CNN_Transformer

Code for paper: A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems

Runs (Examples)

To train the proposed model on TSP20, run the following:

$ train.py --exp_name Experiment-Name -n 20 --bsz 512 --gpu_id 0 --embedding conv --nb_neighbors 10 --kernel_size 11 --segm_len 5 --batchnorm

To test the trained model on TSP20 random dataset, run the following:

$ test.py -n 20 --embedding conv --nb_neighbors 10 --kernel_size 11 --gpu_id 0 --ckpt_file Experiment-Name-on-Train.pkl --exp_tag Result-File-Name --segm_len 5 --greedy --beamsearch --batchnorm

OR you can instead run shell scripts for train and test respectively. You may modify the .sh files uploaded on this repository if necessary.

$ sh train_sh/train-sh-filename.sh
$ sh test_sh/test-sh-filename.sh

You can use test_tsplib.ipynb to test the trained model on the TSPLIB dataset

The pretrained model checkpoints are uploaded in the checkpoint directory

Requirements

• pytorch 1.12.1
• numpy 1.22.3
• pandas 1.4.3
• python 3.10.5
• matplotlib 3.7.1

Acknowledgement

The codes here are based on the work of Xavier Bresson's TSP Transformer

Cite

@article{jung2023lightweight,
  title={A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems},
  author={Jung, Minseop and Lee, Jaeseung and Kim, Jibum},
      year={2023},
      eprint={2305.01883},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}