  Neural architecture search (NAS) has been proposed to automatically tune deep
neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually
suffer from expensive computational cost. Network morphism, which keeps the
functionality of a neural network while changing its neural architecture, could
be helpful for NAS by enabling more efficient training during the search. In
this paper, we propose a novel framework enabling Bayesian optimization to
guide the network morphism for efficient neural architecture search. The
framework develops a neural network kernel and a tree-structured acquisition
function optimization algorithm to efficiently explores the search space.
Intensive experiments on real-world benchmark datasets have been done to
demonstrate the superior performance of the developed framework over the
state-of-the-art methods. Moreover, we build an open-source AutoML system based
on our method, namely Auto-Keras. The system runs in parallel on CPU and GPU,
with an adaptive search strategy for different GPU memory limits.
