v140</PlatformToolset>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>false</UseDebugLibraries>
-    <PlatformToolset>v120</PlatformToolset>
+    <PlatformToolset>v140</PlatformToolset>
     <WholeProgramOptimization>true</WholeProgramOptimization>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>

Neuron.cpp

@@ -25,7 +25,7 @@ double Neuron::transferFunctionDerivative(double inputValue)
 
 void Neuron::feedForward(double inputValue)
 {
-	outputValue = Neuron::transferFunction(inputValue);
+	outputValue = transferFunction(inputValue);
 }
 
 double Neuron::getWeightedOutputValue(unsigned int outputNeuron) const
@@ -38,11 +38,11 @@ double Neuron::getWeightedOutputValue(unsigned int outputNeuron) const
 	return 0.0;
 }
 
-void Neuron::createOutputWeights(unsigned int number)
+void Neuron::createRandomOutputWeights(unsigned int numberOfWeights)
 {
 	outputWeights.clear();
 
-	for (unsigned int i = 0; i < number; ++i)
+	for (unsigned int i = 0; i < numberOfWeights; ++i)
 	{
 		outputWeights.push_back(std::rand() / (double)RAND_MAX);
 	}

Neuron.h

@@ -16,6 +16,6 @@ public:
 	static double transferFunctionDerivative(double inputValue);
 	void feedForward(double inputValue);
 	double getWeightedOutputValue(unsigned int outputNeuron) const;
-	void createOutputWeights(unsigned int number);
+	void createRandomOutputWeights(unsigned int numberOfWeights);
 	double getOutputValue() const;
 };