Submitted by redditnit21 t3_y5qn9h in deeplearning
BrotherAmazing t1_iso8zyl wrote
Reply to comment by No_Slide_1942 in Testing Accuracy higher than Training Accuracy by redditnit21
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.
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).
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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