diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index 1c6f2bd3f8c..93cb805d089 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -2530,6 +2530,7 @@ class LSTM(RNN):
       config['implementation'] = 1
     return cls(**config)
 
+
 @tf_export('keras.layers.LSTM', v1=[])
 class UnifiedLSTM(LSTM):
   """Long Short-Term Memory layer - Hochreiter 1997.
@@ -2655,8 +2656,6 @@ class UnifiedLSTM(LSTM):
     self.state_spec = [
         InputSpec(shape=(None, dim)) for dim in (self.units, self.units)
     ]
-    self._num_constants = None
-    self._num_inputs = None
     self._dropout_mask = None
     self.could_use_cudnn = (
         activation == 'tanh' and recurrent_activation == 'sigmoid' and
@@ -2775,46 +2774,6 @@ class UnifiedLSTM(LSTM):
     else:
       return output
 
-  @property
-  def trainable_weights(self):
-    if self.trainable:
-      weights = []
-      weights += self.cell.trainable_weights
-      return weights
-    return []
-
-  @property
-  def non_trainable_weights(self):
-    if not self.trainable:
-      weights = []
-      weights += self.cell.non_trainable_weights
-      return weights
-    return []
-
-  @property
-  def losses(self):
-    losses = []
-    losses += self.cell.losses
-    return losses + self._losses
-
-  @property
-  def updates(self):
-    updates = []
-    updates += self.cell.updates
-    return updates + self._updates
-
-  def get_weights(self):
-    weights = []
-    weights += self.cell.weights
-    return K.batch_get_value(weights)
-
-  def set_weights(self, weights):
-    tuples = []
-    cell_weights = weights[:len(self.cell.weights)]
-    if cell_weights:
-      tuples.append((self.cell.weights, cell_weights))
-    K.batch_set_value(tuples)
-
 
 def _canonical_to_params(weights, biases, shape, transpose_weights=False):
   """Utility function convert variable to CuDNN compatible parameter.