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CNN Hyperparameter Optimization using Random Grid Coarse-to-fine Search for Face Classification

机译：CNN HyperParameter优化使用随机网格粗加学搜索面部分类

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Convolutional Neural Network (CNN) is a recently used popular machine learning technique to classify images. However, choosing an optimum and efficient architecture is an inevitable challenge. The research goal was to implement CNN on face classification from low quality CCTV footage. The best model was gained from the hyperparameter optimization process used on CNN structure. The optimized hyperparameters were those connected to the structure network including activation function, the number of kernel, the size of kernel, and the number of nodes on the fully connected layers. Hyperparameter optimization strategy used was random grid coarse-to-fine search optimization approach. This approach combined random search, grid search, and coarse-to-fine technique that was easily and efficiently applied, yet worked well. Exhaustive-random search process was done by evaluating all selected activation functions and choosing another hyperparameters randomly. This was based on the assumption that activation functions were the most related hyperparameter to the model. The SELU activation function used in the next step was the one with the best average performance. Grid coarse-to-fine was conducted to optimize the number of kernel and the number of node on fully connected layer, while grid search was conducted to optimize the kernel size. This process aimed to locate optimal value gradually in hyperparameter which had high-dimensional space. Evaluation of the model resulted from the optimum hyperparameter was 97,56%.
机译：卷积神经网络（CNN）是最近使用的流行机器学习技术来分类图像。然而，选择最佳和有效的架构是一个不可避免的挑战。研究目标是在低质量CCTV镜头上实施CNN。从CNN结构上使用的高参数优化过程中获得了最佳模型。优化的超参数是连接到结构网络的那些，包括激活函数，内核数量，内核大小以及完全连接的层上的节点的数量。使用的超参数优化策略是随机电网粗良好的搜索优化方法。这种方法组合了随机搜索，网格搜索和粗良好的技术，易于和有效地应用，但工作得很好。通过评估所有选定的激活函数并随机选择另一个超参数来完成彻底的随机搜索过程。这是基于该假设，即激活函数是模型最相关的封路数据。下一步中使用的SELU激活函数是具有最佳平均性能的函数。进行网格粗细到精细，以优化完全连接层上的内核数量和节点数，而网格搜索是针对优化内核大小的。该过程旨在在具有高维空间的普遍参数中逐渐定位最佳值。从最佳丙酰胺计的模型评估为97,56％。

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