  A fully-convolutional neural-network model is used to predict the streamwise
velocity fields at several wall-normal locations by taking as input the
streamwise and spanwise wall-shear-stress planes in a turbulent open channel
flow. The training data are generated by performing a direct numerical
simulation (DNS) at a friction Reynolds number of $Re_{\tau}=180$. Various
networks are trained for predictions at three inner-scaled locations ($y^+ =
15,~30,~50$) and for different time steps between input samples $\Delta
t^{+}_{s}$. The inherent non-linearity of the neural-network model enables a
better prediction capability than linear methods, with a lower error in both
the instantaneous flow fields and turbulent statistics. Using a dataset with
higher $\Delta t^+_{s}$ improves the generalization at all the considered
wall-normal locations, as long as the network capacity is sufficient to
generalize over the dataset. The use of a multiple-output network, with
parallel dedicated branches for two wall-normal locations, does not provide any
improvement over two separated single-output networks, other than a moderate
saving in training time. Training time can be effectively reduced, by a factor
of 4, via a transfer learning method that initializes the network parameters
using the optimized parameters of a previously-trained network.
