How can I check the neural network is correct?

Question

Bernardino del pino 2022-5-26

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1727495-how-can-i-check-the-neural-network-is-correct

回答： Lokesh 2023-10-10

I have some data (p is the input and t the output). The net works well but I want to ckeck it manually. When I compute it, it doesn't work. I explain it better in the code:

p=[4.21470484282808,4.61178342351945,4.92103025244236,5.16780470857077,5.36888616086475,5.53567693961333,5.67614306410908,5.79599161759301,5.89941107438887,5.98953800357575
 0.0841938476119945,0.0688748497618962,0.0506266219880575,0.0313984679864031,0.0122666193874076,-0.00620394471778739,-0.0237367071204724,-0.0402208466896825,-0.0556358572482061,-0.0700100771974078
 0.624790077024891,0.933828019025972,1.24834383939203,1.55431911928733,1.84468199477835,2.11642821064707,2.3688169191345,2.60230677876707,2.81795118085545,3.01705857857006
 0.00257893224675201,0.00368824779406364,0.00453083959629498,0.00489009192682788,0.0046033702524108,0.00357272498883625,0.00175843692227414,-0.000832738095460411,-0.00416162543741231,-0.00816576040624318
 ];
 t=[2.83606065756777,3.31180314379271,3.70377438426516,4.02978774606033,4.30397305613069,4.53713048911324,4.7374656190205,4.91123610026158,5.06326477923633,5.19730529195449
 0.0485161358312315,0.0461344489481127,0.0391584214613128,0.0290027475982773,0.016808673107218,0.00342380868886163,-0.0105496421711672,-0.0246959627873845,-0.0387354315195166,-0.0524841437666582
 0.930574382489153,1.30350896221403,1.66272401320841,1.99818564751866,2.30664368564685,2.58817825301552,2.84437665551212,3.07741070830855,3.28958545610536,3.48311553793172
 0.00580490407681004,0.00759806971982089,0.00862898201442169,0.00863448538934149,0.0074887953804546,0.00517928895558281,0.00176746424529477,-0.00263950757984306,-0.00791199822691147,-0.0139137399633852
 ];
n=5;
logsig=@(x) 1./(1+exp(-x));
net = fitnet(n,'trainlm');
net.layers{1}.transferFcn='logsig'
[net,tr]=train(net,p,t);
nntraintool
Y_pred=net(p)
error=abs(t-Y_pred)
plotregression(t,Y_pred)
W1 = net.IW{1}
W2 = net.LW{2}
b1 = net.b{1}
b2 = net.b{2}
a1=logsig(W1*p+b1)
a2=W2*a1+b2
% my doubt is: why a2 is not the same that Y_pred? What am I doind bad?

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Answer 1

Lokesh 2023-10-10

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https://ww2.mathworks.cn/matlabcentral/answers/1727495-how-can-i-check-the-neural-network-is-correct#answer_1329779

在 MATLAB Online 中打开

Hi Bernardino,

I understand that you want to compute the output of a neural network manually.

In your code, there seems to be a mismatch between the variable "a2" and "Y_pred" due to missing normalization parameters.

To compute the output of neural network manually, we should first determine whether the input data needs to be normalized using “mapminmax”. If normalization is required, we can proceed with the normalization process. Similarly, we should also check if the output needs to be reverse normalized using "mapminmax” and perform the reverse normalization if necessary.

Please refer to the following code for manually computing the output of a neural network:

%% Set “p” and “t” to input and output respectively 
p=[]; t=[];  
n=5; 
logsig=@(x) 1./(1+exp(-x)); 
net = fitnet(n,'trainlm'); 
net.layers{1}.transferFcn='logsig' 
[net,tr]=train(net,p,t); 
Y_pred=net(p); 
error=abs(t-Y_pred); 
plotregression(t,Y_pred); 
%% Code to compute output manually 
w = cellfun(@transpose,[net.IW{1},net.LW(2:size(net.LW,1)+1:end)],'UniformOutput',false); %% Extract Weights 
b = cellfun(@transpose,net.b','UniformOutput',false); %% Extract Biases 
tf = cellfun(@(x)x.transferFcn,net.layers','UniformOutput',false); 
%% mapminmax on inputs 
if strcmp(net.Inputs{1}.processFcns{:},'mapminmax') 
    xoffset = net.Inputs{1}.processSettings{1}.xoffset; 
    gain = net.Inputs{1}.processSettings{1}.gain; 
    ymin = net.Inputs{1}.processSettings{1}.ymin; 
    In0 = bsxfun(@plus,bsxfun(@times,bsxfun(@minus,p,xoffset),gain),ymin); %% Normalize Inputs 
else 
    In0 = inputs; 
end 
In = cell(1,length(w)); 
Out = In; 
In{1} = In0'*w{1}+b{1}; 
Out{1} = eval([tf{1},'(In{1})']); 
for i=2:length(w) 
    In{i} = Out{i-1}*w{i}+b{i}; 
    Out{i} = eval([tf{i},'(In{',num2str(i),'})']); %% Calculate Output of each layer 
end 
%% reverse mapminmax on outputs 
if strcmp(net.Outputs{end}.processFcns{:},'mapminmax') 
    gain = net.outputs{end}.processSettings{:}.gain; 
    ymin = net.outputs{end}.processSettings{:}.ymin; 
    xoffset = net.outputs{end}.processSettings{:}.xoffset; 
    output = bsxfun(@plus,bsxfun(@rdivide,bsxfun(@minus,Out{end}',ymin),gain),xoffset) %% Reverse Normalization 
else 
    output = Out{end} 
end 
%% output matches Y_pred 