main.py

import numpy as np

from nn import nn


def run():
    # dataset
    data = np.array([
        [[1, 1, 1],
         [1, 0, 1],
         [1, 1, 1]],

        [[0, 1, 0],
         [0, 1, 0],
         [0, 1, 0]],

        [[1, 0, 1],
         [1, 1, 1],
         [0, 0, 1]],

        [[1, 1, 1],
         [0, 0, 1],
         [0, 0, 1]]
    ])

    normalization = 10

    data_annotation_target = np.array([
        0, 1, 4, 7
    ]) / normalization

    network = nn(3 * 3, 9)
    network.train(data, data_annotation_target, "./weights.json")

    network.read_from_file("weights.json")
    for i, _ in enumerate(data):
        predict = round(network.predict(data[i]) * normalization)
        print(f"predict: {predict}, result: {predict == data_annotation_target[i] * normalization}")


if __name__ == "__main__":
    run()