Validation loss less than training loss (vald accuracy higher than training accuracy) without using dropout

Question

I have been working on Multitask model, using VGG16 with no dropout layers. I find out that the validation accuracy is higher than the training accuracy and validation loss is lesser than the training loss.

I cant seem to findout the reason to why is this happening in the model.

Below is the training plot:

Data:

I am using (randomly shuffled images) 70% train, 15% validation, 15% test, and the results on 15% test data is as follows:

Do you think these results are too good to be true?

score 2 · Accepted Answer · answered Apr 07 '20 at 16:23

2

At the beginning, yes, but at the end you can see they sort of start changing places.

At the end of the training you are getting near an overfit point (if the val loss starts increasing or the val accurace starts decreasing, then you've reached overfitting)

But at the beginning, what can explain that behavior might be some data unbalance between training and test. Maybe you've got easier examples in the validation database, or a class unbalance, or more empty values, etc.

answered Apr 07 '20 at 16:23

Daniel Möller

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Okay, What do you suggest for this ? My data contains imbalanced classes as well – Obiii Apr 08 '20 at 13:37
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Use class weights in the loss, balance your data, etc. – Daniel Möller Apr 08 '20 at 14:11

Validation loss less than training loss (vald accuracy higher than training accuracy) without using dropout

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