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BackPropagation works!

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Shin
2014-11-11 11:12:04 +01:00
parent 75ca9bc21f
commit 9ef4274396
29 changed files with 519 additions and 170 deletions
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80
tests/nn-01.cpp
View File

@@ -1,47 +1,67 @@
#include "../src/NeuronNetwork/FeedForward"
#include "../src/NeuronNetwork/FeedForwardQuick"
#include "../src/NeuronNetwork/Learning/BackPropagation"
#include <iostream>
#include <vector>
class X: public Shin::NeuronNetwork::Problem
{
protected:
public:
X(const X& a) :q(a.q) {}
X(const std::vector<bool> &a):q(a) {}
std::vector<bool> representation() const
{
return std::vector<bool>({1,1});
return q;
}
protected:
std::vector<bool> q;
};
int main()
{
Shin::NeuronNetwork::FeedForwardNetwork n({2,3,2});
Shin::NeuronNetwork::Solution s =n.solve(X());
Shin::NeuronNetwork::FeedForwardNetworkQuick q({2,3,2});
Shin::NeuronNetwork::Solution sq =q.solve(X());
if(s.size()!=2)
srand(time(NULL));
std::vector<Shin::NeuronNetwork::Solution> s;
std::vector<X> p;
//
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({1})));
p.push_back(X(std::vector<bool>({0})));
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({0})));
p.push_back(X(std::vector<bool>({1})));
Shin::NeuronNetwork::FeedForwardNetworkQuick q({1,1});
Shin::NeuronNetwork::Learning::BackPropagation b(q);
int i=0;
std::cerr << i%4 <<". FOR: [" << p[i%2].representation()[0] << "] res: " << q.solve(p[i%2])[0] << " should be " << s[i%2][0]<<"\n";
for(int i=0;i<2000;i++)
{
std::cout << "1";
return 1;
}
if(s[0]!=1)
{
std::cout << "2";
return 1;
}
if(s[1]!=1)
{
std::cout << "3";
return 1;
}
if(s.size()!=sq.size())
{
std::cout << "3";
return 1;
b.teach(p[i%2],s[i%2]);
std::cerr << i%2 <<". FOR: [" << p[i%2].representation()[0] << "] res: " << q.solve(p[i%2])[0] << " should be " << s[i%2][0]<<"\n";
}
b.debugOn();
for(int i=0;i<2;i++)
if(s[i]!=sq[i])
{
std::cout << "4 " << i;
return 1;
}
return 0;
{
b.teach(p[i%2],s[i%2]);
std::cerr << i%4 <<". FOR: [" << p[i%4].representation()[0] << "," <<p[i%4].representation()[0] << "] res: " << q.solve(p[i%4])[0] << " should be " <<
s[i%4][0]<<"\n";
}
b.debugOff();
/*
for(int i=0;i<40;i++)
{
b.teach(p[i%4],s[i%4]);
}
b.debugOn();
std::cerr << "LEARNED\n";
for(int i=0;i<4;i++)
{
b.teach(p[i%4],s[i%4]);
std::cerr << i%4 <<". FOR: [" << p[i%4].representation()[0] << "," <<p[i%4].representation()[1] << "] res: " << q.solve(p[i%4])[0] << " should be " <<
s[i%4][0]<<"\n";
}
*/
}

11
tests/nn-02.cpp
View File

@@ -43,17 +43,6 @@ int main()
return 1;
}
if(s[0]!=0)
{
std::cout << "2";
return 1;
}
if(s[1]!=1)
{
std::cout << "3";
return 1;
}
for(int i=0;i<2;i++)
{
if(s[i]!=sq[i])

113
tests/nn-03.cpp
View File

@@ -1,74 +1,61 @@
#include "../src/NeuronNetwork/Network"
#include "../src/NeuronNetwork/FeedForward"
#include "../src/NeuronNetwork/FeedForwardQuick"
#include "../src/NeuronNetwork/Learning/BackPropagation"
#include <iostream>
#include <vector>
class X: public Shin::NeuronNetwork::Problem
{
public: X(bool x,bool y):x(x),y(y) {}
protected: std::vector<bool> representation() const { return std::vector<bool>({x,y}); }
private:
bool x;
bool y;
public:
X(const X& a) :q(a.q) {}
X(const std::vector<bool> &a):q(a) {}
std::vector<bool> representation() const
{
return q;
}
protected:
std::vector<bool> q;
};
int main()
{
srand(time(NULL));
int lm=5;
Shin::NeuronNetwork::FeedForwardNetwork net({2,lm,1});
bool x=1;
int prev_err=0;
int err=0;
int l;
int n;
int w;
int pot;
int wei;
int c=0;
std::cout << "\ntest 1 & 1 -" << net.solve(X(1,1))[0];
std::cout << "\ntest 1 & 0 -" << net.solve(X(1,0))[0];
std::cout << "\ntest 0 & 1 - " << net.solve(X(0,1))[0];
std::cout << "\ntest 0 & 0- " << net.solve(X(0,0))[0];
std::cout << "\n---------------------------------------";
do{
if(c%10000 ==1)
{
std::cout << "\nmixed";
srand(time(NULL));
}
err=0;
c++;
l=rand()%2+1;
n=rand()%lm;
w=rand()%2;
if(l==2)
n=0;
pot=net[l]->operator[](n)->getPotential();
net[l]->operator[](n)->setPotential(pot*(rand()%21+90)/100);
wei=net[l]->operator[](n)->getWeight(w);
net[l]->operator[](n)->setWeight(w,wei*(rand()%21+90)/100);
std::vector<Shin::NeuronNetwork::Solution> s;
std::vector<X> p;
for(int i=0;i<100;i++)
{
bool x= rand()%2;
bool y=rand()%2;
Shin::NeuronNetwork::Solution s =net.solve(X(x,y));
if(s[0]!= (x xor y))
err++;
}
//
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({0})));
p.push_back(X(std::vector<bool>({1,0})));
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({0})));
p.push_back(X(std::vector<bool>({0,1})));
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({0})));
p.push_back(X(std::vector<bool>({0,0})));
s.push_back(Shin::NeuronNetwork::Solution(std::vector<double>({1})));
p.push_back(X(std::vector<bool>({1,1})));
Shin::NeuronNetwork::FeedForwardNetworkQuick q({2,4,1});
Shin::NeuronNetwork::Learning::BackPropagation b(q);
b.debugOn();
for(int i=0;i<4;i++)
{
b.teach(p[i%4],s[i%4]);
std::cerr << i%4 <<". FOR: [" << p[i%4].representation()[0] << "," <<p[i%4].representation()[1] << "] res: " << q.solve(p[i%4])[0] << " should be " <<
s[i%4][0]<<"\n";
}
b.debugOff();
for(int i=0;i<40;i++)
{
b.teach(p[i%4],s[i%4]);
}
b.debugOn();
std::cerr << "LEARNED\n";
for(int i=0;i<4;i++)
{
b.teach(p[i%4],s[i%4]);
std::cerr << i%4 <<". FOR: [" << p[i%4].representation()[0] << "," <<p[i%4].representation()[1] << "] res: " << q.solve(p[i%4])[0] << " should be " <<
s[i%4][0]<<"\n";
}
if(err > prev_err)
{
net[l]->operator[](n)->setPotential(pot);
net[l]->operator[](n)->setWeight(w,wei);
};
// std::cout << "C: " << c << " err: " << err << " prev: "<<prev_err << "\n";
prev_err=err;
if(err <1)
x=0;
}while(x);
std::cout << "\ntest 1 & 1 -" << net.solve(X(1,1))[0];
std::cout << "\ntest 1 & 0 -" << net.solve(X(1,0))[0];
std::cout << "\ntest 0 & 1 - " << net.solve(X(0,1))[0];
std::cout << "\ntest 0 & 0- " << net.solve(X(0,0))[0];
std::cout << "\nTotaly: " << c << "\n";
}