diff --git a/keras_contrib/layers/normalization.py b/keras_contrib/layers/normalization.py
index a6f4dcc..eda985a 100644
--- a/keras_contrib/layers/normalization.py
+++ b/keras_contrib/layers/normalization.py
@@ -1,6 +1,7 @@
 from keras.engine import Layer, InputSpec
 from .. import initializers, regularizers
 from .. import backend as K
+from keras.utils.generic_utils import get_custom_objects
 
 import numpy as np
 
@@ -69,7 +70,7 @@ class BatchRenormalization(Layer):
     """
 
     def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,
-                 r_max_val=3., d_max_val=5., t_delta=1., weights=None, beta_init='zero',
+                 r_max_value=3., d_max_value=5., t_delta=1., weights=None, beta_init='zero',
                  gamma_init='one', gamma_regularizer=None, beta_regularizer=None,
                  **kwargs):
         self.supports_masking = True
@@ -82,8 +83,8 @@ class BatchRenormalization(Layer):
         self.gamma_regularizer = regularizers.get(gamma_regularizer)
         self.beta_regularizer = regularizers.get(beta_regularizer)
         self.initial_weights = weights
-        self.r_max_value = r_max_val
-        self.d_max_value = d_max_val
+        self.r_max_value = r_max_value
+        self.d_max_value = d_max_value
         self.t_delta = t_delta
         if self.mode == 0:
             self.uses_learning_phase = True
@@ -133,13 +134,13 @@ class BatchRenormalization(Layer):
             mean_batch, var_batch = K.moments(x, reduction_axes, shift=None, keep_dims=False)
             std_batch = (K.sqrt(var_batch + self.epsilon))
 
-            r_max_val = K.get_value(self.r_max)
+            r_max_value = K.get_value(self.r_max)
             r = std_batch / (K.sqrt(self.running_std + self.epsilon))
-            r = K.stop_gradient(K.clip(r, 1 / r_max_val, r_max_val))
+            r = K.stop_gradient(K.clip(r, 1 / r_max_value, r_max_value))
 
-            d_max_val = K.get_value(self.d_max)
+            d_max_value = K.get_value(self.d_max)
             d = (mean_batch - self.running_mean) / K.sqrt(self.running_std + self.epsilon)
-            d = K.stop_gradient(K.clip(d, -d_max_val, d_max_val))
+            d = K.stop_gradient(K.clip(d, -d_max_value, d_max_value))
 
             if sorted(reduction_axes) == range(K.ndim(x))[:-1]:
                 x_normed_batch = (x - mean_batch) / std_batch
@@ -197,13 +198,13 @@ class BatchRenormalization(Layer):
             std = K.sqrt(K.var(x, axis=self.axis, keepdims=True) + self.epsilon)
             x_normed_batch = (x - m) / (std + self.epsilon)
 
-            r_max_val = K.get_value(self.r_max)
+            r_max_value = K.get_value(self.r_max)
             r = std / (self.running_std + self.epsilon)
-            r = K.stop_gradient(K.clip(r, 1 / r_max_val, r_max_val))
+            r = K.stop_gradient(K.clip(r, 1 / r_max_value, r_max_value))
 
-            d_max_val = K.get_value(self.d_max)
+            d_max_value = K.get_value(self.d_max)
             d = (m - self.running_mean) / (self.running_std + self.epsilon)
-            d = K.stop_gradient(K.clip(d, -d_max_val, d_max_val))
+            d = K.stop_gradient(K.clip(d, -d_max_value, d_max_value))
 
             x_normed = ((x_normed_batch * r) + d) * self.gamma + self.beta
 
@@ -223,11 +224,13 @@ class BatchRenormalization(Layer):
         config = {'epsilon': self.epsilon,
                   'mode': self.mode,
                   'axis': self.axis,
-                  'gamma_regularizer': self.gamma_regularizer.get_config() if self.gamma_regularizer else None,
-                  'beta_regularizer': self.beta_regularizer.get_config() if self.beta_regularizer else None,
+                  'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
+                  'beta_regularizer': regularizers.serialize(self.beta_regularizer),
                   'momentum': self.momentum,
                   'r_max_value': self.r_max_value,
                   'd_max_value': self.d_max_value,
                   't_delta': self.t_delta}
         base_config = super(BatchRenormalization, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+get_custom_objects().update({'BatchRenormalization': BatchRenormalization})