def get_params()

in egg/zoo/external_game/game.py [0:0]

• 119 lines of code
• 1 McCabe index (conditional complexity)

def get_params():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--train_data", type=str, default=None, help="Path to the train data"
    )
    parser.add_argument(
        "--validation_data", type=str, default=None, help="Path to the validation data"
    )
    parser.add_argument(
        "--dump_data",
        type=str,
        default=None,
        help="Path to the data for which to produce output information",
    )
    parser.add_argument(
        "--dump_output",
        type=str,
        default=None,
        help="Path for dumping output information",
    )

    parser.add_argument(
        "--batches_per_epoch",
        type=int,
        default=1000,
        help="Number of batches per epoch (default: 1000)",
    )

    parser.add_argument(
        "--sender_hidden",
        type=int,
        default=10,
        help="Size of the hidden layer of Sender (default: 10)",
    )
    parser.add_argument(
        "--receiver_hidden",
        type=int,
        default=10,
        help="Size of the hidden layer of Receiver (default: 10)",
    )

    parser.add_argument(
        "--sender_embedding",
        type=int,
        default=10,
        help="Dimensionality of the embedding hidden layer for Sender (default: 10)",
    )
    parser.add_argument(
        "--receiver_embedding",
        type=int,
        default=10,
        help="Dimensionality of the embedding hidden layer for Receiver (default: 10)",
    )

    parser.add_argument(
        "--sender_cell",
        type=str,
        default="rnn",
        help="Type of the cell used for Sender {rnn, gru, lstm} (default: rnn)",
    )
    parser.add_argument(
        "--receiver_cell",
        type=str,
        default="rnn",
        help="Type of the cell used for Receiver {rnn, gru, lstm} (default: rnn)",
    )
    parser.add_argument(
        "--sender_layers",
        type=int,
        default=1,
        help="Number of layers in Sender's RNN (default: 1)",
    )
    parser.add_argument(
        "--receiver_layers",
        type=int,
        default=1,
        help="Number of layers in Receiver's RNN (default: 1)",
    )

    parser.add_argument(
        "--sender_entropy_coeff",
        type=float,
        default=1e-2,
        help="The entropy regularisation coefficient for Sender (default: 1e-2)",
    )
    parser.add_argument(
        "--receiver_entropy_coeff",
        type=float,
        default=1e-2,
        help="The entropy regularisation coefficient for Receiver (default: 1e-2)",
    )

    parser.add_argument(
        "--sender_lr",
        type=float,
        default=1e-1,
        help="Learning rate for Sender's parameters (default: 1e-1)",
    )
    parser.add_argument(
        "--receiver_lr",
        type=float,
        default=1e-1,
        help="Learning rate for Receiver's parameters (default: 1e-1)",
    )
    parser.add_argument(
        "--temperature",
        type=float,
        default=1.0,
        help="GS temperature for the sender (default: 1.0)",
    )
    parser.add_argument(
        "--train_mode",
        type=str,
        default="gs",
        help="Selects whether GumbelSoftmax or Reinforce is used" "(default: gs)",
    )

    parser.add_argument(
        "--n_classes",
        type=int,
        default=None,
        help="Number of classes for Receiver to output. If not set, is automatically deduced from "
        "the training set",
    )

    args = core.init(parser)
    return args