First implementation of weight updates. Very slow rate of change in the output value.

Layer.cpp

@@ -51,6 +51,21 @@ void Layer::connectTo(const Layer & nextLayer)
 	}
 }
 
-void Layer::updateInputWeights(const Layer & prevLayer)
+void Layer::updateInputWeights(Layer & prevLayer)
 {
+	static const double trainingRate = 0.8;
+
+	for (size_t currentLayerIndex = 0; currentLayerIndex < size() - 1; ++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);
+		}
+	}
 }

Layer.h

@@ -14,5 +14,5 @@ public:
 	double getWeightedSum(int outputNeuron) const;
 	void connectTo(const Layer & nextLayer);
 
-	void updateInputWeights(const Layer &prevLayer);
+	void updateInputWeights(Layer &prevLayer);
 };

Neuro.cpp

@@ -14,18 +14,21 @@ int main()
 
 		Net myNet({ inputValues.size(), 4, targetValues.size() });
 
-		myNet.feedForward(inputValues);
-
-		std::vector<double> outputValues = myNet.getOutput();
-
-		std::cout << "Result: ";
-		for (double &value : outputValues)
+		for (int i = 0; i < 20; ++i)
 		{
-			std::cout << value << " ";
-		}
-		std::cout << std::endl;
+			myNet.feedForward(inputValues);
 
-		myNet.backProp(targetValues);
+			std::vector<double> outputValues = myNet.getOutput();
+
+			std::cout << "Result: ";
+			for (double &value : outputValues)
+			{
+				std::cout << value << " ";
+			}
+			std::cout << std::endl;
+
+			myNet.backProp(targetValues);
+		}
 	}
 	catch (std::exception &ex)
 	{

Neuron.cpp

@@ -84,3 +84,13 @@ double Neuron::getGradient() const
 	return gradient;
 }
 
+double Neuron::getOutputWeight(size_t index) const
+{
+	return outputWeights.at(index);
+}
+
+void Neuron::setOutputWeight(size_t index, double value)
+{
+	outputWeights.at(index) = value;
+}
+

Neuron.h

@@ -25,6 +25,9 @@ public:
 
 	double getGradient() const;
 
+	double getOutputWeight(size_t index) const;
+	void setOutputWeight(size_t index, double value);
+
 private:
 	static double transferFunction(double inputValue);
 	static double transferFunctionDerivative(double inputValue);