  In this manuscript, we show that any neural network with any activation
function can be represented as a decision tree. The representation is
equivalence and not an approximation, thus keeping the accuracy of the neural
network exactly as is. We believe that this work provides better understanding
of neural networks and paves the way to tackle their black-box nature. We share
equivalent trees of some neural networks and show that besides providing
interpretability, tree representation can also achieve some computational
advantages for small networks. The analysis holds both for fully connected and
convolutional networks, which may or may not also include skip connections
and/or normalizations.
