KMeans.training_graph() now returns an additional value, currently un…

…used.

PiperOrigin-RevId: 170083271

tensorflow/contrib/factorization/examples/mnist.py

@@ -142,7 +142,7 @@ def inference(inp, num_clusters, hidden1_units, hidden2_units):
       # initial_clusters=tf.contrib.factorization.KMEANS_PLUS_PLUS_INIT,
       use_mini_batch=True)
 
-  (all_scores, _, clustering_scores, _, kmeans_init,
+  (all_scores, _, clustering_scores, _, _, kmeans_init,
    kmeans_training_op) = kmeans.training_graph()
   # Some heuristics to approximately whiten this output.
   all_scores = (all_scores[0] - 0.5) * 5

tensorflow/contrib/factorization/python/ops/clustering_ops.py

@@ -337,6 +337,7 @@ def training_graph(self):
         assigned cluster instead.
       cluster_centers_initialized: scalar indicating whether clusters have been
         initialized.
+      cluster_centers_var: a Variable holding the cluster centers.
       init_op: an op to initialize the clusters.
       training_op: an op that runs an iteration of training.
     """
@@ -380,7 +381,7 @@ def training_graph(self):
           inputs, num_clusters, cluster_idx, cluster_centers_var)
 
     return (all_scores, cluster_idx, scores, cluster_centers_initialized,
-            init_op, training_op)
+            cluster_centers_var, init_op, training_op)
 
   def _mini_batch_sync_updates_op(self, update_in_steps, cluster_centers_var,
                                   cluster_centers_updated, total_counts):

tensorflow/contrib/learn/python/learn/estimators/kmeans.py

@@ -106,7 +106,7 @@ def _kmeans_clustering_model_fn(features, labels, mode, params, config):
   """Model function for KMeansClustering estimator."""
   assert labels is None, labels
   (all_scores, model_predictions, losses,
-   is_initialized, init_op, training_op) = clustering_ops.KMeans(
+   is_initialized, _, init_op, training_op) = clustering_ops.KMeans(
        _parse_tensor_or_dict(features),
        params.get('num_clusters'),
        initial_clusters=params.get('training_initial_clusters'),