Training a neural network

8 visualizaciones (últimos 30 días)
Sam harris
Sam harris el 29 de Jun. de 2012
Respondida: Greg Heath el 22 de Abr. de 2014
Hi,
I am trying to develop a neural network which predicts an output based on 4 inputs, one of which is the output of the previous step. Currently I am just using a standard function fitting network (not a time-series prediction).
The neural network works really well (r squared approx. 0.98 - 0.99) when the output of the previous step is given independent of the neural network result.
However, when I use the neural network predicted output as the input to the next prediction, the neural network result is virtually worthless. Also, the results differ greatly every time I re-train the network - i.e. it seems the results are very dependent on the initial weights.
I am not sure if this is a problem of overtraining? Any help would be greatly appreciated.
Sam
  5 comentarios
Mostrar 3 comentarios más antiguos
Greg Heath
Greg Heath el 4 de Sept. de 2012
Please post this as a new question.
Greg Heath
Greg Heath el 4 de Sept. de 2012
How many data points? How many hidden nodes? Is there a validation set for stopping? Do you get the same type of performace from a matlab demo data set?

Iniciar sesión para comentar.

Iniciar sesión para responder a esta pregunta.

Respuesta aceptada

Greg Heath
Greg Heath el 22 de Abr. de 2014
Sam harris on 2 Jul 2012
% Create a Nonlinear Autoregressive Network with External Input
% inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 10;
1. What makes you think these are appropriate inputs??
% net =narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
% net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
% net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};
2. Why bother? The last 2 statements are defaults.
% [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
% net.divideFcn = 'dividerand'; % Divide data randomly
% net.divideMode = 'value'; % Divide up every value
3. The last 2 statements are inappropriate for time series
% net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100;
% net.divideParam.testRatio = 15/100;
% net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% net.performFcn = 'mse'; % Mean squared error
% net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
% 'ploterrcorr', 'plotinerrcorr'};
4. Why bother? The last 6 statements are defaults.
% % Train the Network
% [net,tr] =train(net,inputs,targets,inputStates,layerStates);
% if true % code
% end
5. What does "if true ...etc... end" suppose to do?
6. You still have to close the loop and continue training.
7. See
http://www.mathworks.com/matlabcentral/newsreader/view_thread/332147#912806

Más respuestas (1)

Greg Heath
Greg Heath el 30 de Jun. de 2012
For the fitting net I assume you are using
x =[input(:,2:end); target(:,1:end-1)];
t = target(:,2:end);
size(input) = ?
size(target) = ?
numHidden = ?
net.divideParam = ?
R2trn ~ 0.985
R2val = ?
R2tst = ?
How is the timeseries net configured? Please include code.
Hope this helps.
Greg
  3 comentarios
Mostrar 1 comentario más antiguo
Greg Heath
Greg Heath el 3 de Jul. de 2012
You didn't answer my questions.
Greg
Sam harris
Sam harris el 3 de Jul. de 2012
Hi Greg,
Thanks for your time, in answer to your questions:
size(input) = 12000 rows by 5 columns (data time series in rows)
numHidden = 10
net.divideParam = 70% used for training, 15% for validation, 15% for testing
R2trn ~ 0.985
R2val ~ 0.98
R2tst ~ 0.98
Code Used:
inputSeries = tonndata(Input,false,false);
targetSeries = tonndata(FOS,false,false);
Test1 = tonndata(Test1,false,false);
FOS1 = tonndata(FOS1,false,false);
inputDelays = 1:1;
feedbackDelays = 1:1;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
eedback Pre/Post-Processing Functions
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
net.divideFcn = 'dividerand';
net.divideMode = 'value';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
net.performFcn = 'mse'; % Mean squared error
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
'ploterrcorr', 'plotinerrcorr'};
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
trainTargets = gmultiply(targets,tr.trainMask);
valTargets = gmultiply(targets,tr.valMask);
testTargets = gmultiply(targets,tr.testMask);
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)
save('my_network','net'); %Saves the network
delay=1;
inputSeriesPred = [inputSeries(end-delay+1:end),Test1];
targetSeriesPred = [targetSeries(end-delay+1:end),FOS1];
[Xs,Xi,Ai,Ts] = preparets(net,inputSeriesPred,{},targetSeriesPred);
yPred = net(Xs,Xi,Ai);
perf = perform(net,yPred,FOS1);
yPred=cell2mat(yPred);
ave=mean(FOSA);
for i=1:length(yPred);
D(i,1)=(FOSA(i,1)-ave)^2;
SSTOT=sum(D);
D1(i,1)=(yPred(1,i)-FOSA(i,1))^2;
SSERR=sum(D1);
end
RSOS=1-(SSERR/SSTOT);
RSOSif true
% code
end
The network appears to be training fine until I use a closeloop network to test it.
Once again many thanks for your help,
Sam

Iniciar sesión para comentar.

Categorías

Más información sobre Define Shallow Neural Network Architectures en Help Center y File Exchange.

Etiquetas

Productos

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by