Python Controller.default_arguments Examples

Example #1

def lstm_control(saveFreq=1110, saveto=None):
    parser = Controller.default_parser()
    parser.add_argument('--max-mb',
                        default=((5000 * 1998) / 10),
                        type=int,
                        required=False,
                        help='Maximum mini-batches to train upon in total.')
    parser.add_argument(
        '--patience',
        default=10,
        type=int,
        required=False,
        help='Maximum patience when failing to get better validation results.')
    parser.add_argument(
        '--valid-freq',
        default=370,
        type=int,
        required=False,
        help=
        'How often in mini-batches prediction function should get validated.')
    args = parser.parse_args()

    l = LSTMController(max_mb=args.max_mb,
                       patience=args.patience,
                       valid_freq=args.valid_freq,
                       default_args=Controller.default_arguments(args))

    print("Controller is ready")
    return l.serve()

Example #2

File: wavenet_controller.py Project: zhang-jian/WaveNet

def wavenet_control(saveFreq=1110, saveto=None):
    parser = Controller.default_parser()
    parser.add_argument('--max-mb',
                        default=((5000 * 1998) / 10),
                        type=int,
                        required=False,
                        help='Maximum mini-batches to train upon in total.')

    args = parser.parse_args()

    l = WaveNetController(max_mb=10000,
                          saveFreq=1000,
                          default_args=Controller.default_arguments(args))

    print("Controller is ready")
    return l.serve()

Example #3

File: lstm_controller.py Project: mila-udem/platoon

def lstm_control(saveFreq=1110, saveto=None):
    parser = Controller.default_parser()
    parser.add_argument('--max-mb', default=((5000 * 1998) / 10), type=int,
                        required=False, help='Maximum mini-batches to train upon in total.')
    parser.add_argument('--patience', default=10, type=int,
                        required=False, help='Maximum patience when failing to get better validation results.')
    parser.add_argument('--valid-freq', default=370, type=int,
                        required=False, help='How often in mini-batches prediction function should get validated.')
    args = parser.parse_args()

    l = LSTMController(max_mb=args.max_mb,
                       patience=args.patience,
                       valid_freq=args.valid_freq,
                       default_args=Controller.default_arguments(args))

    print("Controller is ready")
    return l.serve()

Example #4

File: batched_pixel_sum_controller.py Project: mila-udem/platoon

def spawn_controller():
    args = parse_arguments()

    mnist_path = "../data/mnist.pkl.gz"

    get_mnist(mnist_path)

    with gzip.open(mnist_path, 'rb') as f:
        kwargs = {}
        if six.PY3:
            kwargs['encoding'] = 'latin1'
        train_set, _, _ = cPickle.load(f, **kwargs)

    controller = BatchedPixelSumController(batch_port=args.batch_port,
                                           dataset=train_set[0],
                                           batch_size=args.batch_size,
                                           default_args=Controller.default_arguments(args))
    controller.start_batch_server()
    return controller.serve()

Example #5

def spawn_controller():
    args = parse_arguments()

    mnist_path = "../data/mnist.pkl.gz"

    get_mnist(mnist_path)

    with gzip.open(mnist_path, 'rb') as f:
        kwargs = {}
        if six.PY3:
            kwargs['encoding'] = 'latin1'
        train_set, _, _ = cPickle.load(f, **kwargs)

    controller = BatchedPixelSumController(
        batch_port=args.batch_port,
        dataset=train_set[0],
        batch_size=args.batch_size,
        default_args=Controller.default_arguments(args))
    controller.start_batch_server()
    return controller.serve()

Example #6

File: lstm_controller.py Project: zwjyyc/platoon

def lstm_control(saveFreq=1110, saveto=None):
    parser = Controller.default_parser()
    parser.add_argument('--seed',
                        default=1234,
                        type=int,
                        required=False,
                        help='Maximum mini-batches to train upon in total.')
    parser.add_argument(
        '--patience',
        default=10,
        type=int,
        required=False,
        help='Maximum patience when failing to get better validation results.')
    args = parser.parse_args()

    l = LSTMController(seed=args.seed,
                       patience=args.patience,
                       default_args=Controller.default_arguments(args))

    print("Controller is ready")
    return l.serve()