Python logging.error方法代碼示例

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
  """Extract the list of feature names and the dimensionality of each feature
     from string of comma separated values.

  Args:
    feature_names: string containing comma separated list of feature names
    feature_sizes: string containing comma separated list of feature sizes

  Returns:
    List of the feature names and list of the dimensionality of each feature.
    Elements in the first/second list are strings/integers.
  """
  list_of_feature_names = [
      feature_names.strip() for feature_names in feature_names.split(',')]
  list_of_feature_sizes = [
      int(feature_sizes) for feature_sizes in feature_sizes.split(',')]
  if len(list_of_feature_names) != len(list_of_feature_sizes):
    logging.error("length of the feature names (=" +
                  str(len(list_of_feature_names)) + ") != length of feature "
                  "sizes (=" + str(len(list_of_feature_sizes)) + ")")

  return list_of_feature_names, list_of_feature_sizes 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
  """Extract the list of feature names and the dimensionality of each feature

     from string of comma separated values.

  Args:
    feature_names: string containing comma separated list of feature names
    feature_sizes: string containing comma separated list of feature sizes

  Returns:
    List of the feature names and list of the dimensionality of each feature.
    Elements in the first/second list are strings/integers.
  """
  list_of_feature_names = [
      feature_names.strip() for feature_names in feature_names.split(",")
  ]
  list_of_feature_sizes = [
      int(feature_sizes) for feature_sizes in feature_sizes.split(",")
  ]
  if len(list_of_feature_names) != len(list_of_feature_sizes):
    logging.error("length of the feature names (=" +
                  str(len(list_of_feature_names)) + ") != length of feature "
                  "sizes (=" + str(len(list_of_feature_sizes)) + ")")

  return list_of_feature_names, list_of_feature_sizes 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
    """Extract the list of feature names and the dimensionality of each feature
         from string of comma separated values.

      Args:
        feature_names: string containing comma separated list of feature names
        feature_sizes: string containing comma separated list of feature sizes

      Returns:
        List of the feature names and list of the dimensionality of each feature.
        Elements in the first/second list are strings/integers.
    """
    list_of_feature_names = [
        feature_names.strip() for feature_names in feature_names.split(',')]
    list_of_feature_sizes = [
        int(feature_sizes) for feature_sizes in feature_sizes.split(',')]
    if len(list_of_feature_names) != len(list_of_feature_sizes):
        logging.error("length of the feature names (=" +
                      str(len(list_of_feature_names)) + ") != length of feature "
                                                        "sizes (=" + str(len(list_of_feature_sizes)) + ")")

    return list_of_feature_names, list_of_feature_sizes 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def validate_class_name(flag_value, category, modules, expected_superclass):
    """Checks that the given string matches a class of the expected type.
      Args:
        flag_value: A string naming the class to instantiate.
        category: A string used further describe the class in error messages
                  (e.g. 'model', 'reader', 'loss').
        modules: A list of modules to search for the given class.
        expected_superclass: A class that the given class should inherit from.
      Raises:
        FlagsError: If the given class could not be found or if the first class
        found with that name doesn't inherit from the expected superclass.
      Returns:
        True if a class was found that matches the given constraints.
      """
    candidates = [getattr(module, flag_value, None) for module in modules]
    for candidate in candidates:
        if not candidate:
            continue
        if not issubclass(candidate, expected_superclass):
            raise flags.FlagsError("%s '%s' doesn't inherit from %s." %
                                   (category, flag_value,
                                    expected_superclass.__name__))
        return True
    raise flags.FlagsError("Unable to find %s '%s'." % (category, flag_value)) 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def validate_class_name(flag_value, category, modules, expected_superclass):
  """Checks that the given string matches a class of the expected type.

  Args:
    flag_value: A string naming the class to instantiate.
    category: A string used further describe the class in error messages
              (e.g. 'model', 'reader', 'loss').
    modules: A list of modules to search for the given class.
    expected_superclass: A class that the given class should inherit from.

  Raises:
    FlagsError: If the given class could not be found or if the first class
    found with that name doesn't inherit from the expected superclass.

  Returns:
    True if a class was found that matches the given constraints.
  """
  candidates = [getattr(module, flag_value, None) for module in modules]
  for candidate in candidates:
    if not candidate:
      continue
    if not issubclass(candidate, expected_superclass):
      raise flags.FlagsError("%s '%s' doesn't inherit from %s." %
                             (category, flag_value,
                              expected_superclass.__name__))
    return True
  raise flags.FlagsError("Unable to find %s '%s'." % (category, flag_value)) 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def remove_training_directory(self, train_dir):
    """Removes the training directory."""
    try:
      logging.info(
          "%s: Removing existing train directory.",
          task_as_string(self.task))
      gfile.DeleteRecursively(train_dir)
    except:
      logging.error(
          "%s: Failed to delete directory " + train_dir +
          " when starting a new model. Please delete it manually and" +
          " try again.", task_as_string(self.task)) 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def remove_training_directory(self, train_dir):
        """Removes the training directory."""
        try:
            logging.info(
                "%s: Removing existing train directory.",
                task_as_string(self.task))
            gfile.DeleteRecursively(train_dir)
        except:
            logging.error(
                "%s: Failed to delete directory " + train_dir +
                " when starting a new model. Please delete it manually and" +
                " try again.", task_as_string(self.task)) 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def validate_class_name(flag_value, category, modules, expected_superclass):
  """Checks that the given string matches a class of the expected type.

  Args:
    flag_value: A string naming the class to instantiate.
    category: A string used further describe the class in error messages (e.g.
      'model', 'reader', 'loss').
    modules: A list of modules to search for the given class.
    expected_superclass: A class that the given class should inherit from.

  Raises:
    FlagsError: If the given class could not be found or if the first class
    found with that name doesn't inherit from the expected superclass.

  Returns:
    True if a class was found that matches the given constraints.
  """
  candidates = [getattr(module, flag_value, None) for module in modules]
  for candidate in candidates:
    if not candidate:
      continue
    if not issubclass(candidate, expected_superclass):
      raise flags.FlagsError(
          "%s '%s' doesn't inherit from %s." %
          (category, flag_value, expected_superclass.__name__))
    return True
  raise flags.FlagsError("Unable to find %s '%s'." % (category, flag_value)) 

# 需要導入模塊: from tensorflow import logging [as 別名]
# 或者: from tensorflow.logging import error [as 別名]
def remove_training_directory(self, train_dir):
    """Removes the training directory."""
    try:
      logging.info("%s: Removing existing train directory.",
                   task_as_string(self.task))
      gfile.DeleteRecursively(train_dir)
    except:
      logging.error(
          "%s: Failed to delete directory " + train_dir +
          " when starting a new model. Please delete it manually and" +
          " try again.", task_as_string(self.task))