def add_arguments(parser):
parser = LossParams.add_arguments(parser)
parser.add_argument(
"--optimizer",
default="Adam",
choices=optimizer.OPTIMIZER_NAMES,
help="optimizer to train the network",
)
parser.add_argument(
"--val_epoch_freq",
type=int,
default=1,
help="validation epoch frequency.",
)
parser.add_argument("--learning_rate", type=float, default=0,
help="Learning rate for the training. If <= 0 it will be set"
" automatically to the default for the specified model adapter.")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--num_epochs", type=int, default=20)
parser.add_argument("--log_dir", help="folder to log tensorboard summary")
parser.add_argument('--display_freq', type=int, default=100,
help='frequency of showing training results on screen')
parser.add_argument('--print_freq', type=int, default=1,
help='frequency of showing training results on console')
parser.add_argument('--save_epoch_freq', type=int, default=1,
help='frequency of saving checkpoints at the end of epochs')
return parser
def make_tag(params):
return (
LossParams.make_str(params)
+ f"_LR{params.learning_rate}"
+ f"_BS{params.batch_size}"
+ f"_O{params.optimizer.lower()}"
)