diff --git a/tensorflow/python/keras/layers/serialization.py b/tensorflow/python/keras/layers/serialization.py
index 35202617716..d8daf79d2d5 100644
--- a/tensorflow/python/keras/layers/serialization.py
+++ b/tensorflow/python/keras/layers/serialization.py
@@ -45,6 +45,15 @@ if tf2.enabled():
   from tensorflow.python.keras.layers.normalization_v2 import *  # pylint: disable=g-import-not-at-top
   from tensorflow.python.keras.layers.recurrent_v2 import *     # pylint: disable=g-import-not-at-top
 
+# This deserialization table is added for backward compatibility, as in TF 1.13,
+# BatchNormalizationV1 and BatchNormalizationV2 are used as class name for v1
+# and v2 version of BatchNormalization, respectively. Here we explictly convert
+# them to the canonical name in the config of deserialization.
+_DESERIALIZATION_TABLE = {
+    'BatchNormalizationV1': 'BatchNormalization',
+    'BatchNormalizationV2': 'BatchNormalization',
+}
+
 
 @keras_export('keras.layers.serialize')
 def serialize(layer):
@@ -68,6 +77,9 @@ def deserialize(config, custom_objects=None):
   globs['Network'] = models.Network
   globs['Model'] = models.Model
   globs['Sequential'] = models.Sequential
+  layer_class_name = config['class_name']
+  if layer_class_name in _DESERIALIZATION_TABLE:
+    config['class_name'] = _DESERIALIZATION_TABLE[layer_class_name]
 
   return deserialize_keras_object(
       config,
diff --git a/tensorflow/python/keras/layers/serialization_test.py b/tensorflow/python/keras/layers/serialization_test.py
index 5e9fa3cef8d..c0eb2fe9359 100644
--- a/tensorflow/python/keras/layers/serialization_test.py
+++ b/tensorflow/python/keras/layers/serialization_test.py
@@ -69,6 +69,31 @@ class LayerSerializationTest(parameterized.TestCase, test.TestCase):
     self.assertEqual(new_layer.gamma_regularizer.__class__,
                      keras.regularizers.L1L2)
 
+  @parameterized.parameters(
+      [batchnorm_v1.BatchNormalization, batchnorm_v2.BatchNormalization])
+  def test_deserialize_batchnorm_backwards_compatiblity(self, batchnorm_layer):
+    layer = batchnorm_layer(
+        momentum=0.9, beta_initializer='zeros', gamma_regularizer='l2')
+    config = keras.layers.serialize(layer)
+    # To simulate if BatchNormalizationV1 or BatchNormalizationV2 appears in the
+    # saved model.
+    if batchnorm_layer is batchnorm_v1.BatchNormalization:
+      config['class_name'] = 'BatchNormalizationV1'
+    else:
+      config['class_name'] = 'BatchNormalizationV2'
+    new_layer = keras.layers.deserialize(config)
+    self.assertEqual(new_layer.momentum, 0.9)
+    if tf2.enabled():
+      self.assertIsInstance(new_layer, batchnorm_v2.BatchNormalization)
+      self.assertEqual(new_layer.beta_initializer.__class__,
+                       keras.initializers.ZerosV2)
+    else:
+      self.assertIsInstance(new_layer, batchnorm_v1.BatchNormalization)
+      self.assertEqual(new_layer.beta_initializer.__class__,
+                       keras.initializers.Zeros)
+    self.assertEqual(new_layer.gamma_regularizer.__class__,
+                     keras.regularizers.L1L2)
+
   @parameterized.parameters([rnn_v1.LSTM, rnn_v2.LSTM])
   def test_serialize_deserialize_lstm(self, layer):
     lstm = layer(5, return_sequences=True)