BackPropagation works!

tests/nn-03.cpp

@@ -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";
 }