  We introduce a deep neural network (DNN) framework called the
\textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work
(\textsc{radnet}), which is capable of making accurate polarization and static
dielectric function predictions for solids. We use these predictions to
calculate Born-effective charges, longitudinal optical transverse optical
(LO-TO) splitting frequencies, and Raman tensors for two prototypical examples:
GaAs and BN. We then compute the Raman spectra, and find excellent agreement
with \textit{ab initio} techniques. \textsc{radnet} is as good or better than
current methodologies. Lastly, we discuss how \textsc{radnet} scales to larger
systems, paving the way for predictions of response functions on meso-scale
structures with \textit{ab initio} accuracy.
