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modified BP interface

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This commit is contained in:
Shin
2016-02-24 19:05:26 +01:00
parent 47de0fa08b
commit c45f12f53c
10 changed files with 195 additions and 13 deletions
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3
CMakeLists.txt
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@@ -94,6 +94,9 @@ set_property(TEST activation PROPERTY LABELS unit)
add_test(backpropagation tests/backpropagation)
set_property(TEST backpropagation PROPERTY LABELS unit)
add_test(backpropagation_function_cmp tests/backpropagation_function_cmp)
set_property(TEST backpropagation_function_cmp PROPERTY LABELS unit)
add_test(basis tests/basis)
set_property(TEST basis PROPERTY LABELS unit)

14
include/NeuralNetwork/Learning/BackPropagation.h
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@@ -2,7 +2,9 @@
#include <vector>
#include <cmath>
#include <NeuralNetwork/FeedForward/Network.h>
#include "CorrectionFunction/Linear.h"
namespace NeuralNetwork {
namespace Learning {
@@ -13,21 +15,23 @@ namespace Learning {
class BackPropagation {
public:
inline BackPropagation(FeedForward::Network &feedForwardNetwork): network(feedForwardNetwork), learningCoefficient(0.4), deltas() {
inline BackPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
network(feedForwardNetwork), correctionFunction(correction),learningCoefficient(0.4), deltas() {
resize();
}
virtual ~BackPropagation() {
delete correctionFunction;
}
BackPropagation(const BackPropagation&)=delete;
BackPropagation& operator=(const NeuralNetwork::Learning::BackPropagation&) = delete;
void teach(const std::vector<float> &input, const std::vector<float> &output);
inline virtual void setLearningCoefficient (const float& coefficient) { learningCoefficient=coefficient; }
protected:
inline virtual float correction(const float & expected, const float &computed) const {
return expected-computed;
};
inline void resize() {
if(deltas.size()!=network.size())
@@ -41,6 +45,8 @@ namespace Learning {
FeedForward::Network &network;
CorrectionFunction::CorrectionFunction *correctionFunction;
float learningCoefficient;
std::vector<std::vector<float>> deltas;

29
include/NeuralNetwork/Learning/CorrectionFunction/ArcTangent.h Normal file
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@@ -0,0 +1,29 @@
#pragma once
#include "CorrectionFunction.h"
#include <iostream>
namespace NeuralNetwork {
namespace Learning {
namespace CorrectionFunction {
class ArcTangent : public CorrectionFunction {
public:
ArcTangent (const float &c=1.0): coefficient(c) {
}
/**
* @brief operator returns error for values
*
*/
inline virtual float operator()(const float &expected, const float &computed) const override final {
//std::cout << (expected-computed) << ":" << atan(expected-computed) << "\n";
return atan(coefficient*(expected-computed));
}
private:
const float coefficient;
};
}
}
}

19
include/NeuralNetwork/Learning/CorrectionFunction/CorrectionFunction.h Normal file
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@@ -0,0 +1,19 @@
#pragma once
namespace NeuralNetwork {
namespace Learning {
namespace CorrectionFunction {
class CorrectionFunction {
public:
virtual ~ CorrectionFunction() {
}
/**
* @brief operator returns error for values
*
*/
virtual float operator()(const float & expected, const float &computed) const = 0;
};
}
}
}

20
include/NeuralNetwork/Learning/CorrectionFunction/Linear.h Normal file
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@@ -0,0 +1,20 @@
#pragma once
#include "CorrectionFunction.h"
namespace NeuralNetwork {
namespace Learning {
namespace CorrectionFunction {
class Linear : public CorrectionFunction {
public:
/**
* @brief operator returns error for values
*
*/
inline virtual float operator()(const float &expected, const float &computed) const override final {
return expected-computed;
}
};
}
}
}

22
include/NeuralNetwork/Learning/CorrectionFunction/Optical.h Normal file
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@@ -0,0 +1,22 @@
#pragma once
#include "CorrectionFunction.h"
namespace NeuralNetwork {
namespace Learning {
namespace CorrectionFunction {
class Optical : public CorrectionFunction {
public:
/**
* @brief operator returns error for values
*
*/
inline virtual float operator()(const float &expected, const float &computed) const override final {
register float tmp=(expected-computed);
register float ret=1+exp(tmp*tmp);
return tmp < 0? -ret:ret;
}
};
}
}
}

11
include/NeuralNetwork/Learning/OpticalBackPropagation.h
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@@ -1,6 +1,7 @@
#pragma once
#include "./BackPropagation.h"
#include "./CorrectionFunction/Optical.h"
namespace NeuralNetwork {
namespace Learning {
@@ -11,18 +12,12 @@ namespace Learning {
class OpticalBackPropagation : public BackPropagation {
public:
OpticalBackPropagation(FeedForward::Network &feedForwardNetwork): BackPropagation(feedForwardNetwork) {
OpticalBackPropagation(FeedForward::Network &feedForwardNetwork): BackPropagation(feedForwardNetwork,new CorrectionFunction::Optical()) {
}
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;
};
};
}
}

2
src/NeuralNetwork/Learning/BackPropagation.cpp
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@@ -12,7 +12,7 @@ void NeuralNetwork::Learning::BackPropagation::teach(const std::vector<float> &i
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())*
deltas[network.size()-1][j]=correctionFunction->operator()( expectation[j-1], neuron.output())*
neuron.getActivationFunction().derivatedOutput(neuron.value(),neuron.output());
}

3
tests/CMakeLists.txt
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@@ -10,6 +10,9 @@ target_link_libraries(basis NeuralNetwork)
add_executable(backpropagation backpropagation.cpp)
target_link_libraries(backpropagation NeuralNetwork)
add_executable(backpropagation_function_cmp backpropagation_function_cmp.cpp)
target_link_libraries(backpropagation_function_cmp NeuralNetwork)
add_executable(backpropagation_perf backpropagation_perf.cpp)
target_link_libraries(backpropagation_perf NeuralNetwork)

85
tests/backpropagation_function_cmp.cpp Normal file
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@@ -0,0 +1,85 @@
#include <NeuralNetwork/FeedForward/Network.h>
#include <cassert>
#include <iostream>
#include "../include/NeuralNetwork/Learning/BackPropagation.h"
#include "../include/NeuralNetwork/Learning/CorrectionFunction/Optical.h"
#include "../include/NeuralNetwork/Learning/CorrectionFunction/ArcTangent.h"
#define LEARN(A,AR,B,BR,C,CR,D,DR,FUN,COEF) \
({\
srand(rand);\
NeuralNetwork::FeedForward::Network n(2);\
NeuralNetwork::ActivationFunction::Sigmoid a(-1);\
n.appendLayer(2,a);\
n.appendLayer(1,a);\
n.randomizeWeights();\
NeuralNetwork::Learning::BackPropagation prop(n,FUN);\
prop.setLearningCoefficient(COEF);\
int error=1; int steps = 0; \
while(error > 0 && steps <99999) {\
steps++;\
error=0;\
prop.teach(A,{AR});\
prop.teach(B,{BR});\
prop.teach(C,{CR});\
prop.teach(D,{DR});\
error+=fabs(n.computeOutput(A)[0]-AR) > 0.1 ? 1:0;\
error+=fabs(n.computeOutput(B)[0]-BR) > 0.1 ? 1:0;\
error+=fabs(n.computeOutput(C)[0]-CR) > 0.1 ? 1:0;\
error+=fabs(n.computeOutput(D)[0]-DR) > 0.1 ? 1:0;\
}\
steps;\
})
int main() {
long rand=(time(NULL));
const float linearCoef=0.7;
const float opticalCoef=0.11;
const float arcTangentCoef=0.6;
const float arcTangent=1.5;
{
std::cout << "XOR:\n";
std::cout << "\tLinear: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),0,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::Linear,linearCoef) << "\n";
std::cout << "\tOptical: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),0,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::Optical,opticalCoef) << "\n";
std::cout << "\tArcTangent: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),0,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::ArcTangent(arcTangent),arcTangentCoef) << "\n";
}
{
std::cout << "AND:\n";
std::cout << "\tLinear: " <<
LEARN(std::vector<float>({1,0}),0,std::vector<float>({1,1}),1,std::vector<float>({0,0}),0,std::vector<float>({0,1}),0,
new NeuralNetwork::Learning::CorrectionFunction::Linear,linearCoef) << "\n";
std::cout << "\tOptical: " <<
LEARN(std::vector<float>({1,0}),0,std::vector<float>({1,1}),1,std::vector<float>({0,0}),0,std::vector<float>({0,1}),0,
new NeuralNetwork::Learning::CorrectionFunction::Optical,opticalCoef) << "\n";
std::cout << "\tArcTangent: " <<
LEARN(std::vector<float>({1,0}),0,std::vector<float>({1,1}),1,std::vector<float>({0,0}),0,std::vector<float>({0,1}),0,
new NeuralNetwork::Learning::CorrectionFunction::ArcTangent(arcTangent),arcTangentCoef) << "\n";
}
{
std::cout << "AND:\n";
std::cout << "\tLinear: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),1,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::Linear,linearCoef) << "\n";
std::cout << "\tOptical: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),1,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::Optical,opticalCoef) << "\n";
std::cout << "\tArcTangent: " <<
LEARN(std::vector<float>({1,0}),1,std::vector<float>({1,1}),0,std::vector<float>({0,0}),1,std::vector<float>({0,1}),1,
new NeuralNetwork::Learning::CorrectionFunction::ArcTangent(arcTangent),arcTangentCoef) << "\n";
}
}