parser.add_argument('--limit',type=int,default=100) parser.add_argument('--adv',type=str,default=None) parser.add_argument('--train_baseline',action='store_true') args = parser.parse_args() import extra_vars from subtlenet.models import particles as train from os import path train.NEPOCH = args.nepoch train.VERSION = str(args.version) + '_Adam' #train.OPTIMIZER = 'RMSprop' data, dims = train.instantiate(args.trunc, args.limit) clf_gen = train.setup_data(data) adv_gen = train.setup_adv_data(data) if args.adv == 'emd': opts = { 'loss' : train.emd, 'scale' : 0.1, 'w_clf' : 0.001, 'w_adv' : 100, } elif args.adv == 'mse': opts = { 'loss' : args.adv, 'scale' : 0.03, 'w_clf' : 0.001, 'w_adv' : 0.1,
parser.add_argument('--window',action='store_true') parser.add_argument('--reshape',action='store_true') parser.add_argument('--suffix',type=str,default='') args = parser.parse_args() import extra_vars from subtlenet.models import particles as train from os import path from sys import exit train.NEPOCH = args.nepoch train.VERSION = args.version data, dims = train.instantiate(args.trunc, args.limit) clf_gen = train.setup_data(data) adv_gen = train.setup_adv_data(data, window=args.window, reshape=args.reshape) if args.adv == 'emd': opts = { 'loss' : train.emd, 'scale' : 0.1, 'w_clf' : 0.001, 'w_adv' : 100, } elif args.adv == 'mse': opts = { 'loss' : args.adv, 'scale' : 0.03, 'w_clf' : 0.001, 'w_adv' : 0.1,