  We construct a neural network to learn the RN-AdS black hole metric based on
the data of optical conductivity by holography. The linear perturbative
equation for the Maxwell field is rewritten in terms of the optical
conductivity such that the neural network is constructed based on the
discretization of this differential equation. In contrast to all previous
models in AdS/DL (deep learning) duality, the derivative of the metric function
appears in the equation of motion and we propose distinct finite difference
methods to discretize this function. The notion of the reduced conductivity is
also proposed to avoid the divergence of the optical conductivity near the
horizon.The dependence of the training outcomes on the location of the cutoff,
the temperature as well as the frequency range is investigated in detail. This
work provides a concrete example for the reconstruction of the bulk geometry
with the given data on the boundary by deep learning.
