  The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for
turbulence modeling due to its high cost-effectiveness. Its accuracy is largely
based on a reliable Reynolds stress anisotropy tensor closure model. There has
been an amount of work aiming at improving traditional closure models, while
they are still not satisfactory to some complex flow configurations. In recent
years, advances in computing power have opened up a new way to address this
problem: the machine-learning-assisted turbulence modeling. In this paper, we
employ neural networks to fully predict the Reynolds stress anisotropy tensor
of turbulent channel flows at different friction Reynolds numbers, for both
interpolation and extrapolation scenarios. Several generic neural networks of
Multi-Layer Perceptron (MLP) type are trained with different input feature
combinations to acquire a complete grasp of the role of each parameter. The
best performance is yielded by the model with the dimensionless mean streamwise
velocity gradient $\alpha$, the dimensionless wall distance $y^+$ and the
friction Reynolds number $\mathrm{Re}_\tau$ as inputs. A deeper theoretical
insight into the Tensor Basis Neural Network (TBNN) clarifies some remaining
ambiguities found in the literature concerning its application of Pope's
general eddy viscosity model. We emphasize the sensitivity of the TBNN on the
constant tensor $\textbf{T}^{*(0)}$ upon the turbulent channel flow data set,
and newly propose a generalized $\textbf{T}^{*(0)}$, which considerably
enhances its performance. Through comparison between the MLP and the augmented
TBNN model with both $\{\alpha, y^+, \mathrm{Re}_\tau\}$ as input set, it is
concluded that the former outperforms the latter and provides excellent
interpolation and extrapolation predictions of the Reynolds stress anisotropy
tensor in the specific case of turbulent channel flow.
