diff --git a/modules/deeplearning/cnn_cld_frac_mod_res.py b/modules/deeplearning/cnn_cld_frac_mod_res.py
index 3a115371f46106b5e8949b724c579df7b97724e7..6ef8c920e883e8e35ea6dc49e93dd0230c1fdfe1 100644
--- a/modules/deeplearning/cnn_cld_frac_mod_res.py
+++ b/modules/deeplearning/cnn_cld_frac_mod_res.py
@@ -89,8 +89,8 @@ if KERNEL_SIZE == 3:
     y_k = slice(1, N*128 + 3)
     # x_128 = slice(3, N*128 + 3)
     # y_128 = slice(3, N*128 + 3)
-    x_128 = slice(2, N*128 + 2)
-    y_128 = slice(2, N*128 + 2)
+    x_128 = slice(4, N*128 + 4)
+    y_128 = slice(4, N*128 + 4)
 elif KERNEL_SIZE == 5:
     slc_x = slice(3, 135)
     slc_y = slice(3, 135)
@@ -361,7 +361,7 @@ class SRCNN:
                 tmp = tmp[:, slc_y_2, slc_x_2]
             else:  # Half res upsampled to full res:
                 tmp = get_grid_cell_mean(tmp)
-                tmp = tmp[:, 0:66, 0:66]
+                tmp = tmp[:, 1:67, 1:67]
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
@@ -376,9 +376,9 @@ class SRCNN:
             hi = normalize(hi, param, mean_std_dct)
             avg = normalize(avg, param, mean_std_dct)
 
-            data_norm.append(lo[:, 0:66, 0:66])
-            data_norm.append(hi[:, 0:66, 0:66])
-            data_norm.append(avg[:, 0:66, 0:66])
+            data_norm.append(lo[:, 1:67, 1:67])
+            data_norm.append(hi[:, 1:67, 1:67])
+            data_norm.append(avg[:, 1:67, 1:67])
             # data_norm.append(std[:, 0:66, 0:66])
         # ---------------------------------------------------
         tmp = input_data[:, label_idx, :, :]
@@ -387,7 +387,7 @@ class SRCNN:
             tmp = tmp[:, slc_y_2, slc_x_2]
         else:  # Half res upsampled to full res:
             tmp = get_grid_cell_mean(tmp)
-            tmp = tmp[:, 0:66, 0:66]
+            tmp = tmp[:, 1:67, 1:67]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
         data_norm.append(tmp)