custom regression (multiple output)

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jaehong kim
jaehong kim 2021-2-12
Hi, I am working on a custom regression neural network.
Inputs size=2 and Output size=6 // Number of Data =25001
However, after a certain iteration, it was confirmed that all Data(25001) outputs are the same.
x axis=Target // y axis=output
Initially, the output is different, but it seems that the output is the same after a while.
My code is here.
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clear,clc,close all
Data=readmatrix('sim_linear.xlsx');
Y_at=Data(:,2);
Y_ft=Data(:,3);
F_at=Data(:,4);
F_ft=Data(:,5);
P_cot=Data(:,6);
T_cot=Data(:,7);
T_bt=Data(:,8);
F_et=Data(:,9);
T_et=Data(:,10);
PW_t=Data(:,11);
idx=randperm(numel(Y_at));
Y_at=Y_at(idx);
Y_ft=Y_ft(idx);
F_at=F_at(idx);
F_ft=F_ft(idx);
P_cot=P_cot(idx);
T_cot=T_cot(idx);
T_bt=T_bt(idx);
F_et=F_et(idx);
T_et=T_et(idx);
PW_t=PW_t(idx);
Input=cat(2,Y_at,Y_ft);
Output=cat(2,F_ft,T_cot,T_bt,F_et,T_et,PW_t);
Inputs=transpose(Input);
Outputs=transpose(Output);
layers = [
featureInputLayer(2,'Name','in')
fullyConnectedLayer(64,'Name','fc1')
tanhLayer('Name','tanh1')
fullyConnectedLayer(32,'Name','fc2')
tanhLayer('Name','tanh2')
fullyConnectedLayer(16,'Name','fc3')
tanhLayer('Name','tanh3')
fullyConnectedLayer(8,'Name','fc4')
tanhLayer('Name','tanh4')
fullyConnectedLayer(6,'Name','fc5')
];
lgraph=layerGraph(layers);
dlnet=dlnetwork(lgraph);
iteration = 1;
averageGrad = [];
averageSqGrad = [];
learnRate = 0.005;
gradDecay = 0.75;
sqGradDecay = 0.95;
output=[];
dlX = dlarray(Inputs,'CB');
for it=1:500
iteration = iteration + 1;
[out,loss,NNgrad]=dlfeval(@gradients,dlnet,dlX,Outputs);
[dlnet.Learnables,averageGrad,averageSqGrad] = adamupdate(dlnet.Learnables,NNgrad,averageGrad,averageSqGrad,iteration,learnRate,gradDecay,sqGradDecay);
if mod(it,100)==0
disp(it);
end
end
function [out,loss,NNgrad,grad1,grad2]=gradients(dlnet,dlx,t)
out=forward(dlnet,dlx);
loss2=sum((out(1,:)-t(1,:)).^2)+sum((out(2,:)-t(2,:)).^2)+sum((out(3,:)-t(3,:)).^2)+sum((out(4,:)-t(4,:)).^2)+sum((out(5,:)-t(5,:)).^2)+sum((out(6,:)-t(6,:)).^2);
loss=loss2;
[NNgrad]=dlgradient(loss,dlnet.Learnables);
end
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Thanks for reading my question. I hope that a great person can answer.

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