diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 899f68dc9a610d5a0d91d121571fc9454ffbbfee..8a43aea38b2eae0be74b8c18d9759ce7330db27d 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -230,7 +230,8 @@ class SRCNN:
             tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
             data_norm.append(tmp)
         # --------
-        idx = params.index('refl_0_65um_nom')
+        param = 'refl_0_65um_nom'
+        idx = params.index(param)
         tmp = input_data[:, idx, 3:131:2, 3:131:2]
         # tmp = input_data[:, idx, 3:131, 3:131]
         tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
@@ -239,7 +240,7 @@ class SRCNN:
         # --------
         tmp = input_data[:, label_idx, 3:131:2, 3:131:2]
         if label_param != 'cloud_fraction':
-            tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
+            tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         else:
             tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
@@ -247,7 +248,7 @@ class SRCNN:
         # ---------
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
-
+        # -----------------------------------------------------
         # label = input_data[:, label_idx, 3:131:2, 3:131:2]
         label = input_data[:, label_idx, 3:131, 3:131]
         if label_param != 'cloud_fraction':