backprop: momentums + decay, quickprop: renaming

include/NeuralNetwork/Learning/BackPropagation.h

@@ -31,6 +31,22 @@ namespace Learning {
 
 			inline virtual void setLearningCoefficient (const float& coefficient) { learningCoefficient=coefficient; }
 
+			float getMomentumWeight() const {
+				return momentumWeight;
+			}
+
+			void setMomentumWeight(const float& m) {
+				momentumWeight=m;
+			}
+
+			float getWeightDecay() const {
+				return weightDecay;
+			}
+
+			void setWeightDecay(const float& wd) {
+				weightDecay=wd;
+			}
+
 		protected:
 
 			virtual inline void resize() {
@@ -41,11 +57,25 @@ namespace Learning {
 					if(slopes[i].size()!=network[i].size())
 						slopes[i].resize(network[i].size());
 				}
+
+				if(lastDeltas.size()!=network.size())
+					lastDeltas.resize(network.size());
+
+				for(std::size_t i=0; i < network.size(); i++) {
+					if(lastDeltas[i].size()!=network[i].size()) {
+						lastDeltas[i].resize(network[i].size());
+
+						for(std::size_t j = 0; j < lastDeltas[i].size(); j++) {
+							lastDeltas[i][j] = 0.0;
+						}
+					}
+				}
+				deltas= lastDeltas;
 			}
 
 			virtual void updateWeights(const std::vector<float> &input);
 
-			virtual void computeDeltas(const std::vector<float> &expectation);
+			virtual void computeSlopes(const std::vector<float> &expectation);
 
 			FeedForward::Network &network;
 
@@ -53,7 +83,13 @@ namespace Learning {
 
 			float learningCoefficient;
 
+			float momentumWeight = 0.0;
+
+			float weightDecay = 0.0;
+
 			std::vector<std::vector<float>> slopes;
+			std::vector<std::vector<float>> deltas;
+			std::vector<std::vector<float>> lastDeltas;
 	};
 }
 }

include/NeuralNetwork/Learning/QuickPropagation.h

@@ -36,36 +36,24 @@ namespace NeuralNetwork {
 						slopes[i].resize(network[i].size());
 				}
 
-				if(previousSlopes.size()!=network.size())
-					previousSlopes.resize(network.size());
+				if(deltas.size()!=network.size())
+					deltas.resize(network.size());
 
 				for(std::size_t i=0; i < network.size(); i++) {
-					if(previousSlopes[i].size()!=network[i].size())
-						previousSlopes[i].resize(network[i].size());
+					if(deltas[i].size()!=network[i].size())
+						deltas[i].resize(network[i].size());
 
 					for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
-						previousSlopes[i][j]=1.0;
+						deltas[i][j]=1.0;
 					}
 				}
-
-				if(lastWeightChange.size()!=network.size())
-					lastWeightChange.resize(network.size());
-
-				for(std::size_t i=0; i < network.size(); i++) {
-					if(lastWeightChange[i].size()!=network[i].size())
-						lastWeightChange[i].resize(network[i].size());
-
-					for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
-						lastWeightChange[i][j]=1.0;
-					}
-				}
-				weightChange= lastWeightChange;
+				weightChange= deltas;
 			}
 
 			virtual void updateWeights(const std::vector<float> &input) override;
 
 			std::vector<std::vector<float>> previousSlopes ={};
-			std::vector<std::vector<float>> lastWeightChange ={};
+			std::vector<std::vector<float>> deltas ={};
 			std::vector<std::vector<float>> weightChange ={};
 		};
 	}