Fix tests and demo

Author: chihuahua

Diff for: tensorboard/plugins/pr_curve/pr_curve_demo.py

@@ -161,7 +161,7 @@ def start_runs(
       weights = tf.cast(consecutive_indices % 2, dtype=tf.float32)
 
     summary.op(
-        tag=color,
+        name=color,
         labels=labels[:, i],
         predictions=predictions[i],
         num_thresholds=thresholds,

Diff for: tensorboard/plugins/pr_curve/summary.py

@@ -36,7 +36,7 @@
 _DEFAULT_NUM_THRESHOLDS = 200
 
 def op(
-    tag,
+    name,
     labels,
     predictions,
     num_thresholds=None,
@@ -90,7 +90,7 @@ def op(
 
   dtype = predictions.dtype
 
-  with tf.name_scope(tag, values=[labels, predictions, weights]):
+  with tf.name_scope(name, values=[labels, predictions, weights]):
     tf.assert_type(labels, tf.bool)
     # We cast to float to ensure we have 0.0 or 1.0.
     f_labels = tf.cast(labels, dtype)

Diff for: tensorboard/plugins/pr_curve/summary_test.py

@@ -221,7 +221,7 @@ def test_many_values_with_weights(self):
   def test_raw_data_op(self):
     # We pass raw counts and precision/recall values.
     op = summary.raw_data_op(
-        tag='foo',
+        name='foo',
         true_positive_counts=tf.constant([75, 64, 21, 5, 0]),
         false_positive_counts=tf.constant([150, 105, 18, 0, 0]),
         true_negative_counts=tf.constant([0, 45, 132, 150, 150]),
@@ -282,11 +282,11 @@ def test_matches_op(self):
     predictions = tf.constant([0.2, 0.4, 0.5, 0.6, 0.8], dtype=tf.float32)
     labels = tf.constant([False, True, True, False, True], dtype=tf.bool)
 
-    pr_curve, update_op = summary.streaming_op(tag='pr_curve',
+    pr_curve, update_op = summary.streaming_op(name='pr_curve',
                                                predictions=predictions,
                                                labels=labels,
                                                num_thresholds=10)
-    expected_pr_curve = summary.op(tag='pr_curve',
+    expected_pr_curve = summary.op(name='pr_curve',
                                    predictions=predictions,
                                    labels=labels,
                                    num_thresholds=10)