MATLAB: Divide function in trainbr train function

divide functiondividefcnneural networktrain functiontrainbrtrainfcn

hello I have made a feedforward neural network. when I set the net.trainFcn to trainbr, and the divide function to divideblock or dividerand with train/val/test ratio of 70/0/30 after training the network, the devideFcn in the training result is set to dividetrain and all of the input data is used for training, and tr.testInd is empty what am I doing wrong? thanks in advance.

Best Answer

The TRAINBR code covering indexing and ratios is BUGGY. I couldn't figure it out when I looked at it back in June 2013.

Now that I'm older, I might have better luck.

1. Complain to MATLAB, directly, with specific, repeatable, examples on MATLAB data

 help nndata
 doc nndata

2. Experiment some more to see if you can impose something other than the 80/0/20 default.

3. Another possibility is to use TRAIN with msereg

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. Please report any findings

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MATLAB: Custom performance vectors for neural network training

% I've been reading in documentation that validation samples are used to measure network generalization; i.e found out how my network would perform on data it has never seen before.

NO. See Below.

%There's also testing samples which are used to give an independent measure of network performance during and after training; used to determing when to stop training.

1. total = design + test

2. design = training + validation

3. training:

 a. used to obtain weight values given training parameters.
 b. training error estimates tend to be extremely biased as the number 
    of unknown weights, Nw, increases toward the number of training 
    equations, Ntrneq.
 c. Ndof = Ntrneq-Nw is the number of estimation degrees of freedom
   (See Wikipedia). As long as Ndof  is sufficiently positive, the bias 
   of estimating error with training data can be mitigated, somewhat,
   by using the degree of freedom adjustment of dividing SSEtrn by Ndof
   instead of Ntrneq.

4. validation:

 a. used repeatedly with the training set to determine a good  set of training parameters (especially the stopping epoch) via choosing the best of multiple random initial weight designs.
 b. Validation set error tends to be much less biased than training set error, especially if training doesn't stop because of validation error convergence.

5. test:

 a.used once, and only once to obtain an unbiased error estimate of          nontraining data.
 b. if performance is unsatisfactory and more designs are necessary, the  data should be repatitioned into new tr/val/tst subsets.

%I would still like to use these. A work around to this is to combine my training, validation, and testing vectors into one matrix.

No. This not a work around. 'dividerand' is the default.

MATLAB uses

 Ntst = round(tstratio*N)
 Nval = round(valratio*N)
 Ntrn = N - Nval - Ntst.

The training record, tr, contains the indices for each subset.

 [ net tr Y E ] = train(net,x,t);

Y is the output and E is the error E = t-Y.

If you want , you can use dividerand anytime before training to obtain the indices then assign the indices to the net by using divideind.

Hope this helps.

*Thank you for formally accepting my answer

Greg

MATLAB: Defining divideblock function for feedforward net

The default 'dividerand' exists at net creation: net = fitnet(H)

Specifying 'divideblock' then replaces it.

Best Answer

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