  By bridging deep networks and physics, the programme of geometrization of
deep networks was proposed as a framework for the interpretability of deep
learning systems. Following this programme we can apply two key ideas of
physics, the geometrization of physics and the least action principle, on deep
networks and deliver a new picture of deep networks: deep networks as memory
space of information, where the capacity, robustness and efficiency of the
memory are closely related with the complexity, generalization and
disentanglement of deep networks. The key components of this understanding
include:(1) a Fisher metric based formulation of the network complexity; (2)the
least action (complexity=action) principle on deep networks and (3)the geometry
built on deep network configurations. We will show how this picture will bring
us a new understanding of the interpretability of deep learning systems.
