diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 018b2bf4411cb6f2042c5c085bcbbd3b845f0562..ddee109c313ba90346b6b90060a5157393a62c5a 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -47,11 +47,11 @@ f.close()
 mean_std_dct.update(mean_std_dct_l1b)
 mean_std_dct.update(mean_std_dct_l2)
 
-#params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
-#data_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom']
-params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cld_opd_dcomp']
+# params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
+# data_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom']
+params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
 data_params = ['temp_11_0um_nom']
-label_params = ['cld_opd_dcomp']
+label_params = ['cloud_fraction']
 
 
 DO_ZERO_OUT = False
@@ -67,8 +67,8 @@ x_64 = np.arange(64)
 y_64 = np.arange(64)
 x_134_2 = x_134[3:131:2]
 y_134_2 = y_134[3:131:2]
-#x_134_2 = x_134[2:133:2]
-#y_134_2 = y_134[2:133:2]
+# x_134_2 = x_134[2:133:2]
+# y_134_2 = y_134[2:133:2]
 t = np.arange(0, 64, 0.5)
 s = np.arange(0, 64, 0.5)
 
@@ -418,7 +418,7 @@ class SRCNN:
         self.loss = tf.keras.losses.MeanSquaredError()  # Regression
 
         # decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
-        initial_learning_rate = 0.001
+        initial_learning_rate = 0.002
         decay_rate = 0.95
         steps_per_epoch = int(self.num_data_samples/BATCH_SIZE)  # one epoch
         decay_steps = int(steps_per_epoch)