- Paper link: https://arxiv.org/abs/1711.08028
- Author's code repo: https://github.com/rasmusbergpalm/recurrent-relational-networks
- PyTorch 1.0+
- DGL 0.5+
The folder contains a DGL implementation of Recurrent Relational Network, and its application on sudoku solving.
- To train the RRN for sudoku, run the following
python3 train_sudoku.py --output_dir out/ --do_train
- Test with specified aggregation steps:
python3 train_sudoku.py --output_dir out/ --do_eval --steps 64
Test accuracy (puzzle-level):
| 32 steps | 64 steps | |
|---|---|---|
| Paper | 94.1 | 96.6 |
| DGL | 95.3 | 98.9 |
- To use the trained model for solving sudoku, follow the example bellow:
from sudoku_solver import solve_sudoku
q = [[9, 7, 0, 4, 0, 2, 0, 5, 3],
[0, 4, 6, 0, 9, 0, 0, 0, 0],
[0, 0, 8, 6, 0, 1, 4, 0, 7],
[0, 0, 0, 0, 0, 3, 5, 0, 0],
[7, 6, 0, 0, 0, 0, 0, 8, 2],
[0, 0, 2, 8, 0, 0, 0, 0, 0],
[6, 0, 5, 1, 0, 7, 2, 0, 0],
[0, 0, 0, 0, 6, 0, 7, 4, 0],
[4, 3, 0, 2, 0, 9, 0, 6, 1]
]
answer = solve_sudoku(q)
print(answer)
'''
[[9 7 1 4 8 2 6 5 3]
[3 4 6 7 9 5 1 2 8]
[2 5 8 6 3 1 4 9 7]
[8 1 4 9 2 3 5 7 6]
[7 6 3 5 1 4 9 8 2]
[5 9 2 8 7 6 3 1 4]
[6 8 5 1 4 7 2 3 9]
[1 2 9 3 6 8 7 4 5]
[4 3 7 2 5 9 8 6 1]]
'''