Ref: https://www.researchgate.net/search.Search.html?type=researcher&query=sarfaraz

following

Hello Nakib,

If you are looking to get the best performance out of your own model, then I would suggest "hyper-parameter tuning" while evaluating using some sort of cross-validation scheme. Tensorflow (which works well with keras) has tools for hyperparameter searching https://www.tensorflow.org/tensorboard/hyperparameter_tuning_with_hparams with visualizations available in tensorboard. Additionally Tensorflow Extended has tools for automated model tuning as more data becomes available.

For hyper-parameter tuning in general, you may begin your search with random selections of the hyper-parameter space to help narrow down your search and then use a more narrow grid searching approach. The goal here is to find the global minimum of your model's loss with respect to your hyper-parameter space.

Also, be sure to use a cross-validation method and regularization to avoid over-fitting to your training data.