iRprop+ implementation

include/NeuralNetwork/Learning/iRPropPlus.h

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+#pragma once
+
+#include "BatchPropagation.h"
+
+namespace NeuralNetwork {
+	namespace Learning {
+
+		/** @class Resilient Propagation
+		 * @brief
+		 */
+		class iRPropPlus : public BatchPropagation {
+
+			public:
+				iRPropPlus(FeedForward::Network &feedForwardNetwork, std::shared_ptr<CorrectionFunction::CorrectionFunction> correction = std::make_shared<CorrectionFunction::Linear>()):
+					BatchPropagation(feedForwardNetwork, correction) {
+				}
+
+				iRPropPlus(const iRPropPlus&)=delete;
+				iRPropPlus& operator=(const NeuralNetwork::Learning::iRPropPlus&) = delete;
+
+				void setInitialWeightChange(float initVal) {
+					initialWeightChange=initVal;
+				}
+				void setLearningCoefficient(float) {
+
+				}
+			protected:
+
+				virtual inline void resize() override {
+					BatchPropagation::resize();
+
+					_lastGradients =_gradients;
+
+					_changesOfWeightChanges = _lastGradients;
+					for(std::size_t i = 1; i < _network.size(); i++) {
+						for(std::size_t j = 0; j < _changesOfWeightChanges[i].size(); j++) {
+							std::fill(_changesOfWeightChanges[i][j].begin(),_changesOfWeightChanges[i][j].end(),initialWeightChange);
+						}
+					}
+					_lastWeightChanges = _lastGradients;
+					for(std::size_t i = 1; i < _network.size(); i++) {
+						for(std::size_t j = 0; j < _lastWeightChanges[i].size(); j++) {
+							std::fill(_lastWeightChanges[i][j].begin(),_lastWeightChanges[i][j].end(),0.1);
+						}
+					}
+				}
+
+				void updateWeightsAndEndBatch() override;
+
+				std::vector<std::vector<std::vector<float>>> _lastGradients = {};
+				std::vector<std::vector<std::vector<float>>> _lastWeightChanges = {};
+				std::vector<std::vector<std::vector<float>>> _changesOfWeightChanges = {};
+
+				float _prevError=0;
+
+				float maxChangeOfWeights = 5;
+				float minChangeOfWeights = 0.0001;
+
+				float initialWeightChange=0.02;
+				float weightChangePlus=1.2;
+				float weightChangeMinus=0.5;
+		};
+	}
+}

src/NeuralNetwork/Learning/iRPropPlus.cpp

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+#include <NeuralNetwork/Learning/iRPropPlus.h>
+
+void NeuralNetwork::Learning::iRPropPlus::updateWeightsAndEndBatch() {
+	float error = 0.0;
+	const auto& outputLayer=_network[_network.size()-1];
+	for(std::size_t j=1;j<outputLayer.size();j++) {
+		error+=_slopes[_network.size()-1][j];
+	}
+
+	error /= outputLayer.size();
+
+	for(std::size_t layerIndex=1;layerIndex<_network.size();layerIndex++) {
+		auto &layer = _network[layerIndex];
+		auto &prevLayer = _network[layerIndex - 1];
+
+		std::size_t prevLayerSize = prevLayer.size();
+		std::size_t layerSize = layer.size();
+
+		for(std::size_t j = 1; j < layerSize; j++) {
+			for(std::size_t k = 0; k < prevLayerSize; k++) {
+				float gradient = _gradients[layerIndex][j][k];
+				float lastGradient = _lastGradients[layerIndex][j][k];
+
+				_lastGradients[layerIndex][j][k] = gradient;
+
+				float weightChangeDelta = _changesOfWeightChanges[layerIndex][j][k];
+				float delta;
+
+				if(gradient * lastGradient > 0) {
+					weightChangeDelta = std::min(weightChangeDelta*weightChangePlus,maxChangeOfWeights);
+					delta = (std::signbit(gradient)? 1.0f : -1.0f ) * weightChangeDelta;
+					layer[j].weight(k) -= delta;
+				} else if (gradient * lastGradient < 0) {
+					weightChangeDelta = std::max(weightChangeDelta*weightChangeMinus,minChangeOfWeights);
+					delta = _lastWeightChanges[layerIndex][j][k];
+					if(error > _prevError) {
+						layer[j].weight(k) += delta;
+					}
+					_lastGradients[layerIndex][j][k] = 0;
+				} else {
+					delta = (std::signbit(gradient)? 1.0f : -1.0f ) * weightChangeDelta;
+					layer[j].weight(k) -= delta;
+				}
+				//std::cout << delta <<"\n";
+
+				_changesOfWeightChanges[layerIndex][j][k] = weightChangeDelta;
+				_lastWeightChanges[layerIndex][j][k] = delta;
+			}
+		}
+	}
+	_prevError = error;
+}