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No_Slide_1942 t1_iso8k41 wrote

Reply to comment by BrotherAmazing in Testing Accuracy higher than Training Accuracy by redditnit21

The same thing is happening without dropout. What should I do?

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BrotherAmazing t1_iso8zyl wrote

If you have a different problem where this happens without dropout then you may indeed want to make sure the training/test split isn’t a “bad” one and do k-fold validation.

The other thing to check would be other regularizers you may be using during training but not test that make it harder for the network to do well on training sets; i.e., you can dial down data augmentation if you are using that, and so on.

Things people have touched upon already for the most part, but this is very common to see when using dropout layers.

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redditnit21 OP t1_isoa0lc wrote

I commented from the different account by mistake. After looking at everyone’s comment. I tried without dropout and the same thing is happening. I am not using any data augmentation except the rescaling (1/.255).

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BrotherAmazing t1_isosnle wrote

Then indeed I would try different randomized training/test set splits to rule that out as one step in the debugging.

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