new doc and optical backprop

CMakeLists.txt

@@ -79,4 +79,16 @@ set_property(TEST recurrent PROPERTY LABELS unit)
 add_test(feedforward_perf tests/feedforward_perf)
 set_property(TEST feedforward_perf PROPERTY LABELS perf)
 
+add_test(optical_backpropagation tests/optical_backpropagation)
+set_property(TEST optical_backpropagation PROPERTY LABELS unit)
+
+
+
+
+add_test(backpropagation_perf tests/backpropagation_perf)
+set_property(TEST backpropagation_perf PROPERTY LABELS perf)
+
+add_test(recurrent_perf tests/recurrent_perf)
+set_property(TEST recurrent_perf PROPERTY LABELS perf)
+
 ENDIF(ENABLE_TESTS)

README.md

@@ -2,6 +2,18 @@ Artifitial Neural Network Library
 =================================
 
 how to build:
+
 mkdir build
+
 cmake ..
+
 make
+
+Perfomace
+---------
+
+i5-5300U & 8GB ram
+
+| date       | feedforward_perf | recurrent_perf | backpropagation_perf |
+------------ | ---------------- | -------------- | -------------------- |
+| 2016/02/07 | 8.27 sec         | 7.15 sec       | 6.00 sec             |

include/NeuralNetwork/Learning/BackPropagation.h

@@ -24,7 +24,7 @@ namespace Learning {
 			inline virtual void setLearningCoefficient (const float& coefficient) { learningCoefficient=coefficient; }
 
 		protected:
-			inline virtual float correction(const float & expected, const float &computed) {
+			inline virtual float correction(const float & expected, const float &computed) const {
 				return expected-computed;
 			};
 			float learningCoefficient;

include/NeuralNetwork/Learning/OpticalBackPropagation.h

@@ -0,0 +1,28 @@
+#pragma once
+
+#include "./BackPropagation.h"
+
+namespace NeuralNetwork {
+namespace Learning {
+
+	/** @class OpticalBackPropagation
+	 * @brief 
+	 */
+	class OpticalBackPropagation : public BackPropagation {
+
+		public:
+			OpticalBackPropagation(): BackPropagation() {
+
+			}
+			virtual ~OpticalBackPropagation() {
+			}
+
+		protected:
+			inline virtual float correction(const float & expected, const float &computed) const override {
+				register float tmp=(expected-computed);
+				register float ret=1+exp(tmp*tmp);
+				return tmp < 0? -ret:ret;
+			};
+	};
+}
+}

mainpage.dox

@@ -5,5 +5,4 @@
 
 @author Tomas Cernik (Tom.Cernik@gmail.com)
 
-
 */

tests/CMakeLists.txt

@@ -10,11 +10,20 @@ target_link_libraries(basis NeuralNetwork)
 add_executable(backpropagation backpropagation.cpp)
 target_link_libraries(backpropagation NeuralNetwork)
 
+add_executable(backpropagation_perf backpropagation_perf.cpp)
+target_link_libraries(backpropagation_perf NeuralNetwork)
+
 add_executable(feedforward feedforward.cpp)
 target_link_libraries(feedforward NeuralNetwork)
 
 add_executable(feedforward_perf feedforward_perf.cpp)
 target_link_libraries(feedforward_perf NeuralNetwork)
 
+add_executable(optical_backpropagation optical_backpropagation.cpp)
+target_link_libraries(optical_backpropagation NeuralNetwork)
+
 add_executable(recurrent recurrent.cpp)
 target_link_libraries(recurrent NeuralNetwork)
+
+add_executable(recurrent_perf recurrent_perf.cpp)
+target_link_libraries(recurrent_perf NeuralNetwork)

tests/backpropagation_perf.cpp

@@ -0,0 +1,26 @@
+#include <NeuralNetwork/FeedForward/Network.h>
+
+#include <cassert>
+#include <iostream>
+#include "../include/NeuralNetwork/Learning/BackPropagation.h"
+
+int main() {
+	{	// XOR problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(200,a);
+		n.appendLayer(500,a);
+		n.appendLayer(900,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::BackPropagation prop;
+		for(int i=0;i<100;i++) {
+			prop.teach(n,{1,0},{1});
+			prop.teach(n,{1,1},{0});
+			prop.teach(n,{0,0},{0});
+			prop.teach(n,{0,1},{1});
+		}
+	}
+}

tests/feedforward_perf.cpp

@@ -7,8 +7,8 @@ int main() {
 	{	// XOR problem
 		NeuralNetwork::FeedForward::Network n(2);
 		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
-		n.appendLayer(2000,a);
-		n.appendLayer(2000,a);
+		n.appendLayer(5000,a);
+		n.appendLayer(5000,a);
 		n.appendLayer(1,a);
 
 		for(int i=0;i<500;i++) {

tests/optical_backpropagation.cpp

@@ -0,0 +1,116 @@
+#include <NeuralNetwork/FeedForward/Network.h>
+
+#include <cassert>
+#include <iostream>
+#include "../include/NeuralNetwork/Learning/OpticalBackPropagation.h"
+
+int main() {
+	{	// XOR problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(2,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::OpticalBackPropagation prop;
+		for(int i=0;i<10000;i++) {
+			prop.teach(n,{1,0},{1});
+			prop.teach(n,{1,1},{0});
+			prop.teach(n,{0,0},{0});
+			prop.teach(n,{0,1},{1});
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,1});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,1});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,0});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,0});
+			assert(ret[0] < 0.1);
+		}
+	}
+	{	// AND problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(2,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::OpticalBackPropagation prop;
+		for(int i=0;i<10000;i++) {
+			prop.teach(n,{1,1},{1});
+			prop.teach(n,{0,0},{0});
+			prop.teach(n,{0,1},{0});
+			prop.teach(n,{1,0},{0});
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,1});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,1});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,0});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,0});
+			assert(ret[0] < 0.1);
+		}
+	}
+	{	// NOT AND problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(2,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::OpticalBackPropagation prop;
+		for(int i=0;i<10000;i++) {
+			prop.teach(n,{1,1},{0});
+			prop.teach(n,{0,0},{1});
+			prop.teach(n,{0,1},{1});
+			prop.teach(n,{1,0},{1});
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,1});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,1});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,0});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,0});
+			assert(ret[0] > 0.9);
+		}
+	}
+}

tests/recurrent_perf.cpp

@@ -0,0 +1,12 @@
+#include <NeuralNetwork/Recurrent/Network.h>
+
+#include <assert.h>
+#include <iostream>
+
+int main() {
+	NeuralNetwork::Recurrent::Network a(2,1,1000);
+
+	for(size_t i=0;i<10000;i++) {
+		 a.computeOutput({1,0.7});
+	}
+}