Bayesian Neural Architecture Search using A Training-Free Performance Metric

Abstract

Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settings. The architecture optimization of RNNs is a time-consuming task, where the search space is typically a mixture of real, integer and categorical values. To allow for shrinking and expanding the size of the network, the representation of architectures often has a variable length. In this paper, we propose to tackle the architecture optimization problem with a variant of the Bayesian Optimization (BO) algorithm. To reduce the evaluation time of candidate architectures the Mean Absolute Error Random Sampling (MRS), a training-free method to estimate the network performance, is adopted as the objective function for BO. Also, we propose three fixed-length encoding schemes to cope with the variable-length architecture representation. The result is a new perspective on accurate and efficient design of RNNs, that we validate on three problems. Our findings show that 1) the BO algorithm can explore different network architectures using the proposed encoding schemes and successfully designs well-performing architectures, and 2) the optimization time is significantly reduced by using MRS, without compromising the performance as compared to the architectures obtained from the actual training procedure.

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• Adam: A Method for Stochastic Optimization
• TensorFlow: A system for large-scale machine learning
• On the difficulty of training Recurrent Neural Networks
• Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research
• BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level
• Random Error Sampling-based Recurrent Neural Network Architecture Optimization
• Low-Cost Recurrent Neural Network Expected Performance Evaluation
• DLOPT: Deep Learning Optimization Library

Cited by in corpus (5)

• AutoML: A Survey of the State-of-the-Art
• Learning Deep Morphological Networks with Neural Architecture Search
• Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems
• An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
• Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS