help="the momentum.",
type=float,
default=0.9)
parser.add_argument("-nl",
"--num_layers",
help="Number of LSTM hidden layers.",
type=int,
default=2)
parser.add_argument("-hu",
"--hidden_units",
help="Number of units in LSTM hidden layer.",
type=int,
default=128)
args = parser.parse_args()
redis_logger_handler.logging_setup(args.redis)
logging.info("===== Start")
images, labels = parseFile(args.images, args.labels, args.format)
dataRDD = images.zip(labels)
args.train_size = labels.count() - args.test_size
logging.info(args)
cluster = TFCluster.run(sc, lstm_ctc_ocr_dist.map_fun, args, args.cluster_size,
num_ps, args.tensorboard, TFCluster.InputMode.SPARK)
if args.mode == "train":
cluster.train(dataRDD, args.epochs)
else:
labelRDD = cluster.inference(dataRDD)
labelRDD.saveAsTextFile(args.output)
def print_log(args, ctx):
import logging
import redis_logger_handler
redis_logger_handler.logging_setup(args.redis)
logging.info('print log..............................')