minor

modules/deeplearning/icing_cnn.py

@@ -117,16 +117,26 @@ def build_residual_block(input, drop_rate, num_neurons, activation, block_name,
 
 class IcingIntensityNN:
     
-    def __init__(self, day_night='DAY', gpu_device=0, datapath=None):
+    def __init__(self, day_night='DAY', l1b_andor_l2='BOTH', gpu_device=0, datapath=None):
 
         if day_night == 'DAY':
             self.train_params_l1b = train_params_l1b_day
             self.train_params_l2 = train_params_l2_day
-            self.train_params = train_params_l1b_day + train_params_l2_day
+            if l1b_andor_l2 == 'BOTH':
+                self.train_params = train_params_l1b_day + train_params_l2_day
+            elif l1b_andor_l2 == 'l1b':
+                self.train_params = train_params_l1b_day
+            elif l1b_andor_l2 == 'l2':
+                self.train_params = train_params_l2_day
         else:
             self.train_params_l1b = train_params_l1b_night
             self.train_params_l2 = train_params_l2_night
-            self.train_params = train_params_l1b_night + train_params_l2_night
+            if l1b_andor_l2 == 'BOTH':
+                self.train_params = train_params_l1b_night + train_params_l2_night
+            elif l1b_andor_l2 == 'l1b':
+                self.train_params = train_params_l1b_night
+            elif l1b_andor_l2 == 'l2':
+                self.train_params = train_params_l2_night
 
         self.train_data = None
         self.train_label = None