Neural Network - Plotting the results
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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
3 个评论
KSSV
2020-6-9
Share your data. What you have.
KSSV
2020-6-9
That can be plotted using meshgrid, inteprolations etc.
Benedikt Wessel
2020-6-9
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.
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