wehub-resource-sync
66 lines
1.4 KiB
Markdown
66 lines
1.4 KiB
Markdown
# Recurrent Relational Network (RRN)
|
|
|
|
* Paper link: https://arxiv.org/abs/1711.08028
|
|
* Author's code repo: https://github.com/rasmusbergpalm/recurrent-relational-networks
|
|
|
|
## Dependencies
|
|
|
|
* PyTorch 1.0+
|
|
* DGL 0.5+
|
|
|
|
## Codes
|
|
|
|
The folder contains a DGL implementation of Recurrent Relational Network, and its
|
|
application on sudoku solving.
|
|
|
|
## Usage
|
|
|
|
- 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:
|
|
|
|
```python
|
|
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]]
|
|
'''
|
|
```
|