Пример #1
0
if __name__ == '__main__':
    parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
    parser.add_argument('--api_path',
                        type=str,
                        default=None,
                        help='The path to the NAS-Bench-201 benchmark file.')
    args = parser.parse_args()

    meta_file = Path(args.api_path)
    assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)

    api = API(str(meta_file))

    # This will show the results of the best architecture based on the validation set of each dataset.
    arch_index, accuracy = api.find_best('cifar10-valid', 'x-valid', None,
                                         None, False)
    print(
        'FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::'
    )
    print('arch-index={:5d}, arch={:}'.format(arch_index,
                                              api.arch(arch_index)))
    api.show(arch_index)
    print('')

    arch_index, accuracy = api.find_best('cifar100', 'x-valid', None, None,
                                         False)
    print(
        'FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::'
    )
    print('arch-index={:5d}, arch={:}'.format(arch_index,
                                              api.arch(arch_index)))
Пример #2
0
    parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
    parser.add_argument(
        "--api_path",
        type=str,
        default=None,
        help="The path to the NAS-Bench-201 benchmark file.",
    )
    args = parser.parse_args()

    meta_file = Path(args.api_path)
    assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)

    api = API(str(meta_file))

    # This will show the results of the best architecture based on the validation set of each dataset.
    arch_index, accuracy = api.find_best("cifar10-valid", "x-valid", None,
                                         None, False)
    print(
        "FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::"
    )
    print("arch-index={:5d}, arch={:}".format(arch_index,
                                              api.arch(arch_index)))
    api.show(arch_index)
    print("")

    arch_index, accuracy = api.find_best("cifar100", "x-valid", None, None,
                                         False)
    print(
        "FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::"
    )
    print("arch-index={:5d}, arch={:}".format(arch_index,
                                              api.arch(arch_index)))