custom regression(multiple output)

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jaehong kim 2021-2-12

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https://ww2.mathworks.cn/matlabcentral/answers/743277-custom-regression-multiple-output

编辑： 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

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.