  Convolutional neural networks (CNNs) have gained widespread usage across
various fields such as weather forecasting, computer vision, autonomous
driving, and medical image analysis due to its exceptional ability to extract
spatial information, share parameters, and learn local features. However, the
practical implementation and commercialization of CNNs in these domains are
hindered by challenges related to model sizes, overfitting, and computational
time. To address these limitations, our study proposes a groundbreaking
approach that involves tensorizing the dense layers in the CNN to reduce model
size and computational time. Additionally, we incorporate attention layers into
the CNN and train it using Contrastive self-supervised learning to effectively
classify cloud information, which is crucial for accurate weather forecasting.
We elucidate the key characteristics of tensorized neural network (TNN),
including the data compression rate, accuracy, and computational speed. The
results indicate how TNN change their properties under the batch size setting.
