I am trying to set up transfer learning using instructions from this repository page on EfficientNet. I am able to perform a stand-alone training and testing using the instructions on that page. The next step is to use cross-validate model from the resource functions page to evaluate the range of performance on my dataset.

The documentation for CVModel provides an example using NetTrain; however, it uses TimeGoal as stopping criterion during cross-valiation.

The relevant code setup based on instructions from that page:

cvEfficientNets = ResourceFunction["CrossValidateModel",Flatten[trainData],
Function[NetTrain[net, #, All, TimeGoal -> 10, 
TargetDevice -> {"GPU", All}]]   

I wonder if that's appropriate if I am trying to compare performance of two different NNs for the same dataset, eg., ResNet vs. EfficientNet. What would be reasonable alternatives to stopping the training during cross-validation stage?

I tried TrainingStoppingCriterion->"Loss";however, WL complains that no validation set has been specified and defaults to using the training set for validation. I think that's probably not correct. If I specify ValidationSet->Scaled[0.1] as is usually done, I am uncertain if that's necessary. K-Fold validation should already have a partition selected for validation.

So, any advice is highly appreciated.


1 Answer 1


I think setting up MaxTrainingRounds to the same number should be a reasonable basis to compare the performance of different NNs during cross-validation stage.


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