Table 1.

Hyperparameters considered in the neural architecture search of deep neural networks (DNN)^a

Hyperparameter	Space
Number of units	1, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000
Hidden layers	1, 2, 3, 4
Dropout rateb	0.5, 0.6, 0.7, 0.8, 0.9, 1
L2c	0.0000, 0.0025, 0.0050, 0.0075, 0.0100, 0.0125, 0.0150, 0.0175, 0.0200, 0.0225, 0.0250, 0.0275, 0.3000, 0.0325, 0.0350, 0.0375, 0.0400, 0.0425, 0.0450, 0.0475, 0.0500, 0.0525, 0.0550, 0.0575, 0.0600, 0.0625, 0.0650, 0.0675, 0.0700, 0.0725, 0.0750, 0.0775, 0.0800, 0.0825, 0.0850, 0.0875, 0.0900, 0.0925, 0.0950, 0.0975, 0.1000

^aThe hyperparameters were randomly select and combined to find the optimal DNN architecture

^bThe dropout rate was applied in all layers, except for the output layer

^cL2 = ridge regularization