accuracy [@he16]. This study [@tan19b] created a series of neural networks with sets of depth and width hyper-parameters, obtaining high accuracy with low computational resources and a limited number of parameters.\
The number of neurons in each layer(*w*) must also be carefully considered. Too few neurons in the hidden layers may cause underfitting because the model lacks complexity [@oppermann19]. By contrast, too many neurons may result in overfitting and increase the training time. The suggestions [@heaton17] could be a good start for tuning the number of neurons:\
$$w_{input} \;<\; w \;<\; w_{output}$$ $$w \;=\; \frac{2}{3}w_{input}+ w_{output}$$ $$w \;<\; 2w_{output}$$ where *$w_{input}$* is the number of neurons for the input layer, and *$w_{output}$* is the number of neurons for the output layer.\
In contrast to adding layers or neurons in each layer, regularization is applied to reduce the complexity of neural network models, especially those with insufficient training data. Overfitting usually occurs in complex neural networks with many layers and neurons, and the complexity can be reduced using regularization methods. A regularization term is added to avoid overfitting and address feature selections. The general expression of the regularization term is as follows [@ng04]:\
$$cost \;=\; loss function + regularization term$$ The 