diff --git a/modules/deeplearning/icing.py b/modules/deeplearning/icing.py
index 4c2db1300db70c92c84e0d5a80dbb6a074dc6535..37947e123f37a7a81d1ab7d6c74b1e459b5e1295 100644
--- a/modules/deeplearning/icing.py
+++ b/modules/deeplearning/icing.py
@@ -350,19 +350,21 @@ class IcingIntensityNN:
 
         fac = 1
 
-        fc = build_residual_block(flat, drop_rate, fac*n_hidden, activation, 'Residual_Block_1', doBatchNorm=False)
+        fc = build_residual_block(flat, drop_rate, fac*n_hidden, activation, 'Residual_Block_1', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_2', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_2', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_3', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_3', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4', doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5', doBatchNorm=False)
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5', doBatchNorm=True)
+
+        fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5', doBatchNorm=True)
 
         fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc)
         fc = tf.keras.layers.BatchNormalization()(fc)