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)