  In this paper, we propose to disentangle and interpret contextual effects
that are encoded in a pre-trained deep neural network. We use our method to
explain the gaming strategy of the alphaGo Zero model. Unlike previous studies
that visualized image appearances corresponding to the network output or a
neural activation only from a global perspective, our research aims to clarify
how a certain input unit (dimension) collaborates with other units (dimensions)
to constitute inference patterns of the neural network and thus contribute to
the network output. The analysis of local contextual effects w.r.t. certain
input units is of special values in real applications. Explaining the logic of
the alphaGo Zero model is a typical application. In experiments, our method
successfully disentangled the rationale of each move during the Go game.
