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()
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()
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()
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()
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()
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()