diff --git a/tensorflow/python/keras/integration_test.py b/tensorflow/python/keras/integration_test.py
index 3c0f73b1c3a..25ca9e69e2e 100644
--- a/tensorflow/python/keras/integration_test.py
+++ b/tensorflow/python/keras/integration_test.py
@@ -26,6 +26,7 @@ from tensorflow.python.keras import testing_utils
 from tensorflow.python.layers import core as tf_core_layers
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import rnn_cell
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
 
 
@@ -312,6 +313,15 @@ class KerasIntegrationTest(test.TestCase):
                           verbose=0)
       self.assertGreater(history.history['val_acc'][-1], 0.7)
 
+  def test_regularizers_with_get_variable(self):
+    # Test case for GitHub issue 22470.
+    with self.cached_session():
+      v = variable_scope.get_variable(
+          'v',
+          shape=[4, 4],
+          initializer=keras.initializers.glorot_uniform(),
+          regularizer=keras.regularizers.l2(0.))
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/regularizers.py b/tensorflow/python/keras/regularizers.py
index 28b6ad4c65a..cbcdae214f9 100644
--- a/tensorflow/python/keras/regularizers.py
+++ b/tensorflow/python/keras/regularizers.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import six
 
+from tensorflow.python.framework import ops
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
@@ -54,12 +55,14 @@ class L1L2(Regularizer):
     self.l2 = K.cast_to_floatx(l2)
 
   def __call__(self, x):
-    regularization = 0.
-    if self.l1:
-      regularization += math_ops.reduce_sum(self.l1 * math_ops.abs(x))
-    if self.l2:
-      regularization += math_ops.reduce_sum(self.l2 * math_ops.square(x))
-    return regularization
+    if self.l1 or self.l2:
+      regularization = ops.convert_to_tensor(0., dtype=K.floatx())
+      if self.l1:
+        regularization += math_ops.reduce_sum(self.l1 * math_ops.abs(x))
+      if self.l2:
+        regularization += math_ops.reduce_sum(self.l2 * math_ops.square(x))
+      return regularization
+    return None
 
   def get_config(self):
     return {'l1': float(self.l1), 'l2': float(self.l2)}