  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.
