  According to AdS/DL (Anti de Sitter/ Deep Learning) correspondence given by
\cite{Has}, in this paper with a data-driven approach and leveraging holography
principle we have designed an artificial neural network architecture to produce
metric field of planar BTZ and quintessence black holes. Data has been
collected by choosing minimally coupled massive scalar field with quantum
fluctuations and try to process two emergent and ground-truth metrics versus
the holographic parameter which plays role of depth of the neural network. Loss
or error function which shows rate of deviation of these two metrics in
presence of penalty regularization term reaches to its minimum value when
values of the learning rate approach to the observed steepest gradient point.
Values of the regularization or penalty term of the quantum scalar field has
critical role to matching this two mentioned metric.
  Also we design an algorithm which helps us to find optimum value for learning
parameter and at last we understand that loss function convergence heavily
depends on the number of epochs and learning rate.
