Hyper-Parameter Optimization: A Review of Algorithms and Applications (2003.05689v1)

Abstract: Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of in almost every aspect of daily life. However, despite this achievement, the design and training of neural networks are still challenging and unpredictable procedures. To lower the technical thresholds for common users, automated hyper-parameter optimization (HPO) has become a popular topic in both academic and industrial areas. This paper provides a review of the most essential topics on HPO. The first section introduces the key hyper-parameters related to model training and structure, and discusses their importance and methods to define the value range. Then, the research focuses on major optimization algorithms and their applicability, covering their efficiency and accuracy especially for deep learning networks. This study next reviews major services and toolkits for HPO, comparing their support for state-of-the-art searching algorithms, feasibility with major deep learning frameworks, and extensibility for new modules designed by users. The paper concludes with problems that exist when HPO is applied to deep learning, a comparison between optimization algorithms, and prominent approaches for model evaluation with limited computational resources.

A Comprehensive Overview of Hyper-Parameter Optimization in Deep Learning

The paper by Yu and Zhu provides an extensive review of hyper-parameter optimization (HPO) techniques, particularly in the context of deep learning. It offers insights into the critical aspects of HPO including the types of hyper-parameters, search algorithms, early stopping strategies, and the role of various toolkits in implementing HPO. The focus is on automating the HPO process, which is pivotal for enhancing model performance and reducing the reliance on manual tuning.

Classification of Hyper-Parameters

The authors start by categorizing hyper-parameters into structure-related and training-related parameters. Structure-related parameters include the number of hidden layers and width of layers, which directly influence the model’s learning capacity. Training-related parameters encompass the learning rate, batch size, and choice of optimizer, which are crucial for the convergence and efficiency of the model. The discussion on learning rate scheduling, including strategies like exponential decay and cyclical learning rates, highlights its importance in achieving satisfactory model performance.

Search Algorithms for Hyper-Parameter Optimization

The paper explores various search algorithms used for HPO:

• Grid Search and Random Search: These are simple yet computationally intensive methods. Grid search performs exhaustive searches over specified parameter grids but suffers from the curse of dimensionality. Random search, although potentially more efficient, does not guarantee finding the global optimum.
• Bayesian Optimization and Tree Parzen Estimators (TPE): These methods offer a more structured approach by modeling a probability distribution over the objective function to iteratively hone in on the best set of parameters.
• Multi-Armed Bandit Algorithms: Techniques like Successive Halving, HyperBand, and Bayesian Optimization–HyperBand (BOHB) are described as resource-efficient methods that dynamically allocate more computational efforts to promising configurations.

Early Stopping Strategies

The paper explores early stopping techniques that are essential for efficient utilization of computational resources. Methods like median stopping and curve fitting allow for termination of suboptimal trials early, conserving resources for more promising configurations. The inclusion of bandit-based mechanisms exemplifies the integration of adaptive strategizing in tuning processes.

Practical Implementation with Toolkits

Several toolkits that facilitate HPO processes are discussed, demonstrating the practical application of the aforementioned strategies:

• Open-Source Tools: NNI and Ray.Tune offer extensive support for implementing state-of-the-art algorithms with customizable interfaces. These tools are particularly advantageous for researchers requiring flexibility.
• Cloud Services: Google Vizier and Amazon SageMaker provide scalable solutions with minimal configuration, leveraging cloud infrastructure to handle large-scale HPO tasks efficiently.

Implications and Future Directions

The paper implies significant implications for both theoretical advancements and practical deployments in AI and machine learning. With the increasing complexity of models, efficient HPO becomes indispensable. Consequently, the paper underscores the necessity for continued refinement of HPO techniques, especially in parallelization and reducing computational costs. Furthermore, the exploration of techniques like transfer learning and meta learning in HPO holds promise for further advancements.

In conclusion, the paper by Yu and Zhu is a valuable resource, providing a detailed synthesis of HPO methodologies and their applicability in deep learning. By offering a thorough comparison of algorithms and tools, the paper aids researchers and practitioners in selecting appropriate HPO strategies for their specific needs, thereby enhancing the reliability and reproducibility of neural network training outcomes.