  Among the machine learning approaches applied in computer vision,
Convolutional Neural Network (CNN) is widely used in the field of image
recognition. However, although existing CNN models have been proven to be
efficient, it is not easy to find a network architecture with better
performance. Some studies choose to optimize the network architecture, while
others chose to optimize the hyperparameters, such as the number and size of
convolutional kernels, convolutional strides, pooling size, etc. Most of them
are designed manually, which requires relevant expertise and takes a lot of
time. Therefore, this study proposes the idea of applying Simplified Swarm
Optimization (SSO) on the hyperparameter optimization of LeNet models while
using MNIST, Fashion MNIST, and Cifar10 as validation. The experimental results
show that the proposed algorithm has higher accuracy than the original LeNet
model, and it only takes a very short time to find a better hyperparameter
configuration after training. In addition, we also analyze the output shape of
the feature map after each layer, and surprisingly, the results were mostly
rectangular. The contribution of the study is to provide users with a simpler
way to get better results with the existing model., and this study can also be
applied to other CNN architectures.
