def get_params()

in egg/zoo/channel/train.py [0:0]

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

def get_params(params):
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--n_features",
        type=int,
        default=10,
        help='Dimensionality of the "concept" space (default: 10)',
    )
    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(
        "--receiver_num_layers",
        type=int,
        default=1,
        help="Number hidden layers of receiver. Only in reinforce (default: 1)",
    )
    parser.add_argument(
        "--sender_num_layers",
        type=int,
        default=1,
        help="Number hidden layers of receiver. Only in reinforce (default: 1)",
    )
    parser.add_argument(
        "--receiver_num_heads",
        type=int,
        default=8,
        help="Number of attention heads for Transformer Receiver (default: 8)",
    )
    parser.add_argument(
        "--sender_num_heads",
        type=int,
        default=8,
        help="Number of self-attention heads for Transformer Sender (default: 8)",
    )
    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("--causal_sender", default=False, action="store_true")
    parser.add_argument("--causal_receiver", default=False, action="store_true")

    parser.add_argument(
        "--sender_generate_style",
        type=str,
        default="in-place",
        choices=["standard", "in-place"],
        help="How the next symbol is generated within the TransformerDecoder (default: in-place)",
    )

    parser.add_argument(
        "--sender_cell",
        type=str,
        default="rnn",
        help="Type of the cell used for Sender {rnn, gru, lstm, transformer} (default: rnn)",
    )
    parser.add_argument(
        "--receiver_cell",
        type=str,
        default="rnn",
        help="Type of the model used for Receiver {rnn, gru, lstm, transformer} (default: rnn)",
    )

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

    parser.add_argument(
        "--probs",
        type=str,
        default="uniform",
        help="Prior distribution over the concepts (default: uniform)",
    )
    parser.add_argument(
        "--length_cost",
        type=float,
        default=0.0,
        help="Penalty for the message length, each symbol would before <EOS> would be "
        "penalized by this cost (default: 0.0)",
    )
    parser.add_argument(
        "--name",
        type=str,
        default="model",
        help="Name for your checkpoint (default: model)",
    )
    parser.add_argument(
        "--early_stopping_thr",
        type=float,
        default=0.9999,
        help="Early stopping threshold on accuracy (default: 0.9999)",
    )

    args = core.init(parser, params)

    return args