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Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in

¹School of information science and technology, Southwest Jiaotong University, ChengDu, China.

|December 14, 2017

Summary

This study introduces an automatic method for optimizing deep neural networks (DNNs) using particle swarm optimization (PSO) and steepest gradient descent. The approach enhances generalization performance compared to random selection.

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Optimizing deep neural network (DNN) architecture and hyperparameters is crucial for performance.
Manual hyperparameter tuning is time-consuming and suboptimal.
Existing automatic methods may lack efficiency or effectiveness in exploring the search space.

Purpose of the Study:

To propose a novel automatic hyperparameter selection approach for DNNs.
To combine particle swarm optimization (PSO) with steepest gradient descent for efficient network configuration.
To improve the generalization performance of DNNs through optimized network structures and hyperparameters.

Main Methods:

Network configurations encoded as real-number vectors for PSO individuals.
PSO algorithm for global search of optimal network configurations.
Steepest gradient descent for local training of DNN classifiers during PSO evaluation and final model training.

Main Results:

The proposed hybrid approach effectively determined optimal network structures and hyperparameters.
Experiments on handwritten characters and biological activity prediction showed superior generalization performance.
DNN classifiers trained with the proposed method outperformed those from a random approach.

Conclusions:

The combined PSO and steepest gradient descent method offers an effective tool for automatic DNN optimization.
This approach provides a viable alternative for selecting network structures and hyperparameters.
The method demonstrates improved generalization capabilities for DNN models.