It’s what other people have already said: This is extremely common to see if you’re using dropout. There’s nothing necessarily wrong here either, and this network might outperform a network (on test data—the data we care about!) that is trained without dropout and gets higher training accuracy.

Here is how you can prove it to yourself:

1. You can keep dropout activated during test time as an experiment and see that the test accuracy, when dropout remains on, does indeed decrease to be below the training accuracy.

2. You can keep everything else fixed and just parametrically dial down the dropout % in each dropout layer. Usually 0.5 (50%) is a default, but you’ll see for a fixed training/test split that as that parameter goes from 0.5 —> 0.25 —> 0.1 —> 0.05 —> 0 that the training accuracy will increase back to be at/above the test accuracy.

You can also rule out the possibility you had a rare split that led to an easy test set and hard training set by splitting randomly over and over and seeing that this phenomenology is not rare, but the norm across nearly all splits, but if 1 and 2 above exhibit behavior consistent with dropout being the reason, then I see this last exercise as a waste of time unless you just want to win an argument against someone who insists it is due to a “bad” split. If they really insist that vs. just propose it as a possible reason, then they don’t have much real-world experience using dropout! This is very common, nothing wrong, telltale sign of dropout.