Overview

Sudoku Net V1 model solves sudoku puzzles. It was developed to see the performance of machine learning applications on solving sudokus. This model is trained on 1 million Sudoku games dataset provided on kaggle by Kyubyong Park

Usage

The Huggingface Hub architecture currently does not support inference from this model since it is unable to determine this model’s pipeline type. Thus, it is recommended to use as easy-to-use PyPI package ai-sudoku-solver. The package can also be found on GitHub

pip install ai-sudoku-solver

Instantiate a SudokuSolver object

from ai_sudoku_solver import SudokuSolver

solver = SudokuSolver("Ritvik19/sudoku-net-v1")

Call the model on your puzzles

puzzle = np.array([[
    [0, 0, 4, 3, 0, 0, 2, 0, 9],
    [0, 0, 5, 0, 0, 9, 0, 0, 1],
    [0, 7, 0, 0, 6, 0, 0, 4, 3],
    [0, 0, 6, 0, 0, 2, 0, 8, 7],
    [1, 9, 0, 0, 0, 7, 4, 0, 0],
    [0, 5, 0, 0, 8, 3, 0, 0, 0],
    [6, 0, 0, 0, 0, 0, 1, 0, 5],
    [0, 0, 3, 5, 0, 8, 6, 9, 0],
    [0, 4, 2, 9, 1, 0, 3, 0, 0]
]])
solution = solver(puzzle)
# array([[
#     [8, 6, 4, 3, 7, 1, 2, 5, 9],
#     [3, 2, 5, 8, 4, 9, 7, 6, 1],
#     [9, 7, 1, 2, 6, 5, 8, 4, 3],
#     [4, 3, 6, 1, 9, 2, 5, 8, 7],
#     [1, 9, 8, 6, 5, 7, 4, 3, 2],
#     [2, 5, 7, 4, 8, 3, 9, 1, 6],
#     [6, 8, 9, 7, 3, 4, 1, 2, 5],
#     [7, 1, 3, 5, 2, 8, 6, 9, 4],
#     [5, 4, 2, 9, 1, 6, 3, 7, 8]
# ]])