  Convolutional neural network (CNN) is one of the most frequently used deep
learning techniques. Various forms of models have been proposed and im-proved
for learning at CNN. When learning with CNN, it is necessary to determine the
optimal hyperparameters. However, the number of hyperparameters is so large
that it is difficult to do it manually, so much research has been done on
automation. A method that uses metaheuristic algorithms is attracting attention
in research on hyperparameter optimization. Metaheuristic algorithms are
naturally inspired and include evolution strategies, genetic algorithms,
antcolony optimization and particle swarm optimization. In particular, particle
swarm optimization converges faster than genetic algorithms, and various models
have been proposed. In this paper, we pro-pose CNN hyperparameter optimization
with linearly decreasing weight particle swarm optimization (LDWPSO). In the
experiment, the MNIST data set and CIFAR-10 data set, which are often used as
benchmark data sets, are used. By opti-mizing CNN hyperparameters with LDWPSO,
learning the MNIST and CIFAR-10 datasets, we compare the accuracy with a
standard CNN based on LeNet-5. As a result, when using the MNIST dataset, the
baseline CNN is 94.02% at the 5th epoch, compared to 98.95% for LDWPSO CNN,
which improves accuracy. When using the CIFAR-10 dataset, the Baseline CNN is
28.07% at the 10th epoch, compared to 69.37% for the LDWPSO CNN, which greatly
improves accuracy. This paper is presented at the 36th Annual Conference of the
Japanese Society for Artificial In-telligence. The final version is available
at the following URL: https://doi.org/10.11517/pjsai.JSAI2022.0_2S4IS2b03
