When the 𝛾(log 𝜏) ≥ 0 constraint was relaxed, non-saturating exponential unit functions were used for the hidden and output layers . The DNN had 10,657 parameters. 𝐿 and 𝑅 were also optimized during training. For DNN pretraining, the weights were initialized using the Xavier method (normal or uniform), and the biases set to zero or sampled from a normal distribution (Table ). Then, the weights and biases of the pretrained DNN were used as initial values for DNN training. The learning rates for pretraining and training were set to 10 -3 and 10 -4 , respectively. To avoid overfitting, the early stopping threshold was set to 10 -8 between two consecutive evaluations of the loss, and the optimal iteration corresponded to the minimum value of 𝜀 , . The model was implemented with PyTorch .