how to save result in matlab neural network 2023

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alsayed almetwaly 2023-12-26

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2064172-how-to-save-result-in-matlab-neural-network-2023

评论： alsayed almetwaly 2023-12-27

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John D'Errico 2023-12-26

Highly confusing question. Do you have a neural network that you generated, and you want to know how to save it? Where? As a .mat file?

Or do you have something, that you want to somehow store as a neural network? Something else maybe?

alsayed almetwaly 2023-12-27

yes i generate a neural network , and i want to save it

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Answer 1

Sulaymon Eshkabilov 2023-12-27

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https://ww2.mathworks.cn/matlabcentral/answers/2064172-how-to-save-result-in-matlab-neural-network-2023#answer_1378102

在 MATLAB Online 中打开

Understand what issue you are facing with the net() - neural network simulation. Here is one code automatically generated by MATLAB, and then I added a few lines to store data from simulations (see comments SAVE).

%   X - input data.
%   y - target data.
Compounds = {'pH', 'L*', 'a*', 'b*', 'SubColor', 'WBSF1', 'WBSF2', 'WBSF3', 'WBSF4', 'WBSF5', 'WBSF5', 'WBSF_ave'};
N = 1;        % 1 = pH; 2 = L*; 3 = a*; 4 = b*; 5 = SubColor; 6:11 = WBSF1:6; 12 = WBSF_ave  
disp(['Simulation of:   ' Compounds{N}]);
x = INALL(); x=x';   % Input: predictor data set 
t = normalize(RALL(:,N), 'norm'); t=t'; % Output Data set
%t = RALL(:,N); t=t';
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainbr';  % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);
% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivision
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';     % Divide up every sample
net.divideParam.trainRatio = 80/100;
net.divideParam.valRatio = 10/100;
net.divideParam.testRatio = 10/100;
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse';  % Mean Squared Error
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist','plotregression', 'plotfit'};
% Train the Network
[net,tr] = train(net,x,t);
TRn{N}=tr;                       % SAVE
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance  = perform(net,t,y);
PERFORn{N}  = performance;       % SAVE
% Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets   = t .* tr.valMask{1};
testTargets  = t .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,y);
valPerformance   = perform(net,valTargets,y);
testPerformance  = perform(net,testTargets,y);
% 
trainTARGETn{N}=trainTargets;             % SAVE
valTARGETn{N}=valTargets;                 % SAVE 
testTARGETn{N}=testTargets;               % SAVE
trainPERFORn{N}=trainPerformance;         % SAVE 
valPERFORn{N}=valPerformance;             % SAVE
testPERFORn{N}=testPerformance;           % SAVE
% View the Network
view(net)
MODELn{N}=net;
% figure(N), plotperform(tr)
% figure(N+1), plottrainstate(tr)
% figure(N+2), plotregression(t,y)