try different hyperparameters (such as the number of units per layer or the learning rate of the optimizerto find the optimal configuration optionallyiterate on feature engineeringadd new featuresor remove features that don' seem to be informative be mindful of the followingevery time you use feedback from your validation process to tune your modelyou leak information about the validation process into the model repeated just few timesthis is innocuousbut done systematically over many iterationsit will eventually cause your model to overfit to the validation process (even though no model is directly trained on any of the validation datathis makes the evaluation process less reliable once you've developed satisfactory model configurationyou can train your final production model on all the available data (training and validationand evaluate it one last time on the test set if it turns out that performance on the test set is significantly worse than the performance measured on the validation datathis may mean either that your validation procedure wasn' reliable after allor that you began overfitting to the validation data while tuning the parameters of the model in this caseyou may want to switch to more reliable evaluation protocol (such as iterated -fold validationlicensed to