  Model compression aims to deploy deep neural networks (DNN) on mobile devices
with limited computing and storage resources. However, most of the existing
model compression methods rely on manually defined rules, which require domain
expertise. DNNs are essentially computational graphs, which contain rich
structural information. In this paper, we aim to find a suitable compression
policy from DNNs' structural information. We propose an automatic graph
encoder-decoder model compression (AGMC) method combined with graph neural
networks (GNN) and reinforcement learning (RL). We model the target DNN as a
graph and use GNN to learn the DNN's embeddings automatically. We compared our
method with rule-based DNN embedding model compression methods to show the
effectiveness of our method. Results show that our learning-based DNN embedding
achieves better performance and a higher compression ratio with fewer search
steps. We evaluated our method on over-parameterized and mobile-friendly DNNs
and compared our method with handcrafted and learning-based model compression
approaches. On over parameterized DNNs, such as ResNet-56, our method
outperformed handcrafted and learning-based methods with $4.36\%$ and $2.56\%$
higher accuracy, respectively. Furthermore, on MobileNet-v2, we achieved a
higher compression ratio than state-of-the-art methods with just $0.93\%$
accuracy loss.
