backpropagation implementation

src/NeuralNetwork/Learning/BackPropagation.cpp

@@ -0,0 +1,54 @@
+#include <NeuralNetwork/Learning/BackPropagation.h>
+
+#include <cassert>
+
+void NeuralNetwork::Learning::BackPropagation::teach(FeedForward::Network &network,const std::vector<float> &input, const std::vector<float> &expectation) {
+
+	network.computeOutput(input);
+	std::vector<std::vector<float>> deltas;
+
+	deltas.resize(network.size());
+
+	for(std::size_t i=0; i < network.size(); i++) {
+		deltas[i].resize(network[i].size());
+		deltas[i][0]=0.0;
+	}
+
+	auto& outputLayer=network[network.size()-1];
+	for(std::size_t j=1;j<outputLayer.size();j++) {
+		auto& neuron = outputLayer[j];
+		deltas[network.size()-1][j]=correction( expectation[j-1], neuron.output())*
+										neuron.getActivationFunction().derivatedOutput(neuron.value(),neuron.output());
+	}
+
+	for(int layerIndex=static_cast<int>(network.size()-2);layerIndex>0;layerIndex--) {
+		auto &layer=network[layerIndex];
+
+		for(std::size_t j=1;j<layer.size();j++) {
+			float deltasWeight = 0;
+			for(std::size_t k=1;k<network[layerIndex+1].size();k++) {
+				deltasWeight+=deltas[layerIndex+1][k]* network[layerIndex+1][k].getWeight(layer[j]);
+			}
+			float newDelta=deltasWeight*layer[j].getActivationFunction().derivatedOutput(layer[j].value(),layer[j].output());
+			deltas[layerIndex][j]=newDelta;
+		}
+	}
+
+	for(std::size_t layerIndex=1;layerIndex<network.size();layerIndex++) {
+		auto &layer=network[layerIndex];
+		auto &prevLayer=network[layerIndex-1];
+
+		std::size_t max=prevLayer.size();
+
+		for(std::size_t j=1;j<layer.size();j++)  {
+			layer[j].setWeight(prevLayer[0],layer[j].getWeight(prevLayer[0])+deltas[layerIndex][j]*learningCoefficient);
+			for(std::size_t k=1;k<max;k++) {
+				if(layerIndex==1) {
+					layer[j].setWeight(prevLayer[k], layer[j].getWeight(prevLayer[k])+learningCoefficient*deltas[layerIndex][j]*input[k-1]);
+				} else {
+					layer[j].setWeight(prevLayer[k], layer[j].getWeight(prevLayer[k])+learningCoefficient*deltas[layerIndex][j]*prevLayer[k].output());
+				}
+			}
+		}
+	}
+}