backpropagation algorithm in matlab - machine-learning

Question

I'm writing a back propagation algorithm in matlab. But I can not get to write a good solution. I read a book Haykin and read some topics in Internet, how make it other people. I understand from door to door this algorithm in theory, but I have a much of error in practice. I have a NaN in my code.
You can see here.
I'm trying classification some points on plate. These are three ellipses, which are placed one inside the other.
I wrote this function. The second layer learn, but first layer dont learn.
function [E, W_1, W_2, B_1, B_2, X_3] = update(W_1, W_2, B_1, B_2, X_1, T, alpha)
V_1 = W_1 * X_1 + B_1;
X_2 = tansig(V_1);
V_2 = W_2 * X_2 + B_2;
X_3 = tansig(V_2);
E = 1 / 2 * sum((T - X_3) .^ 2);
dE = (T - X_3);
for j = 1 : size(X_2, 1)
delta_2_sum = 0;
for i = 1 : size(X_3, 1)
delta_2 = dE(i, 1) * dtansig(1, V_2(i, 1) );
W_2_tmp(i, j) = W_2(i, j) - alpha * delta_2 * X_2(j, 1);
B_2_tmp(i, 1) = B_2(i, 1) - alpha * delta_2;
end;
end;
for k = 1 : size(X_1, 1)
for j = 1 : size(X_2, 1)
delta_2_sum = 0;
for i = 1 : size(X_3, 1)
delta_2 = dE(i, 1) * dtansig(1, V_2(i, 1) );
delta_2_sum = delta_2_sum + W_2(i, j) * delta_2;
end;
delta_1 = delta_2_sum * dtansig(1, V_1(j, 1) );
W_1_tmp(j, k) = W_1(j, k) - alpha * delta_1 * X_1(k, 1);
B_1_tmp(j, 1) = B_1(j, 1) - alpha * delta_1;
end;
end;
if (min(W_1) < -10000 )
X = 1;
end;
B_1 = B_1_tmp;
B_2 = B_2_tmp;
W_1 = W_1_tmp
W_2 = W_2_tmp;
end
I wrote another variant code. And this code don't work. I calculated this code with 1-dimensional vector as input and as output. And I don't have truth result.
What can I do?
I use matlab nntool interface. But my backprop was written my hand.
How I can testing my code?
function [net] = backProp(net, epoch, alpha)
for u = 1 : epoch % Число эпох
for p = 1 : size(net.userdata{1, 1}, 2)
% Учим по всем элементам выборки
[~, ~, ~, De, Df, f] = frontProp(net, p, 1);
for l = size(net.LW, 1) : -1 : 1 % Обходим слои
if (size(net.LW, 1) == l )
delta{l} = De .* Df{l};
else
% size(delta{l + 1})
% size(net.LW{l + 1})
delta{l} = Df{l} .* (delta{l + 1}' * net.LW{l + 1} )';
end;
if (l == 1)
net.IW{l} + alpha * delta{l} * f{l}'
net.IW{l} = net.IW{l} + alpha * delta{l} * f{l}';
else
net.LW{l} + alpha * delta{l} * f{l}'
net.LW{l} = net.LW{l} + alpha * delta{l} * f{l}';
end;
end;
end;
end;
end