Step 6:  

W1=rand(4,2); %assign random weights to connections between input and hidden layers
W2=rand(1,4); %assign random weights to connections between hidden and output layers

for epoch=1:10000  %start training the network

        clc;  %these additional  lines will just allow you to see the progress of the training in the command windown when you run the program
        disp(['Epoch # ' num2str(epoch)]);
        [W1 W2] = BackpropXOR(W1,W2,X,D);
        W1 %print updated values of W1 to screen
        W2 %print updated values of W2 to screen

end; %for epoch

%*********************************************************************************
    %The network is now trained. From here, we're just checking to see if the network is
    %adequetly trained by giving it the input values to see what output  values we get
   
    N=4; %number of input trials (number of rows in X)
   
    for k=1:N
        x=X(k,:)';  %note the transpose symbol '
        v1=W1*x; %get the hidden layer node values
        y1=1./(1+exp(-v1)); %apply the activation function to get the output of the hidden layer
        v=W2*y1; %get the output layer node value
        y(k)=1./(1+exp(-v)); %get the final output of the network.
        %Note that y(k) saves the values of the network output for each k so we can print it below    
    end; %for k=1:N
   
    y'   %print values of the output node (y) to the screen for each of the four trials
   
    %you should end up with 4 numbers because N=4.  Compare these four
    %numbers to the values in the vector D which provides the correct answers.
    %They should be very similar if the network was trained properly.