Auto-Keras: An Efficient Neural Architecture Search System

TL;DR

Auto-Keras introduces a novel Bayesian optimization framework guided by network morphism for efficient neural architecture search, significantly reducing computational costs and enabling an open-source AutoML system adaptable to various hardware constraints.

Contribution

The paper presents a new NAS framework combining Bayesian optimization with network morphism, improving search efficiency and building an accessible AutoML system.

Findings

Outperforms state-of-the-art NAS methods on benchmark datasets.

Reduces computational cost of neural architecture search.

Provides an open-source AutoML system adaptable to hardware limits.

Abstract

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.