CNN model is a popular method for imagery analysis, so it could be utilized to recognize handwritten digits based on MNIST datasets. For higher recognition accuracy, various CNN models with different fully connected layer sizes are exploited to figure out the relationship between the CNN fully connected layer size and the recognition accuracy. Inspired by previous pruning work, we performed pruning methods of distinctiveness on CNN models and compared the pruning performance with NN models. For better pruning performances on CNN, the effect of angle threshold on the pruning performance was explored. The evaluation results show that: for the fully connected layer size, there is a threshold, so that when the layer size increases, the recognition accuracy grows if the layer size smaller than the threshold, and falls if the layer size larger than the threshold; the performance of pruning performed on CNN is worse than on NN; as pruning angle threshold increases, the fully connected layer size and the recognition accuracy decreases. This paper also shows that for CNN models trained by the MNIST dataset, they are capable of handwritten digit recognition and achieve the highest recognition accuracy with fully connected layer size 400. In addition, for same dataset MNIST, CNN models work better than big, deep, simple NN models in a published paper.

中文翻译：

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修剪数字卷积神经网络的手写数字识别

CNN模型是一种流行的图像分析方法，因此可用于基于MNIST数据集识别手写数字。为了获得更高的识别精度，人们利用具有不同全连接层大小的各种CNN模型来找出CNN全连接层大小与识别精度之间的关系。受先前修剪工作的启发，我们对CNN模型执行了独特的修剪方法，并将修剪性能与NN模型进行了比较。为了在CNN上获得更好的修剪性能，探索了角度阈值对修剪性能的影响。评估结果表明：对于完全连接的层大小，存在一个阈值，因此，当层大小增加时，如果层大小小于阈值，则识别精度会提高，如果层大小大于阈值，则下降；CNN上的修剪性能比NN上差；随着修剪角度阈值的增加，完全连接的图层大小和识别精度会降低。本文还显示，对于由MNIST数据集训练的CNN模型，它们具有手写数字识别能力，并且在完全连接的层大小为400的情况下具有最高的识别精度。此外，对于相同的MNIST数据集，CNN模型的效果要优于大型，深层的，已发表论文中的简单NN模型。