Backprop seems to be working, yay

Layer.cpp

@@ -53,19 +53,37 @@ void Layer::connectTo(const Layer & nextLayer)
 
 void Layer::updateInputWeights(Layer & prevLayer)
 {
-	static const double trainingRate = 0.8;
+	static const double trainingRate = 0.5;
 
-	for (size_t currentLayerIndex = 0; currentLayerIndex < size() - 1; ++currentLayerIndex)
+	for (size_t currentLayerIndex = 0; currentLayerIndex < sizeWithoutBiasNeuron(); ++currentLayerIndex)
 	{
 		Neuron &targetNeuron = at(currentLayerIndex);
 
 		for (size_t prevLayerIndex = 0; prevLayerIndex < prevLayer.size(); ++prevLayerIndex)
 		{
 			Neuron &sourceNeuron = prevLayer.at(prevLayerIndex);
-
+		
 			sourceNeuron.setOutputWeight(currentLayerIndex, 
 				sourceNeuron.getOutputWeight(currentLayerIndex) + 
 				sourceNeuron.getOutputValue() * targetNeuron.getGradient() * trainingRate);
 		}
 	}
 }
+
+void Layer::addBiasNeuron()
+{
+	push_back(Neuron(1.0));
+	hasBiasNeuron = true;
+}
+
+size_t Layer::sizeWithoutBiasNeuron() const
+{
+	if (hasBiasNeuron)
+	{
+		return size() - 1;
+	}
+	else
+	{
+		return size();
+	}
+}

Layer.h

@@ -6,6 +6,9 @@
 
 class Layer : public std::vector < Neuron >
 {
+private:
+	bool hasBiasNeuron = false;
+
 public:
 	Layer(size_t numNeurons);
 
@@ -15,4 +18,8 @@ public:
 	void connectTo(const Layer & nextLayer);
 
 	void updateInputWeights(Layer &prevLayer);
+
+	void addBiasNeuron();
+
+	size_t sizeWithoutBiasNeuron() const;
 };

Net.cpp

@@ -17,8 +17,7 @@ Net::Net(std::initializer_list<size_t> layerSizes)
 		Layer &currentLayer = *layerIt;
 		const Layer &nextLayer = *(layerIt + 1);
 
-		Neuron biasNeuron(1.0);
-		currentLayer.push_back(biasNeuron);
+		currentLayer.addBiasNeuron();
 
 		currentLayer.connectTo(nextLayer);
 	}

Neuro.cpp

@@ -9,12 +9,12 @@ int main()
 	{
 		std::cout << "Neuro running" << std::endl;
 
-		std::vector<double> inputValues = { 1.0, 4.0, 5.0 };
-		std::vector<double> targetValues = { 3.0 };
+		std::vector<double> inputValues = { 0.1, 0.2, 0.8 };
+		std::vector<double> targetValues = { 0.8 };
 
 		Net myNet({ inputValues.size(), 4, targetValues.size() });
 
-		for (int i = 0; i < 20; ++i)
+		for (int i = 0; i < 200; ++i)
 		{
 			myNet.feedForward(inputValues);