diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py
index 1d1f3b45864..c8ccb7f6242 100644
--- a/tensorflow/python/keras/metrics.py
+++ b/tensorflow/python/keras/metrics.py
@@ -177,6 +177,12 @@ def _assert_thresholds_range(thresholds):
                      .format(invalid_thresholds))
 
 
+def _parse_init_thresholds(thresholds, default_threshold=0.5):
+  thresholds = to_list(default_threshold if thresholds is None else thresholds)
+  _assert_thresholds_range(thresholds)
+  return thresholds
+
+
 def _update_confusion_matrix_variables(variables_to_update,
                                        y_true,
                                        y_pred,
@@ -869,12 +875,11 @@ class _ConfusionMatrixConditionCount(Metric):
     """
     super(_ConfusionMatrixConditionCount, self).__init__(name=name, dtype=dtype)
     self._confusion_matrix_cond = confusion_matrix_cond
-    self.thresholds = 0.5 if thresholds is None else thresholds
-    thresholds_list = to_list(self.thresholds)
-    _assert_thresholds_range(thresholds_list)
+    self.thresholds = _parse_init_thresholds(
+        thresholds, default_threshold=0.5)
     self.accumulator = self.add_weight(
         'accumulator',
-        shape=(len(thresholds_list),),
+        shape=(len(self.thresholds),),
         initializer=init_ops.zeros_initializer)
 
   def update_state(self, y_true, y_pred, sample_weight=None):
@@ -895,10 +900,10 @@ class _ConfusionMatrixConditionCount(Metric):
     }, y_true, y_pred, self.thresholds, sample_weight)
 
   def result(self):
-    if isinstance(self.thresholds, (list, tuple)):
-      result = self.accumulator
-    else:
+    if len(self.thresholds) == 1:
       result = self.accumulator[0]
+    else:
+      result = self.accumulator
     return ops.convert_to_tensor(result)
 
   def reset_states(self):
@@ -1152,16 +1157,15 @@ class Precision(Metric):
       dtype: (Optional) data type of the metric result.
     """
     super(Precision, self).__init__(name=name, dtype=dtype)
-    self.thresholds = 0.5 if thresholds is None else thresholds
-    thresholds_list = to_list(self.thresholds)
-    _assert_thresholds_range(thresholds_list)
+    self.thresholds = _parse_init_thresholds(
+        thresholds, default_threshold=0.5)
     self.tp = self.add_weight(
         'true_positives',
-        shape=(len(thresholds_list),),
+        shape=(len(self.thresholds),),
         initializer=init_ops.zeros_initializer)
     self.fp = self.add_weight(
         'false_positives',
-        shape=(len(thresholds_list),),
+        shape=(len(self.thresholds),),
         initializer=init_ops.zeros_initializer)
 
   def update_state(self, y_true, y_pred, sample_weight=None):
@@ -1184,7 +1188,7 @@ class Precision(Metric):
 
   def result(self):
     result = math_ops.div_no_nan(self.tp, self.tp + self.fp)
-    return result if isinstance(self.thresholds, (list, tuple)) else result[0]
+    return result[0] if len(self.thresholds) == 1 else result
 
   def reset_states(self):
     num_thresholds = len(to_list(self.thresholds))
@@ -1237,16 +1241,15 @@ class Recall(Metric):
       dtype: (Optional) data type of the metric result.
     """
     super(Recall, self).__init__(name=name, dtype=dtype)
-    self.thresholds = 0.5 if thresholds is None else thresholds
-    thresholds_list = to_list(self.thresholds)
-    _assert_thresholds_range(thresholds_list)
+    self.thresholds = _parse_init_thresholds(
+        thresholds, default_threshold=0.5)
     self.tp = self.add_weight(
         'true_positives',
-        shape=(len(thresholds_list),),
+        shape=(len(self.thresholds),),
         initializer=init_ops.zeros_initializer)
     self.fn = self.add_weight(
         'false_negatives',
-        shape=(len(thresholds_list),),
+        shape=(len(self.thresholds),),
         initializer=init_ops.zeros_initializer)
 
   def update_state(self, y_true, y_pred, sample_weight=None):
@@ -1269,7 +1272,7 @@ class Recall(Metric):
 
   def result(self):
     result = math_ops.div_no_nan(self.tp, self.tp + self.fn)
-    return result if isinstance(self.thresholds, (list, tuple)) else result[0]
+    return result[0] if len(self.thresholds) == 1 else result
 
   def reset_states(self):
     num_thresholds = len(to_list(self.thresholds))