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danielgafni t1_isl8voi wrote

Are you using dropout or other regularizations that affect training but not testing? You’ve got the answer

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redditnit21 OP t1_isn27zi wrote

Yeah, I am using dropout layer in the model to avoid overfitting. So am I supposed to remove the dropout layer?

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First_Bullfrog_4861 t1_isnanri wrote

also: data augmentation will make the training set more complex.

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redditnit21 OP t1_isnc1yx wrote

There is no data augmentation suing. Should I use data augmentation?

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Dmytro_P t1_isnljcv wrote

It depends on how large and diverse your dataset is, but in most cases you should. You'd see an even larger difference between the train and test sets.

You can also try to use multiple folds, to train model 5 times for example with the different test set, to check if the test set you selected accidentally contains simpler samples.

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danielgafni t1_it97yro wrote

Don’t remove it, it’s just how it works. There is nothing wrong with having a higher train loss if you are using dropout.

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