Merge changes from github.

PiperOrigin-RevId: 192388250

2 files changed, 13 insertions, 13 deletions

diff --git a/tensorflow/contrib/training/python/training/evaluation.py b/tensorflow/contrib/training/python/training/evaluation.py
index 1a5fb45be0..4bb53e8678 100644
--- a/tensorflow/contrib/training/python/training/evaluation.py
+++ b/tensorflow/contrib/training/python/training/evaluation.py
@@ -36,9 +36,8 @@ out the metrics values to stdout:
 
   # Choose the metrics to compute:
   names_to_values, names_to_updates = tf.contrib.metrics.aggregate_metric_map({
-      "accuracy": tf.contrib.metrics.streaming_accuracy(predictions, labels),
-      "mse": tf.contrib.metrics.streaming_mean_squared_error(
-        predictions, labels),
+      "accuracy": tf.metrics.accuracy(labels, predictions),
+      "mse": tf.metrics.mean_squared_error(labels, predictions),
   })
 
   # Define the summaries to write:
@@ -81,9 +80,8 @@ more summaries and call the evaluate_repeatedly method:
 
   # Choose the metrics to compute:
   names_to_values, names_to_updates = tf.contrib.metrics.aggregate_metric_map({
-      "accuracy": tf.contrib.metrics.streaming_accuracy(predictions, labels),
-      "mse": tf.contrib.metrics.streaming_mean_squared_error(
-          predictions, labels),
+      "accuracy": tf.metrics.accuracy(labels, predictions),
+      "mse": tf.metrics.mean_squared_error(labels, predictions),
   })
 
   # Define the summaries to write:
diff --git a/tensorflow/contrib/training/python/training/evaluation_test.py b/tensorflow/contrib/training/python/training/evaluation_test.py
index b07039916c..c36d00e842 100644
--- a/tensorflow/contrib/training/python/training/evaluation_test.py
+++ b/tensorflow/contrib/training/python/training/evaluation_test.py
@@ -27,7 +27,6 @@ import numpy as np
 from tensorflow.contrib.framework.python.ops import variables
 from tensorflow.contrib.layers.python.layers import layers
 from tensorflow.contrib.losses.python.losses import loss_ops
-from tensorflow.contrib.metrics.python.ops import metric_ops
 from tensorflow.contrib.training.python.training import evaluation
 from tensorflow.contrib.training.python.training import training
 from tensorflow.core.protobuf import config_pb2
@@ -38,6 +37,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import metrics
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables as variables_lib
 from tensorflow.python.platform import gfile
@@ -196,7 +196,8 @@ class EvaluateOnceTest(test.TestCase):
     logits = logistic_classifier(inputs)
     predictions = math_ops.round(logits)
 
-    accuracy, update_op = metric_ops.streaming_accuracy(predictions, labels)
+    accuracy, update_op = metrics.accuracy(
+        predictions=predictions, labels=labels)
 
     checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
 
@@ -311,7 +312,8 @@ class EvaluateRepeatedlyTest(test.TestCase):
     logits = logistic_classifier(inputs)
     predictions = math_ops.round(logits)
 
-    accuracy, update_op = metric_ops.streaming_accuracy(predictions, labels)
+    accuracy, update_op = metrics.accuracy(
+        predictions=predictions, labels=labels)
 
     final_values = evaluation.evaluate_repeatedly(
         checkpoint_dir=checkpoint_dir,
@@ -365,7 +367,8 @@ class EvaluateRepeatedlyTest(test.TestCase):
     logits = logistic_classifier(inputs)
     predictions = math_ops.round(logits)
 
-    accuracy, update_op = metric_ops.streaming_accuracy(predictions, labels)
+    accuracy, update_op = metrics.accuracy(
+        predictions=predictions, labels=labels)
 
     timeout_fn_calls = [0]
     def timeout_fn():
@@ -417,9 +420,8 @@ class EvaluateRepeatedlyTest(test.TestCase):
     self.assertEqual(final_values['my_var'], expected_value)
 
   def _create_names_to_metrics(self, predictions, labels):
-    accuracy0, update_op0 = metric_ops.streaming_accuracy(predictions, labels)
-    accuracy1, update_op1 = metric_ops.streaming_accuracy(
-        predictions + 1, labels)
+    accuracy0, update_op0 = metrics.accuracy(labels, predictions)
+    accuracy1, update_op1 = metrics.accuracy(labels, predictions + 1)
 
     names_to_values = {'Accuracy': accuracy0, 'Another_accuracy': accuracy1}
     names_to_updates = {'Accuracy': update_op0, 'Another_accuracy': update_op1}