Python SessionLog.START属性代码示例

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def after_run(self, run_context, run_values):
        if not self._summary_writer:
            return

        stale_global_step = run_values.results["global_step"]
        global_step = stale_global_step + 1
        if self._next_step is None or self._request_summary:
            global_step = run_context.session.run(self._global_step_tensor)

        if self._next_step is None:
            self._summary_writer.add_session_log(
                SessionLog(status=SessionLog.START), global_step)

        if "summary" in run_values.results:
            self._timer.update_last_triggered_step(global_step)
            for summary in run_values.results["summary"]:
                self._summary_writer.add_summary(summary, global_step)
            self._summary_writer.flush()

        self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def after_run(self, run_context, run_values):
    _ = run_context
    if not self._summary_writer:
      return

    global_step = run_values.results["global_step"]

    if self._next_step is None:
      self._summary_writer.add_session_log(
          SessionLog(status=SessionLog.START), global_step)

    if self._request_summary:
      self._timer.update_last_triggered_step(global_step)
      if "summary" in run_values.results:
        for summary in run_values.results["summary"]:
          self._summary_writer.add_summary(summary, global_step)

    self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def _CheckForRestartAndMaybePurge(self, event):
    """Check and discard expired events using SessionLog.START.

    Check for a SessionLog.START event and purge all previously seen events
    with larger steps, because they are out of date. Because of supervisor
    threading, it is possible that this logic will cause the first few event
    messages to be discarded since supervisor threading does not guarantee
    that the START message is deterministically written first.

    This method is preferred over _CheckForOutOfOrderStepAndMaybePurge which
    can inadvertently discard events due to supervisor threading.

    Args:
      event: The event to use as reference. If the event is a START event, all
        previously seen events with a greater event.step will be purged.
    """
    if event.HasField(
        'session_log') and event.session_log.status == SessionLog.START:
      self._Purge(event, by_tags=False) 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def after_run(self, run_context, run_values):
        if not self._summary_writer:
            return

        stale_global_step = run_values.results["global_step"]
        global_step = stale_global_step + 1
        if self._next_step is None or self._request_summary:
            global_step = run_context.session.run(self._global_step_tensor)

        if self._next_step is None:
            self._summary_writer.add_session_log(SessionLog(status=SessionLog.START), global_step)

        if "summary" in run_values.results:
            self._timer.update_last_triggered_step(global_step)
            for summary in run_values.results["summary"]:
                self._summary_writer.add_summary(summary, global_step)

        self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def after_run(self, run_context, run_values):
    _ = run_context
    if not self._summary_writer:
      return

    global_step = run_values.results["global_step"]

    if self._next_step is None:
      self._summary_writer.add_session_log(
          SessionLog(status=SessionLog.START), global_step)

    if self._request_summary:
      self._timer.update_last_triggered_step(global_step)
      if "summary" in run_values.results:
        self._summary_writer.add_summary(run_values.results["summary"],
                                         global_step)

    self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def testSessionLogSummaries(self):
    data = [
        {'session_log': SessionLog(status=SessionLog.START), 'step': 0},
        {'session_log': SessionLog(status=SessionLog.CHECKPOINT), 'step': 1},
        {'session_log': SessionLog(status=SessionLog.CHECKPOINT), 'step': 2},
        {'session_log': SessionLog(status=SessionLog.CHECKPOINT), 'step': 3},
        {'session_log': SessionLog(status=SessionLog.STOP), 'step': 4},
        {'session_log': SessionLog(status=SessionLog.START), 'step': 5},
        {'session_log': SessionLog(status=SessionLog.STOP), 'step': 6},
    ]

    self._WriteScalarSummaries(data)
    units = efi.get_inspection_units(self.logdir)
    self.assertEqual(1, len(units))
    printable = efi.get_dict_to_print(units[0].field_to_obs)
    self.assertEqual(printable['sessionlog:start']['steps'], [0, 5])
    self.assertEqual(printable['sessionlog:stop']['steps'], [4, 6])
    self.assertEqual(printable['sessionlog:checkpoint']['num_steps'], 3) 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def after_run(self, run_context, run_values):
    _ = run_context
    if not self._summary_writer:
      return

    stale_global_step = run_values.results["global_step"]
    global_step = stale_global_step + 1
    if self._next_step is None or self._request_summary:
      global_step = run_context.session.run(self._global_step_tensor)

    if self._next_step is None:
      self._summary_writer.add_session_log(
          SessionLog(status=SessionLog.START), global_step)

    if self._request_summary:
      self._timer.update_last_triggered_step(global_step)
      if "summary" in run_values.results:
        for summary in run_values.results["summary"]:
          self._summary_writer.add_summary(summary, global_step)

    self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def get_field_to_observations_map(generator, query_for_tag=''):
  """Return a field to `Observations` dict for the event generator.

  Args:
    generator: A generator over event protos.
    query_for_tag: A string that if specified, only create observations for
      events with this tag name.

  Returns:
    A dict mapping keys in `TRACKED_FIELDS` to an `Observation` list.
  """

  def increment(stat, event, tag=''):
    assert stat in TRACKED_FIELDS
    field_to_obs[stat].append(Observation(step=event.step,
                                          wall_time=event.wall_time,
                                          tag=tag)._asdict())

  field_to_obs = dict([(t, []) for t in TRACKED_FIELDS])

  for event in generator:
    ## Process the event
    if event.HasField('graph_def') and (not query_for_tag):
      increment('graph', event)
    if event.HasField('session_log') and (not query_for_tag):
      status = event.session_log.status
      if status == SessionLog.START:
        increment('sessionlog:start', event)
      elif status == SessionLog.STOP:
        increment('sessionlog:stop', event)
      elif status == SessionLog.CHECKPOINT:
        increment('sessionlog:checkpoint', event)
    elif event.HasField('summary'):
      for value in event.summary.value:
        if query_for_tag and value.tag != query_for_tag:
          continue

        for proto_name, display_name in SUMMARY_TYPE_TO_FIELD.items():
          if value.HasField(proto_name):
            increment(display_name, event, value.tag)
  return field_to_obs 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def start_standard_services(self, sess):
    """Start the standard services for 'sess'.

    This starts services in the background.  The services started depend
    on the parameters to the constructor and may include:

      - A Summary thread computing summaries every save_summaries_secs.
      - A Checkpoint thread saving the model every save_model_secs.
      - A StepCounter thread measure step time.

    Args:
      sess: A Session.

    Returns:
      A list of threads that are running the standard services.  You can use
      the Supervisor's Coordinator to join these threads with:
        sv.coord.Join(<list of threads>)

    Raises:
      RuntimeError: If called with a non-chief Supervisor.
      ValueError: If not `logdir` was passed to the constructor as the
        services need a log directory.
    """
    if not self._is_chief:
      raise RuntimeError("Only chief supervisor can start standard services. "
                         "Because only chief supervisors can write events.")

    if not self._logdir:
      logging.warning("Standard services need a 'logdir' "
                      "passed to the SessionManager")
      return

    if self._global_step is not None and self._summary_writer:
      # Only add the session log if we keep track of global step.
      # TensorBoard cannot use START message for purging expired events
      # if there is no step value.
      current_step = training_util.global_step(sess, self._global_step)
      self._summary_writer.add_session_log(
          SessionLog(status=SessionLog.START),
          current_step)

    threads = []
    if self._save_summaries_secs and self._summary_writer:
      if self._summary_op is not None:
        threads.append(SVSummaryThread(self, sess))
      if self._global_step is not None:
        threads.append(SVStepCounterThread(self, sess))
    if self.saver and self._save_model_secs:
      threads.append(SVTimerCheckpointThread(self, sess))
    for t in threads:
      t.start()
    return threads 

# 需要导入模块: from tensorflow.core.util.event_pb2 import SessionLog [as 别名]
# 或者: from tensorflow.core.util.event_pb2.SessionLog import START [as 别名]
def testCloseAndReopen(self):
    test_dir = self._CleanTestDir("close_and_reopen")
    sw = tf.train.SummaryWriter(test_dir)
    sw.add_session_log(tf.SessionLog(status=SessionLog.START), 1)
    sw.close()
    # Sleep at least one second to make sure we get a new event file name.
    time.sleep(1.2)
    sw.reopen()
    sw.add_session_log(tf.SessionLog(status=SessionLog.START), 2)
    sw.close()

    # We should now have 2 events files.
    event_paths = sorted(glob.glob(os.path.join(test_dir, "event*")))
    self.assertEquals(2, len(event_paths))

    # Check the first file contents.
    rr = tf.train.summary_iterator(event_paths[0])
    # The first event should list the file_version.
    ev = next(rr)
    self._assertRecent(ev.wall_time)
    self.assertEquals("brain.Event:2", ev.file_version)
    # The next event should be the START message.
    ev = next(rr)
    self._assertRecent(ev.wall_time)
    self.assertEquals(1, ev.step)
    self.assertEquals(SessionLog.START, ev.session_log.status)
    # We should be done.
    self.assertRaises(StopIteration, lambda: next(rr))

    # Check the second file contents.
    rr = tf.train.summary_iterator(event_paths[1])
    # The first event should list the file_version.
    ev = next(rr)
    self._assertRecent(ev.wall_time)
    self.assertEquals("brain.Event:2", ev.file_version)
    # The next event should be the START message.
    ev = next(rr)
    self._assertRecent(ev.wall_time)
    self.assertEquals(2, ev.step)
    self.assertEquals(SessionLog.START, ev.session_log.status)
    # We should be done.
    self.assertRaises(StopIteration, lambda: next(rr))

  # Checks that values returned from session Run() calls are added correctly to
  # summaries.  These are numpy types so we need to check they fit in the
  # protocol buffers correctly.