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I understand this is a wide question.

But there can be some suggestions.

I can try some methods which I do not know.

I think the model is already prefect on train data. But the test accuracy is a little low. So do you think there still can be some improvement on the deep model?

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    $\begingroup$ in theory you would like to have 100%/100% usually best model is when test%==train% and this value is highest possible (here >90%) but in practice if you was unlucky during data splitting your scores are very good and there may be little room for improvement $\endgroup$
    – quester
    Oct 12, 2019 at 12:29

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Your model is overfit: it learns patterns on the training data which are specific to the training data, hence the big difference in performance on the test data. This is bad, because it means that your model wrongly assumes that some patterns are good indication for the class even though it's just noise.

It's possible that by solving the overfitting problem the performance would improve. However nobody can give you any advice without knowing anything about your actual problem and data. Btw you don't explain why you use accuracy as evaluation measure, it might or might not be optimal for your problem.

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Sqlova reach 99.8% in training and 90.0% in testing.

This work improve Sqlova further in testing.

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