|
- from .base_options import BaseOptions
-
-
- class TrainOptions(BaseOptions):
- def initialize(self):
- BaseOptions.initialize(self)
- self.parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen')
- self.parser.add_argument('--update_html_freq', type=int, default=4000, help='frequency of saving training results to html')
- self.parser.add_argument('--print_freq', type=int, default=200, help='frequency of showing training results on console')
- self.parser.add_argument('--save_latest_freq', type=int, default=10000, help='frequency of saving the latest results')
- self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')
- self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
- self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
- self.parser.add_argument('--niter', type=int, default=30, help='# of iter at starting learning rate')
- self.parser.add_argument('--niter_decay', type=int, default=30, help='# of iter to linearly decay learning rate to zero')
- self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results')
- self.parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
- self.isTrain = True
|