MATLAB: How do i increase the training epochs for trainbr

deep learningDeep Learning ToolboxMATLABtraining

I am training a narxnet using the train br function, however it ends up using all 1000 epochs each time so I cant use very many neurons, so im wondering how i can go about increasing the number. The only ways ive seen require you to define the layers of an LSTM network.

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I got it to work thanks to a kind redditor. Here is the relevant webpage for anyone who has the same issue.

https://uk.mathworks.com/help/deeplearning/ref/trainbr.html

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MATLAB: Neural network of a single layer

net  = feedforwardnet;
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x is the required number of layers. Here 1.

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An ovefit network that is overtrained memorizes the training data and can perform very poorly on nontraining data. Instead of using validation set stopping to prevent poor performance on nontraining data, TRAINBR uses Bayesian Regularization. Unfortunately, TRAINBR does not allow a val set. Research has shown that in general, using both is better than using either.

So, if you wish both, use another training algorithm with the obsolete MSEREG or use the regularization option in the new MSE.

 help msereg
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Nontraining indices are calculated first. The default for TRAINBR is

 Ntst = round(0.2*N)
 Nval = 0
 Ntrn = N - Nval-Ntst

Hope this helps.

Thank you for formally accepting my answer

Greg

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