def add_fit_args()

in image_classification/common/fit.py [0:0]

• 58 lines of code
• 2 McCabe index (conditional complexity)

def add_fit_args(parser):
    """
    parser : argparse.ArgumentParser
    return a parser added with args required by fit
    """
    train = parser.add_argument_group('Training', 'model training')
    train.add_argument('--network', type=str,
                       help='the neural network to use')
    train.add_argument('--num-layers', type=int,
                       help='number of layers in the neural network, \
                             required by some networks such as resnet')
    train.add_argument('--gpus', type=str,
                       help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu')
    train.add_argument('--kv-store', type=str, default='device',
                       help='key-value store type')
    train.add_argument('--num-epochs', type=int, default=100,
                       help='max num of epochs')
    train.add_argument('--lr', type=float, default=0.1,
                       help='initial learning rate')
    train.add_argument('--lr-factor', type=float, default=0.1,
                       help='the ratio to reduce lr on each step')
    train.add_argument('--lr-step-epochs', type=str,
                       help='the epochs to reduce the lr, e.g. 30,60')
    train.add_argument('--initializer', type=str, default='default',
                       help='the initializer type')
    train.add_argument('--optimizer', type=str, default='sgd',
                       help='the optimizer type')
    train.add_argument('--mom', type=float, default=0.9,
                       help='momentum for sgd')
    train.add_argument('--wd', type=float, default=0.0001,
                       help='weight decay for sgd')
    train.add_argument('--batch-size', type=int, default=128,
                       help='the batch size')
    train.add_argument('--disp-batches', type=int, default=20,
                       help='show progress for every n batches')
    train.add_argument('--model-prefix', type=str,
                       help='model prefix')
    train.add_argument('--save-final-model-only', action='store_true', default=False)
    parser.add_argument('--monitor', dest='monitor', type=int, default=0,
                        help='log network parameters every N iters if larger than 0')
    train.add_argument('--load-epoch', type=int,
                       help='load the model on an epoch using the model-load-prefix')
    train.add_argument('--top-k', type=int, default=0,
                       help='report the top-k accuracy. 0 means no report.')
    train.add_argument('--loss', type=str, default='',
                       help='show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss')
    train.add_argument('--test-io', type=int, default=0,
                       help='1 means test reading speed without training')
    train.add_argument('--dtype', type=str, default='float32',
                       help='precision: float32 or float16')
    train.add_argument('--gc-type', type=str, default='none',
                       help='type of gradient compression to use, \
                             takes `2bit` or `none` for now')
    train.add_argument('--gc-threshold', type=float, default=0.5,
                       help='threshold for 2bit gradient compression')
    # additional parameters for large batch sgd
    train.add_argument('--macrobatch-size', type=int, default=0,
                       help='distributed effective batch size')
    train.add_argument('--warmup-epochs', type=int, default=5,
                       help='the epochs to ramp-up lr to scaled large-batch value')
    train.add_argument('--warmup-strategy', type=str, default='linear',
                       help='the ramping-up strategy for large batch sgd')
    return train