Update LFNA

exps/LFNA/lfna.py

@@ -94,7 +94,10 @@ def main(args):
     )
 
     optimizer = torch.optim.Adam(
-        meta_model.parameters(), lr=args.init_lr, weight_decay=1e-5, amsgrad=True
+        meta_model.parameters(),
+        lr=args.init_lr,
+        weight_decay=args.weight_decay,
+        amsgrad=True,
     )
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
@@ -137,7 +140,7 @@ def main(args):
         )
         success, best_score = meta_model.save_best(-loss_meter.avg)
         if success:
-            logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
+            logger.log("Achieve the best with best-score = {:.5f}".format(best_score))
             last_success_epoch = iepoch
             save_checkpoint(
                 {
@@ -262,6 +265,12 @@ if __name__ == "__main__":
         default=0.005,
         help="The initial learning rate for the optimizer (default is Adam)",
     )
+    parser.add_argument(
+        "--weight_decay",
+        type=float,
+        default=0.00001,
+        help="The weight decay for the optimizer (default is Adam)",
+    )
     parser.add_argument(
         "--meta_batch",
         type=int,
@@ -274,11 +283,11 @@ if __name__ == "__main__":
         default=5,
         help="Enlarge the #iterations for an epoch",
     )
-    parser.add_argument("--epochs", type=int, default=1000, help="The total #epochs.")
+    parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
     parser.add_argument(
         "--early_stop_thresh",
         type=int,
-        default=25,
+        default=100,
         help="The maximum epochs for early stop.",
     )
     parser.add_argument(
@@ -299,11 +308,13 @@ if __name__ == "__main__":
     if args.rand_seed is None or args.rand_seed < 0:
         args.rand_seed = random.randint(1, 100000)
     assert args.save_dir is not None, "The save dir argument can not be None"
-    args.save_dir = "{:}-d{:}_{:}_{:}-e{:}-env{:}".format(
+    args.save_dir = "{:}-d{:}_{:}_{:}-lr{:}-wd{:}-e{:}-env{:}".format(
         args.save_dir,
         args.hidden_dim,
         args.layer_dim,
         args.time_dim,
+        args.init_lr,
+        args.weight_decay,
         args.epochs,
         args.env_version,
     )