In recent years, convolutional neural networks (CNNs) have become deeper in order to achieve better classification accuracy in image classification. However, it is difficult to deploy the state-of-the-art deep CNNs for industrial use due to the difficulty of manually fine-tuning the hyperparameters and the trade-off between classification accuracy and computational cost. This paper proposes a novel multi-objective optimization method for evolving state-of-the-art deep CNNs in real-life applications, which automatically evolves the non-dominant solutions at the Pareto front. Three major contributions are made: Firstly, a new encoding strategy is designed to encode one of the best state-of-the-art CNNs; With the classification accuracy and the number of floating point operations as the two objectives, a multi-objective particle swarm optimization method is developed to evolve the non-dominant solutions; Last but not least, a new infrastructure is designed to boost the experiments by concurrently running the experiments on multiple GPUs across multiple machines, and a Python library is developed and released to manage the infrastructure. The experimental results demonstrate that the non-dominant solutions found by the proposed algorithm form a clear Pareto front, and the proposed infrastructure is able to almost linearly reduce the running time.
翻译:近些年来,革命性神经网络(CNNs)变得更加深入,以便在图像分类中实现更好的分类准确性。然而,由于难以手工微微调整超参数以及分类准确性和计算成本之间的权衡,很难将最先进的深有CNN用于工业用途,因此很难将最先进的深有CNN用于工业用途。本文件提出了在现实生活中不断演变的最先进的CNN应用中采用的新颖的多目标优化方法,该方法自动演变出Pareto Front的无主解决方案。 作出了三大贡献:首先,设计了新的编码战略,将最先进的最先进的CNN编码之一编码成最先进的CNN;由于分类精确性和浮动点操作的数量是两个目标,因此很难将分类的精确性和浮动点操作数量作为分类的精确性与计算成本之间的权衡。本文件提出了一种多目的粒子热优化方法,以发展出一个新的基础设施,通过同时进行多台机器对多个GPUPUS的实验,并开发出一个Python图书馆来管理基础设施。 实验结果表明,通过一个几乎不偏向直线式的解决方案,正在运行中找到的模型。