Sigmoid and it's dearativate - neural-network

Question

public double Sigmoid(double x)
{
return 2 / (1 + Math.Exp(-2 * x)) - 1;
}
public double Derivative(double x)
{
double s = Sigmoid(x) - (Sigmoid(x)* Sigmoid(x));
return s;
}
When i train the network it is giving output:
0,0 = 0 it is always 0 //I dont know
0,1 = 0,67 and it is going up //good but after 1000 repets it gets to
0.20 and it is goind down
1,0 = 0.50 and it is going up //good but after 1000 repets it gets to
0.20 and it is goind down
1,1 = 0.80 and it is going up //wrong it should go down.
Where is the mistake?
Neural network (XOR and back propagation)
int pw = Convert.ToInt32(textBox1.Text);
for (int i12 = 0; i12 < pw; i12++)
{
//i1 = Convert.ToDouble(textBox2.Text);
// i2 = Convert.ToDouble(textBox3.Text);
// desired = Convert.ToDouble(textBox1.Text);
for (int i = 0; i < 4; i++)
{
if (i == 0)
{
i1 = 1;
i2 = 1;
desired = 0;
}
else if (i == 1)
{
i1 = 1;
i2 = 0;
desired = 1;
}
else if (i == 2)
{
i1 = 0;
i2 = 1;
desired = 1;
}
else if (i == 3)
{
i1 = 0;
i2 = 0;
desired = 0;
}
// double[] questions = new double[2];
// questions[0] = 1;
// questions[1] = 0;
// Random rnd = new Random();
// double s = questions[rnd.Next(0, 2)];
// double s1 = questions[rnd.Next(0, 2)];
// i1 = s;
// i2 = s1;
//hidden layer hidden values
h1 = i1 * w1 + i2 * w2; //i1*w1+i2*w2
h2 = i1 * w3 + i2 * w4;//i1*w3+i2*w4
h3 = i1 * w5 + i2 * w6;//i1*w5+i2*w6;
//hidden layer hidden values
//VALUE OF HIDDEN LAYER
h1v = Sigmoid(h1);
h2v = Sigmoid(h2);
h3v = Sigmoid(h3);
//VALUE OF HIDDEN LAYER
//output final
output = h1v * w7 + h2v * w8 + h3v * w9;
outputS = Sigmoid(output);
//output final
//BACKPROPAGATION
//MARGIN ERROR
Error = desired - outputS; //desired-cena jaka ma byc OutputS-zgadnienta cena
//Margin Error
//DElta output sum
deltaoutputsum = Derivative(output) * Error * 0.05; //output bez sigmoida i error
//Delta output sum
//weight of w7,w8,w9.
w7b = w7; //0.3
w8b = w8; // 0.5
w9b = w9;// 0.9
w7 = w7 + deltaoutputsum * h1v; //waga w7
w8 = w8 + deltaoutputsum * h2v; //waga w8
w9 = w9 + deltaoutputsum * h3v; //waga w9
//weights of w7,w8,w9.
//DELTA HIDDEN SUm
h1 = deltaoutputsum * w7b * Derivative(h1);
h2 = deltaoutputsum * w8b * Derivative(h2);
h3 = deltaoutputsum * w9b * Derivative(h3);
//DELTA HIDDEN SUM
//weights 1,2,3,4,5,6
w1 = w1 - h1 * i1;
w2 = w2 - h1 * i2;
w3 = w3 - h2 * i1;
w4 = w4 - h2 * i2;
w5 = w5 - h3 * i1;
w6 = w6 - h3 * i2;
Why after training it give:
1.0 == close to 0, should be close to 1
1.1 == close to 1,should be 0
0.0 == it is good, close to 0
0.1 == close to 0,should be close to 1
This is the code to use after training(i1 and i1 are inputs 1 or 0 )
i1 = Convert.ToDouble(textBox4.Text);
i2 = Convert.ToDouble(textBox5.Text);
//hidden layer hidden values
h1 = i1 * w1 + i2 * w2; //i1*w1+i2*w2
h2 = i1 * w3 + i2 * w4;//i1*w3+i2*w4
h3 = i1 * w5 + i2 * w6;//i1*w5+i2*w6;
//hidden layer hidden values
//VALUE OF HIDDEN LAYER
h1v = Sigmoid(h1);
h2v = Sigmoid(h2);
h3v = Sigmoid(h3);
//VALUE OF HIDDEN LAYER
//output final
output = h1v * w7 + h2v * w8 + h3v * w9;
outputS = Sigmoid(output);
MessageBox.Show(outputS.ToString());
w1-w10 are weights. h1v are valuse of hidden layers. h1 are weights of hidden layers