message: Rolling back precision-recall reset_state change due to failures
diff: @@ -1337,9 +1337,8 @@ class Precision(Metric):
  def reset_state(self):
  num_thresholds = len(to_list(self.thresholds))
  - backend.batch_set_value([(v, np.zeros((num_thresholds,)))
  - for v in (self.true_positives,
  - self.false_positives)])
  + backend.batch_set_value(
  + [(v, np.zeros((num_thresholds,))) for v in self.variables])
  def get_config(self):
  config = {
  @@ -1463,9 +1462,8 @@ class Recall(Metric):
  def reset_state(self):
  num_thresholds = len(to_list(self.thresholds))
  - backend.batch_set_value([(v, np.zeros((num_thresholds,)))
  - for v in (self.true_positives,
  - self.false_negatives)])
  + backend.batch_set_value(
  + [(v, np.zeros((num_thresholds,))) for v in self.variables])
  def get_config(self):
  config = {
  @@ -1548,11 +1546,8 @@ class SensitivitySpecificityBase(Metric, metaclass=abc.ABCMeta):
  def reset_state(self):
  num_thresholds = len(self.thresholds)
  - confusion_matrix_variables = (self.true_positives, self.true_negatives,
  - self.false_positives, self.false_negatives)
  - backend.batch_set_value([
  - (v, np.zeros((num_thresholds,))) for v in confusion_matrix_variables
  - ])
  + backend.batch_set_value(
  + [(v, np.zeros((num_thresholds,))) for v in self.variables])
  def get_config(self):
  config = {'class_id': self.class_id}
  @@ -2378,15 +2373,14 @@ class AUC(Metric):
  name=self.name)
  def reset_state(self):
  - confusion_matrix_variables = (self.true_positives, self.true_negatives,
  - self.false_positives, self.false_negatives)
  if self.multi_label:
  - backend.batch_set_value([
  - (v, np.zeros((self.num_thresholds, self._num_labels)))
  - for v in confusion_matrix_variables])
  + backend.batch_set_value(
  + [(v, np.zeros((self.num_thresholds, self._num_labels)))
  + for v in self.variables])
  else:
  - backend.batch_set_value([(v, np.zeros((self.num_thresholds,)))
  - for v in confusion_matrix_variables])
  + backend.batch_set_value([
  + (v, np.zeros((self.num_thresholds,))) for v in self.variables
  + ])
  def get_config(self):
  if is_tensor_or_variable(self.label_weights):
  