Addressed reviewer's comments.

Change-Id: Ie5395e5cafc3544f909294650ff1b8de4c6153f7

Author: wwwind

tensorflow_model_optimization/python/core/clustering/keras/cluster.py

@@ -196,8 +196,7 @@ def _strip_clustering_wrapper(layer):
       layer.layer._non_trainable_weights = []
       for i in range(len(layer.restore)):
         # This is why we used integers as keys
-        name, weight = layer.restore[i]
-        weight_name = layer.layer.name + "/" + name
+        name, weight_name, weight = layer.restore[i]
         # In both cases we use k.batch_get_value since we need physical copies
         # of the arrays to initialize a new tensor
         if i in layer.gone_variables:

tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper.py

@@ -244,12 +244,13 @@ def fn():
 
     for ct, weight in enumerate(self.layer.weights):
       name = self._weight_name(weight.name)
+      full_name = self.layer.name + "/" + name
       if ct in self.gone_variables:
         # Again, not sure if this is needed
         weight_name = clusterable_weights_to_variables[name]
-        self.restore.append((name, get_updater(weight_name)))
+        self.restore.append((name, full_name, get_updater(weight_name)))
       else:
-        self.restore.append((name, weight))
+        self.restore.append((name, full_name, weight))
 
   def call(self, inputs):
     # Go through all tensors and replace them with their clustered copies.