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A new hyperparameters optimization method for convolutional neural networks

机译：一种新的卷积神经网络优化方法

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The use of convolutional neural networks involves hyperparameters optimization. Gaussian process based Bayesian optimization (GPEI) has proven to be an effective algorithm to optimize several hyperparameters. Then deep networks for global optimization algorithm (DNGO) that used neural network as an alternative to Gaussian process was proposed to optimize more hyperparameters.This paper presents a new algorithm that combines multiscale and multilevel evolutionary optimization (MSMLEO) with GPEI to optimize dozens of hyperparameters. These hyperparameters are divided into two groups. The first group related with the sizes of layers and kernels are discrete integers. The second group related with learning rates and so on is continuous floating-point numbers. All combinations of the first group are corresponding to the combinations of grid points on multi-scale grids and MSMLEO launches GPEI to optimize the second group of hyperparameters while the first group keeps fixed. The output of convolutional networks configured with above two groups of optimized hyperparameters is used as the fitness of MSMLEO. MSMLEO alternates with GPEI to search the optimal hyperparameters from coarsest scale to finest scale. Experimental results show that our algorithm has better performance and adaptability on optimizing dozens of hyperparameters of neural networks with a variety of numerical types. (C) 2019 Published by Elsevier B.V.
机译：卷积神经网络的使用涉及超参数优化。基于高斯过程的贝叶斯优化（GPEI）已被证明是优化几种超参数的有效算法。然后提出了使用神经网络作为高斯过程的替代的全局优化算法（DNGO）的深网络，以优化更多的超开名。本文提出了一种新的算法，将多尺度和多级进化优化（MSMLEO）与GPEI结合以优化数十个QuandEncameters 。这些超级分为两组。与图层和内核大小相关的第一个组是离散整数。第二组与学习率相关等于连续浮点数。第一组的所有组合对应于多尺度网格上的网格点的组合，而MSMLEO启动GPEI以优化第二组超参数，而第一组保持固定。配置有超过两组优化的超参数组的卷积网络的输出用作MSMLEO的适应性。 MSMLEO与GPEI交替，将最佳的超参数从粗尺寸搜索到最精确的规模。实验结果表明，我们的算法具有更好的性能和适应性，以优化具有多种数字类型的神经网络的数十次超公路。 （c）2019年由elestvier b.v发布。

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