iRprop+ implementation

include/NeuralNetwork/Learning/iRPropPlus.h

@@ -0,0 +1,64 @@
+#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;
+		};
+	}
+}