Neural Network: How can I plot the classification Lines?

Hello erverybody,

I'm a beginner in matlab and neural networks. I tried to build a neural network for classification (at the end of the page you can find the architectur).

You can see the code below.

My question is: How can I plot the lines, seperating the data in the plot?

Additionally: If I change, for example, the learning rate (coeff) the output will be always around 0.5. You know the reason behind that? Why is it the mean value (0,1)?

It is highly appreciated if you find mistakes or improvements in/for my code.

% Example if I change the coeff
out:

0.459
0.567
0.563
0.478
...

% The code

clear
clc

%% Input
x1=0.1.*rand(10,2)+0.6;
x2=0.1.*rand(10,2);
x31=0.1.*rand(10,1);
x32=0.1.*rand(10,1)+0.6;
x3=[x31,x32];
x41=0.1.*rand(10,1)+0.6;
x42=0.1.*rand(10,1);
x4=[x41,x42];
x0=-ones(40,1); % negative bias for every Input
in=[x1;x2;x3;x4];
in=[x0,in]'; % complete Inputmatrix

clear x1
clear x2
clear x0

o1=ones(10,1);
o2=zeros(10,1);
output=[o1;o1;o2;o2]'; % correct Output

clear o2
clear o1

weights=2*rand(3,3)-1; % randomn values for the weight matrix
iterations=100000;
coeff=0.9;

%% Training
for i=1:iterations
    
    out=zeros(40,1);
    numIn=length(in);
    e=0;
    for j=1:numIn
        
        input=in(:,j);
        desired_out=output(:,j);
        
        % Forward-Propagation
        % Hidden_Layer
        H1=dot(input,weights(:,1)); % Hidden-Layer_Neuron1
        H2=dot(input,weights(:,2)); % Hidden-Layer_Neuron2
        HL(1)=sigmoid(H1); % Acitvation function (first Input for the Output_Layer)
        HL(2)=sigmoid(H2); % Activation function (second Input for the Output_Layer)
        % Output-Layer
        input_OL=[input(1);HL(1);HL(2)]; % Input vector for the Ouput-Layer (Bias and the output from the Hidden-Layer)
        O3=dot(input_OL,weights(:,3)); % Output-Layer
        OL=sigmoid(O3); % Output value  neuronal network
        out(j)=OL;
        
        
        % Backward-Propagation
        % Output-Layer
        Hout=[-1,HL]'; % Output  Hiddenlayer
        delta=desired_out-OL; % Deviation  Errorfunction MSE 0.5*(delta)^2
        dsigmoid_OL=OL*(1-OL); % Deviation  Sigmoid-Function (Activationfunction)
        % dO3/dw=H0, Deviation weighted sum regarding the weights 
        weights(:,3)=weights(:,3)+coeff*delta*dsigmoid_OL.*Hout;
        % Hidden-Layer
        dsigmoid_HL=Hout(2:3).*(1-Hout(2:3));
        
        % weigths(input->hidden)+coeff*der(err.fct.)*der(act.fct_Outputlayer)*Input Outputlayer*weigths(hidden->outputlayer)*der(act.fct._Hiddenlayer)*Input
        weights(:,1)=weights(:,1)+coeff*delta*dsigmoid_OL*HL(1)*weights(2,3)*dsigmoid_HL(1).*input; % Weights  1. Neuron in Hidden-Layer
        weights(:,2)=weights(:,2)+coeff*delta*dsigmoid_OL*HL(2)*weights(3,3)*dsigmoid_HL(2).*input; % Weights  2. Neuron in Hidden-Layer
        
        e=e+abs(delta);
    end
end

weights
out
e
%% Plot
plotpv(in(2:3,:),output)


%% Sigmoid
function s=sigmoid(x)
s=1/(1+exp(-x)); 
end

The adjusted weigths:

weights =

    1.8707    2.2326  -10.9875
   -9.5857    9.4768  -18.1220
    9.0824   -8.8898  -18.2335

The error:

    e =

    0.0546

This is the architecture of my neural network: