Use learning_rate and learning_rate_min

This keeps things consisten with CANDLE conventions, and removes
potentially confusing parameters.

Author: yngtodd

examples/darts/advanced/default_model.txt

@@ -4,14 +4,12 @@ data_url = 'ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/'
 savepath = './results'
 log_interval = 10
 train_data = 'top_21_auc_1fold.uno.h5'
-learning_rate = 0.01
+learning_rate = 0.025
 learning_rate_min = 0.001
 momentum = 0.9
 weight_decay = 3e-4
 grad_clip = 5 
 batch_size = 100
 epochs = 10
 seed = 13
-lr = 0.025
-lr_min = 0.001
 

examples/darts/advanced/example.py

@@ -77,15 +77,15 @@ def run(params):
 
     optimizer = optim.SGD(
         model.parameters(),
-        args.lr,
+        args.learning_rate,
         momentum=args.momentum,
         weight_decay=args.weight_decay
     )
 
     scheduler = optim.lr_scheduler.CosineAnnealingLR(
         optimizer,
         float(args.epochs),
-        eta_min=args.lr_min
+        eta_min=args.learning_rate_min
     )
 
     train_meter = darts.EpochMeter(tasks, 'train')

examples/darts/uno/default_model.txt

@@ -4,14 +4,12 @@ data_url = 'http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/'
 savepath = '.'
 log_interval = 10
 train_data = 'top_21_auc_1fold.uno.h5'
-learning_rate = 0.01
+learning_rate = 0.025
 learning_rate_min = 0.001
 momentum = 0.9
 weight_decay = 3e-4
 grad_clip = 5 
 batch_size = 100
 epochs = 10
 seed = 13
-lr = 0.025
-lr_min = 0.001
 

examples/darts/uno/uno_example.py

@@ -56,15 +56,15 @@ def run(params):
 
     optimizer = optim.SGD(
         model.parameters(),
-        args.lr,
+        args.learning_rate,
         momentum=args.momentum,
         weight_decay=args.weight_decay
     )
 
     scheduler = optim.lr_scheduler.CosineAnnealingLR(
         optimizer,
         float(args.epochs),
-        eta_min=args.lr_min
+        eta_min=args.learning_rate_min
     )
 
     train_meter = darts.EpochMeter(tasks, 'train')