Python tf_logging.error方法代码示例

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def GetTempDir():
  """Return a temporary directory for tests to use."""
  global _googletest_temp_dir
  if not _googletest_temp_dir:
    first_frame = tf_inspect.stack()[-1][0]
    temp_dir = os.path.join(tempfile.gettempdir(),
                            os.path.basename(tf_inspect.getfile(first_frame)))
    temp_dir = tempfile.mkdtemp(prefix=temp_dir.rstrip('.py'))

    def delete_temp_dir(dirname=temp_dir):
      try:
        file_io.delete_recursively(dirname)
      except errors.OpError as e:
        logging.error('Error removing %s: %s', dirname, e)

    atexit.register(delete_temp_dir)
    _googletest_temp_dir = temp_dir

  return _googletest_temp_dir 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _SetPath(self, path):
    """Sets the current path to watch for new events.

    This also records the size of the old path, if any. If the size can't be
    found, an error is logged.

    Args:
      path: The full path of the file to watch.
    """
    old_path = self._path
    if old_path and not io_wrapper.IsGCSPath(old_path):
      try:
        # We're done with the path, so store its size.
        size = gfile.Stat(old_path).length
        logging.debug('Setting latest size of %s to %d', old_path, size)
        self._finalized_sizes[old_path] = size
      except errors.OpError as e:
        logging.error('Unable to get size of %s: %s', old_path, e)

    self._path = path
    self._loader = self._loader_factory(path) 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def Reload(self):
    """Call `Reload` on every `EventAccumulator`."""
    logging.info('Beginning EventMultiplexer.Reload()')
    self._reload_called = True
    # Build a list so we're safe even if the list of accumulators is modified
    # even while we're reloading.
    with self._accumulators_mutex:
      items = list(self._accumulators.items())

    names_to_delete = set()
    for name, accumulator in items:
      try:
        accumulator.Reload()
      except (OSError, IOError) as e:
        logging.error("Unable to reload accumulator '%s': %s", name, e)
      except directory_watcher.DirectoryDeletedError:
        names_to_delete.add(name)

    with self._accumulators_mutex:
      for name in names_to_delete:
        logging.warning("Deleting accumulator '%s'", name)
        del self._accumulators[name]
    logging.info('Finished with EventMultiplexer.Reload()')
    return self 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _execute_schedule(experiment, schedule):
  """Execute the method named `schedule` of `experiment`."""
  if not hasattr(experiment, schedule):
    logging.error('Schedule references non-existent task %s', schedule)
    valid_tasks = [x for x in dir(experiment)
                   if not x.startswith('_')
                   and callable(getattr(experiment, x))]
    logging.error('Allowed values for this experiment are: %s', valid_tasks)
    raise ValueError('Schedule references non-existent task %s' % schedule)

  task = getattr(experiment, schedule)
  if not callable(task):
    logging.error('Schedule references non-callable member %s', schedule)
    valid_tasks = [x for x in dir(experiment)
                   if not x.startswith('_')
                   and callable(getattr(experiment, x))]
    logging.error('Allowed values for this experiment are: %s', valid_tasks)
    raise TypeError('Schedule references non-callable member %s' % schedule)
  return task() 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def every_n_step_end(self, step, outputs):
    super(ExportMonitor, self).every_n_step_end(step, outputs)
    try:
      self._last_export_dir = self._estimator.export(
          self.export_dir,
          exports_to_keep=self.exports_to_keep,
          signature_fn=self.signature_fn,
          input_fn=self._input_fn,
          default_batch_size=self._default_batch_size,
          input_feature_key=self._input_feature_key,
          use_deprecated_input_fn=self._use_deprecated_input_fn)
    except RuntimeError:
      # Currently we are not syncronized with saving checkpoints, which leads to
      # runtime errors when we are calling export on the same global step.
      # Exports depend on saved checkpoints for constructing the graph and
      # getting the global step from the graph instance saved in the checkpoint.
      # If the checkpoint is stale with respect to current step, the global step
      # is taken to be the last saved checkpoint's global step and exporter
      # doesn't export the same checkpoint again with the following error.
      logging.info("Skipping exporting because the existing checkpoint has "
                   "already been exported. "
                   "Consider exporting less frequently.") 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def after_run(self, run_context, run_values):
    _ = run_context
    scalar_stopping_signal = run_values.results
    if _StopSignals.should_stop(scalar_stopping_signal):
      # NOTE(xiejw): In prediction, stopping signals are inserted for each
      # batch. And we append one more batch to signal the system it should stop.
      # The data flow might look like
      #
      #  batch   0: images, labels, stop = 0  (user provided)
      #  batch   1: images, labels, stop = 0  (user provided)
      #  ...
      #  batch  99: images, labels, stop = 0  (user provided)
      #  batch 100: images, labels, stop = 1  (TPUEstimator appended)
      #
      # where the final batch (id = 100) is appended by TPUEstimator, so we
      # should drop it before returning the predictions to user.
      # To achieve that, we throw the OutOfRangeError in after_run. Once
      # Monitored Session sees this error in SessionRunHook.after_run, the
      # "current" prediction, i.e., batch with id=100, will be discarded
      # immediately
      raise errors.OutOfRangeError(None, None, 'Stopped by stopping signal.') 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _validate_input_pipeline(self):
    """Validates the input pipeline.

    Perform some sanity checks to log user friendly information. We should
    error out to give users better error message. But, if
    _WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break
    user code, so, log a warning.

    Raises:
      RuntimeError: If the validation failed.
    """
    if ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
      err_msg = ('Input pipeline contains one or more QueueRunners. '
                 'It could be slow and not scalable. Please consider '
                 'converting your input pipeline to use `tf.data` instead (see '
                 'https://www.tensorflow.org/guide/datasets for '
                 'instructions.')
      if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
        raise RuntimeError(err_msg)
      else:
        logging.warn(err_msg) 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _AddRateToSummary(tag, rate, step, sw):
  """Adds the given rate to the summary with the given tag.

  Args:
    tag:   Name for this value.
    rate:  Value to add to the summary. Perhaps an error rate.
    step:  Global step of the graph for the x-coordinate of the summary.
    sw:    Summary writer to which to write the rate value.
  """
  sw.add_summary(
      summary_pb2.Summary(value=[summary_pb2.Summary.Value(
          tag=tag, simple_value=rate)]), step) 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _run(self, sess, enqueue_op, coord=None):

        if coord:
            coord.register_thread(threading.current_thread())
        decremented = False
        try:
            while True:
                if coord and coord.should_stop():
                    break
                try:
                    self.func(sess, enqueue_op)  # call enqueue function
                except self._queue_closed_exception_types:  # pylint: disable=catching-non-exception
                    # This exception indicates that a queue was closed.
                    with self._lock:
                        self._runs_per_session[sess] -= 1
                        decremented = True
                        if self._runs_per_session[sess] == 0:
                            try:
                                sess.run(self._close_op)
                            except Exception as e:
                                # Intentionally ignore errors from close_op.
                                logging.vlog(1, "Ignored exception: %s", str(e))
                        return
                except ValueError:  # ignore value error defined by queueing function
                    pass
        except Exception as e:
            # This catches all other exceptions.
            if coord:
                coord.request_stop(e)
            else:
                logging.error("Exception in QueueRunner: %s", str(e))
                with self._lock:
                    self._exceptions_raised.append(e)
                raise
        finally:
            # Make sure we account for all terminations: normal or errors.
            if not decremented:
                with self._lock:
                    self._runs_per_session[sess] -= 1 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def _run(self, sess, enqueue_op, coord=None):

        if coord:
            coord.register_thread(threading.current_thread())
        decremented = False
        try:
            while True:
                if coord and coord.should_stop():
                    break
                try:
                    self.func(sess, enqueue_op)  # call enqueue function
                except self._queue_closed_exception_types:  # pylint: disable=catching-non-exception
                    # This exception indicates that a queue was closed.
                    with self._lock:
                        self._runs_per_session[sess] -= 1
                        decremented = True
                        if self._runs_per_session[sess] == 0:
                            try:
                                sess.run(self._close_op)
                            except Exception as e:
                                # Intentionally ignore errors from close_op.
                                logging.vlog(1, "Ignored exception: %s", str(e))
                        return
        except Exception as e:
            # This catches all other exceptions.
            if coord:
                coord.request_stop(e)
            else:
                logging.error("Exception in QueueRunner: %s", str(e))
                with self._lock:
                    self._exceptions_raised.append(e)
                raise
        finally:
            # Make sure we account for all terminations: normal or errors.
            if not decremented:
                with self._lock:
                    self._runs_per_session[sess] -= 1 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def close(self):
    """Closes this session.

    Calling this method frees all resources associated with the session.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        closing the TensorFlow session.
    """
    with self._extend_lock:
      if self._opened and not self._closed:
        self._closed = True
        with errors.raise_exception_on_not_ok_status() as status:
          tf_session.TF_CloseDeprecatedSession(self._session, status) 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def __exit__(self, exec_type, exec_value, exec_tb):
    if exec_type is errors.OpError:
      logging.error('Session closing due to OpError: %s', (exec_value,))
    self._default_session_context_manager.__exit__(
        exec_type, exec_value, exec_tb)
    self._default_graph_context_manager.__exit__(exec_type, exec_value, exec_tb)

    self._default_session_context_manager = None
    self._default_graph_context_manager = None

    self.close() 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config) 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def after_run(self, run_context, run_values):
    if np.isnan(run_values.results):
      failure_message = "Model diverged with loss = NaN."
      if self._fail_on_nan_loss:
        logging.error(failure_message)
        raise NanLossDuringTrainingError
      else:
        logging.warning(failure_message)
        # We don't raise an error but we request stop without an exception.
        run_context.request_stop() 

# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import error [as 别名]
def __init__(self,
               save_steps=None,
               save_secs=None,
               output_dir=None,
               summary_writer=None,
               scaffold=None,
               summary_op=None):
    """Initializes a `SummarySaverHook`.

    Args:
      save_steps: `int`, save summaries every N steps. Exactly one of
          `save_secs` and `save_steps` should be set.
      save_secs: `int`, save summaries every N seconds.
      output_dir: `string`, the directory to save the summaries to. Only used
          if no `summary_writer` is supplied.
      summary_writer: `SummaryWriter`. If `None` and an `output_dir` was passed,
          one will be created accordingly.
      scaffold: `Scaffold` to get summary_op if it's not provided.
      summary_op: `Tensor` of type `string` containing the serialized `Summary`
          protocol buffer or a list of `Tensor`. They are most likely an output
          by TF summary methods like `tf.summary.scalar` or
          `tf.summary.merge_all`. It can be passed in as one tensor; if more
          than one, they must be passed in as a list.

    Raises:
      ValueError: Exactly one of scaffold or summary_op should be set.
    """
    if ((scaffold is None and summary_op is None) or
        (scaffold is not None and summary_op is not None)):
      raise ValueError(
          "Exactly one of scaffold or summary_op must be provided.")
    self._summary_op = summary_op
    self._summary_writer = summary_writer
    self._output_dir = output_dir
    self._scaffold = scaffold
    self._timer = SecondOrStepTimer(every_secs=save_secs,
                                    every_steps=save_steps)
    # TODO(mdan): Throw an error if output_dir and summary_writer are None.