Neural Network - Plotting the results

Hello everybody,

below you can find a screenshot from the tensorflow playground. My question is: how can I plot the results in matlab like in the picture?

Thanks in advance

[KSSV]

Share your data. What you have.

[KSSV]

That can be plotted using meshgrid, inteprolations etc.

[Benedikt Wessel]

This is my plot and this my code:

clear
clc

%% Input
x1=0.1.*rand(20,2)+0.7;
x2=0.1.*rand(20,2);
x31=0.1.*rand(20,1);
x32=0.1.*rand(20,1)+0.7;
x3=[x31,x32];
x41=0.1.*rand(20,1)+0.7;
x42=0.1.*rand(20,1);
x4=[x41,x42];
x5=0.2*rand(20,2)+0.3;
x0=-ones(100,1); % negative bias for every Input
in=[x1;x2;x3;x4;x5];
in=[x0,in]'; % complete Inputmatrix

clear x1
clear x2
clear x0

o1=ones(20,1);
o2=zeros(20,1);
output=[o1;o1;o2;o2;o2]'; % correct Output

clear o2
clear o1

weights=2*rand(3,3)-1; % randomn values for the weight matrix
iterations=5000;
coeff=0.3;
err=zeros(iterations,1);

%% Training
for i=1:iterations
    
    out=zeros(100,1);
    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
    
    err(i)=e;
end

%% Plot
plotpv(in(2:3,:),output)
hold on

x=(0:0.1:1);
a=weights(2,1)/weights(3,1);
b=weights(1,1)/weights(3,1);
y=-a*x+b; % aus den Gewichten eine Funktion ableiten
plot(x,y) % diese Funktion grafisch darstellen

a=weights(2,2)/weights(3,2);
b=weights(1,2)/weights(3,2);
y=-a*x+b; % aus den Gewichten eine Funktion ableiten
plot(x,y) % diese Funktion grafisch darstellen

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

It´s my first attempt. I want to add a few layers and neurons later to get results for more complex data.