Training a neural network
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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
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?
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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
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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
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