Auto-Keras: Efficient Neural Architecture Search with Network Morphism
  Neural architecture search (NAS) has been proposed to automatically tune deep
neural networks, but existing search algorithms 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 a 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 by introducing a neural network kernel
and a tree-structured acquisition function optimization algorithm, which more
efficiently explores the search space. Intensive experiments 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 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.
