metavar="N",
help="Log training loss every log_interval batches.")
parser.add_argument("--num_epochs",
type=int,
default=20,
help="Number of epochs to train.")
parser.add_argument("--rnn_cell_sizes",
type=int,
nargs="+",
default=[256, 128],
help="List of sizes for each layer of the RNN.")
parser.add_argument("--batch_size",
type=int,
default=64,
help="Batch size for training and eval.")
parser.add_argument("--keep_probability",
type=float,
default=0.5,
help="Keep probability for dropout between layers.")
parser.add_argument("--learning_rate",
type=float,
default=0.01,
help="Learning rate to be used during training.")
parser.add_argument("--no_gpu",
action="store_true",
default=False,
help="Disables GPU usage even if a GPU is available.")
FLAGS, unparsed = parser.parse_known_args()
tfe.run(main=main, argv=[sys.argv[0]] + unparsed)
"--num_epochs", type=int, default=20, help="Number of epochs to train.")
parser.add_argument(
"--rnn_cell_sizes",
type=int,
nargs="+",
default=[256, 128],
help="List of sizes for each layer of the RNN.")
parser.add_argument(
"--batch_size",
type=int,
default=64,
help="Batch size for training and eval.")
parser.add_argument(
"--keep_probability",
type=float,
default=0.5,
help="Keep probability for dropout between layers.")
parser.add_argument(
"--learning_rate",
type=float,
default=0.01,
help="Learning rate to be used during training.")
parser.add_argument(
"--no_gpu",
action="store_true",
default=False,
help="Disables GPU usage even if a GPU is available.")
FLAGS, unparsed = parser.parse_known_args()
tfe.run(main=main, argv=[sys.argv[0]] + unparsed)
