def test_metrics() -> None:
random.seed(10)
dp = DataProvider.load_from_folder(dataset_folder)
nn = NeuralNet(sizes=[784, 128, 10], epochs=10)
nn.train(dp.get_train_x(), dp.get_hot_encoded_train_y(),
dp.get_test_x(), dp.get_hot_encoded_test_y())
scores = nn.compute_metrics(dp.get_test_x(),
dp.get_hot_encoded_test_y())
print('precyzja_makro: ', scores['precyzja_makro'])
print('czulosc_makro: ', scores['czulosc_makro'])
print('dokladnosc: ', scores['dokladnosc'])
print('raport: ', scores['raport'])
print('macierz_bledow: ', scores['macierz_bledow'])
