  A neural network with two hidden layers is developed for nuclear mass
prediction, based on the finite-range droplet model (FRDM12). Different
hyperparameters, including the number of hidden units, the choice of activation
functions, the initializers, and the learning rates, are adjusted explicitly
and systematically. The resulting mass predictions are achieved by averaging
the predictions given by several different sets of hyperparameters with
different regularizers and seed numbers. It can provide us not only the average
values of mass predictions but also reliable estimations in the mass prediction
uncertainties. The overall root-mean-square deviations of nuclear mass have
been reduced from $0.603$ MeV for the FRDM12 model to $0.200$ MeV and $0.232$
MeV for the training set and validation set, respectively.
