diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 3d041bbbce0a4e7b9b6fa1adda4bdece7da8b976..0e4d6db76c1ef2576391c3b5596ed3361b41baa7 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -241,8 +241,8 @@ class IcingIntensityFCN:
         self.X_img = tf.keras.Input(shape=(None, None, n_chans))
 
         self.inputs.append(self.X_img)
-        self.inputs.append(tf.keras.Input(shape=(None, None, 5)))
-        #self.inputs.append(tf.keras.Input(shape=(None, None, 3)))
+        #self.inputs.append(tf.keras.Input(shape=(None, None, 5)))
+        self.inputs.append(tf.keras.Input(shape=(None, None, 3)))
 
         self.flight_level = 0
 
@@ -362,18 +362,18 @@ class IcingIntensityFCN:
 
         nda = h5f[param][nd_idxs,]
 
-        nda[np.logical_and(nda >= 0, nda < 2000)] = 0
-        nda[np.logical_and(nda >= 2000, nda < 4000)] = 1
-        nda[np.logical_and(nda >= 4000, nda < 6000)] = 2
-        nda[np.logical_and(nda >= 6000, nda < 8000)] = 3
-        nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
-
         # nda[np.logical_and(nda >= 0, nda < 2000)] = 0
-        # nda[np.logical_and(nda >= 2000, nda < 8000)] = 1
-        # nda[np.logical_and(nda >= 8000, nda < 15000)] = 2
+        # nda[np.logical_and(nda >= 2000, nda < 4000)] = 1
+        # nda[np.logical_and(nda >= 4000, nda < 6000)] = 2
+        # nda[np.logical_and(nda >= 6000, nda < 8000)] = 3
+        # nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
 
-        nda = tf.one_hot(nda, 5).numpy()
-        # nda = tf.one_hot(nda, 3).numpy()
+        nda[np.logical_and(nda >= 0, nda < 3000)] = 0
+        nda[np.logical_and(nda >= 3000, nda < 6000)] = 1
+        nda[np.logical_and(nda >= 6000, nda < 15000)] = 2
+
+        # nda = tf.one_hot(nda, 5).numpy()
+        nda = tf.one_hot(nda, 3).numpy()
         nda = np.expand_dims(nda, axis=1)
         nda = np.expand_dims(nda, axis=1)