Title: Auto-Keras: Efficient Neural Architecture Search with Network Morphism,
Abstract: 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.