Merge pull request #21798 from facaiy:ENH/div_no_nan_treate_negative_as_zero

PiperOrigin-RevId: 214290400

1 files changed, 20 insertions, 30 deletions

diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index bbf5d3f30c..91939b5bf2 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -45,24 +45,6 @@ from tensorflow.python.util.deprecation import deprecated
 _EPSILON = 1e-7
 
 
-def _safe_div(numerator, denominator, name):
-  """Divides two values, returning 0 if the denominator is <= 0.
-
-  Args:
-    numerator: A real `Tensor`.
-    denominator: A real `Tensor`, with dtype matching `numerator`.
-    name: Name for the returned op.
-
-  Returns:
-    0 if `denominator` <= 0, else `numerator` / `denominator`
-  """
-  return array_ops.where(
-      math_ops.greater(denominator, 0),
-      math_ops.truediv(numerator, denominator),
-      0,
-      name=name)
-
-
 @deprecated(None, 'Please switch to tf.metrics.true_positives. Note that the '
             'order of the labels and predictions arguments has been switched.')
 def streaming_true_positives(predictions,
@@ -3238,22 +3220,28 @@ def streaming_covariance(predictions,
 
     # We update the means by Delta=Error*BatchCount/(BatchCount+PrevCount)
     # batch_mean_prediction is E[x_B] in the update equation
-    batch_mean_prediction = _safe_div(
-        math_ops.reduce_sum(weighted_predictions), batch_count,
-        'batch_mean_prediction')
-    delta_mean_prediction = _safe_div(
-        (batch_mean_prediction - mean_prediction) * batch_count, update_count,
-        'delta_mean_prediction')
+    batch_mean_prediction = math_ops.div_no_nan(
+        math_ops.reduce_sum(weighted_predictions),
+        batch_count,
+        name='batch_mean_prediction')
+    delta_mean_prediction = math_ops.div_no_nan(
+        (batch_mean_prediction - mean_prediction) * batch_count,
+        update_count,
+        name='delta_mean_prediction')
     update_mean_prediction = state_ops.assign_add(mean_prediction,
                                                   delta_mean_prediction)
     # prev_mean_prediction is E[x_A] in the update equation
     prev_mean_prediction = update_mean_prediction - delta_mean_prediction
 
     # batch_mean_label is E[y_B] in the update equation
-    batch_mean_label = _safe_div(
-        math_ops.reduce_sum(weighted_labels), batch_count, 'batch_mean_label')
-    delta_mean_label = _safe_div((batch_mean_label - mean_label) * batch_count,
-                                 update_count, 'delta_mean_label')
+    batch_mean_label = math_ops.div_no_nan(
+        math_ops.reduce_sum(weighted_labels),
+        batch_count,
+        name='batch_mean_label')
+    delta_mean_label = math_ops.div_no_nan(
+        (batch_mean_label - mean_label) * batch_count,
+        update_count,
+        name='delta_mean_label')
     update_mean_label = state_ops.assign_add(mean_label, delta_mean_label)
     # prev_mean_label is E[y_A] in the update equation
     prev_mean_label = update_mean_label - delta_mean_label
@@ -3915,8 +3903,10 @@ def cohen_kappa(labels,
       po_sum = math_ops.reduce_sum(po)
       total = math_ops.reduce_sum(pe_row)
       pe_sum = math_ops.reduce_sum(
-          metrics_impl._safe_div(  # pylint: disable=protected-access
-              pe_row * pe_col, total, None))
+          math_ops.div_no_nan(
+              math_ops.to_double(pe_row * pe_col),
+              math_ops.to_double(total),
+              name=None))
       po_sum, pe_sum, total = (math_ops.to_double(po_sum),
                                math_ops.to_double(pe_sum),
                                math_ops.to_double(total))