Do Multiple Output Neural Networks share the same weights and biases?

Question

Andrea Parizzi 2019-1-25

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/441558-do-multiple-output-neural-networks-share-the-same-weights-and-biases

评论： Andrea Parizzi 2019-2-3

Hello, I think it may be a stupid question but training a multiple output NN in matlab shares the weights and biases for all of them or creates internally one NN for each Output?

I mean training one NN for each component of the ouptut (like netx and nety below) is the same as training only one NN for both the components (like netb)?

Context: total_force is a vector of 20000 samples x 2 directions,

total_emg is a vector of 8 features x 20000 samples

...
netx = fitnet(i);    %MLN for x (the first component of total_force)
nety = fitnet(i);    %MLN for y
netb = fitnet(i);    %MLN for both x and y (i.e. the whole vector total_force)
[netx xtr] = train(netx,total_emg,total_force(:,1)');     
% I set the same division train/test/val for the other 2 MLN
nety.divideFcn = 'divideind';
netb.divideFcn = 'divideind';
nety.divideParam.trainInd = xtr.trainInd;
netb.divideParam.trainInd = xtr.trainInd;
nety.divideParam.valInd = xtr.valInd;
netb.divideParam.valInd = xtr.valInd;
nety.divideParam.testInd = xtr.testInd;
netb.divideParam.testInd = xtr.testInd;
[nety ytr] = train(nety,total_emg,total_force(:,2)');
[netb btr] = train(netb,total_emg,total_force');
...

running the code beow gives different performances, so I think that the single net for both x and y is trained sharing biases and weights(and is the one that usually performs worst), can someone tell me if I'm right?

x = netx(total_emg(:, xtr.testInd));
y = nety(total_emg(:, xtr.testInd));   
x_y = netb(total_emg(:, xtr.testInd)); 
% R
t_1 = corrcoef(total_force(xtr.testInd,:), x_y');
t_2 = corrcoef(total_force(xtr.testInd,:), [x', y']);
% MSE
MSE_1net= immse(total_force(xtr.testInd,:), x_y');
MSE_2net = immse(total_force(xtr.testInd,:), [x', y']);        