def _train_internal(graph,
                    output_dir,
                    train_op,
                    loss_op,
                    global_step_tensor,
                    init_op,
                    init_feed_dict,
                    init_fn,
                    log_every_steps,
                    supervisor_is_chief,
                    supervisor_master,
                    supervisor_save_model_secs,
                    keep_checkpoint_max,
                    supervisor_save_summaries_steps,
                    feed_fn,
                    steps,
                    fail_on_nan_loss,
                    monitors,
                    max_steps):
  """See train."""
  if (steps is not None) and (max_steps is not None):
    raise ValueError('Can not provide both steps and max_steps.')
  if not output_dir:
    raise ValueError('Output directory should be non-empty %s.' % output_dir)
  if train_op is None:
    raise ValueError('Missing train_op.')
  if loss_op is None:
    raise ValueError('Missing loss_op.')

  with graph.as_default():
    global_step_tensor = contrib_variables.assert_or_get_global_step(
        graph, global_step_tensor)
    if global_step_tensor is None:
      raise ValueError('No "global_step" was provided or found in the graph.')

    # Get current step.
    try:
      start_step = load_variable(output_dir, global_step_tensor.name)
    except (errors.NotFoundError, ValueError):
      start_step = 0

    summary_writer = (get_summary_writer(output_dir)
                      if supervisor_is_chief else None)

    # Add default chief monitors if none were provided.
    if not monitors:
      monitors = monitors_lib.get_default_monitors(
          loss_op=loss_op,
          summary_op=logging_ops.get_summary_op(),
          save_summary_steps=supervisor_save_summaries_steps,
          summary_writer=summary_writer) if supervisor_is_chief else []

    # TODO(ipolosukhin): Replace all functionality of Supervisor
    # with Chief-Exclusive Monitors.
    if not supervisor_is_chief:
      # Prune list of monitor to the ones runnable on all workers.
      monitors = [monitor for monitor in monitors if monitor.run_on_all_workers]

    if max_steps is None:
      max_steps = (start_step + steps) if steps else None
    # Start monitors, can create graph parts.
    for monitor in monitors:
      monitor.begin(max_steps=max_steps)

  supervisor = tf_supervisor.Supervisor(
      graph,
      init_op=init_op or tf_supervisor.Supervisor.USE_DEFAULT,
      init_feed_dict=init_feed_dict,
      is_chief=supervisor_is_chief,
      logdir=output_dir,
      saver=_make_saver(graph, keep_checkpoint_max),
      global_step=global_step_tensor,
      summary_op=None,
      summary_writer=summary_writer,
      save_model_secs=supervisor_save_model_secs,
      init_fn=init_fn)
  session = supervisor.PrepareSession(master=supervisor_master,
                                      start_standard_services=True)
  supervisor.StartQueueRunners(session)

  with session:
    get_current_step = lambda: session.run(global_step_tensor)

    start_step = get_current_step()
    last_step = start_step
    last_log_step = start_step
    loss_value = None
    logging.info('Training steps [%d,%s)', last_step, 'inf'
                 if max_steps is None else str(max_steps))

    excinfo = None
    try:
      while not supervisor.ShouldStop() and (
          (max_steps is None) or (last_step < max_steps)):
        start_time = time.time()
        feed_dict = feed_fn() if feed_fn is not None else None

        outputs, should_stop = _run_with_monitors(
            session, last_step + 1, [train_op, loss_op], feed_dict, monitors)

#.........这里部分代码省略.........

def evaluate(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             update_op=None,
             global_step_tensor=None,
             supervisor_master='',
             log_every_steps=10,
             feed_fn=None,
             max_steps=None):
  """Evaluate a model loaded from a checkpoint.

  Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
  to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
  loop for `max_steps` steps, or until an exception (generally, an
  end-of-input signal from a reader operation) is raised from running
  `eval_dict`.

  In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
  every `log_every_steps` steps, they are logged. At the very end of evaluation,
  a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
  and written to `output_dir`.

  Args:
    graph: A `Graph` to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A string containing the directory to write a summary to.
    checkpoint_path: A string containing the path to a checkpoint to restore.
      Can be `None` if the graph doesn't require loading any variables.
    eval_dict: A `dict` mapping string names to tensors to evaluate. It is
      evaluated in every logging step. The result of the final evaluation is
      returned. If `update_op` is None, then it's evaluated in every step. If
      `max_steps` is `None`, this should depend on a reader that will raise an
      end-of-input exception when the inputs are exhausted.
    update_op: A `Tensor` which is run in every step.
    global_step_tensor: A `Variable` containing the global step. If `None`,
      one is extracted from the graph using the same logic as in `Supervisor`.
      Used to place eval summaries on training curves.
    supervisor_master: The master string to use when preparing the session.
    log_every_steps: Integer. Output logs every `log_every_steps` evaluation
      steps. The logs contain the `eval_dict` and timing information.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    max_steps: Integer. Evaluate `eval_dict` this many times.

  Returns:
    A tuple `(eval_results, global_step)`:
    eval_results: A `dict` mapping `string` to numeric values (`int`, `float`)
      that are the result of running eval_dict in the last step. `None` if no
      eval steps were run.
    global_step: The global step this evaluation corresponds to.

  Raises:
    ValueError: if `output_dir` is empty.
  """
  if not output_dir:
    raise ValueError('Output directory should be non-empty %s.' % output_dir)
  with graph.as_default():
    global_step_tensor = contrib_variables.assert_or_get_global_step(
        graph, global_step_tensor)

    # Create or get summary op, global_step and saver.
    saver = _get_saver()
    local_init_op = _get_local_init_op()
    ready_op = _get_ready_op()

    session_manager = session_manager_lib.SessionManager(
        local_init_op=local_init_op,
        ready_op=ready_op)
    session, initialized = session_manager.recover_session(
        master=supervisor_master,
        saver=saver,
        checkpoint_dir=checkpoint_path)

    # Start queue runners.
    coord = coordinator.Coordinator()
    threads = queue_runner.start_queue_runners(session, coord)

  with session:
    if not initialized:
      logging.warning('Failed to initialize from %s.', checkpoint_path)
      # TODO(ipolosukhin): This should be failing, but old code relies on that.
      session.run(variables.global_variables_initializer())
      if checkpoint_path:
        _restore_from_checkpoint(session, graph, checkpoint_path, saver)

    current_global_step = session.run(global_step_tensor)
    eval_results = None
    # TODO(amodei): Fix this to run through the eval set exactly once.
    step = 0
    eval_step = None
    feed_dict = None
    logging.info('Eval steps [%d,%s) for training step %d.', step,
                 'inf' if max_steps is None
                 else str(max_steps), current_global_step)
    try:
      try:
        while (max_steps is None) or (step < max_steps):
          step += 1
          start_time = time.time()
#.........这里部分代码省略.........

def _supervised_train(graph,
                      output_dir,
                      train_op,
                      loss_op,
                      global_step_tensor=None,
                      init_op=None,
                      init_feed_dict=None,
                      init_fn=None,
                      log_every_steps=10,
                      supervisor_is_chief=True,
                      supervisor_master='',
                      supervisor_save_model_steps=1000,
                      keep_checkpoint_max=5,
                      supervisor_save_summaries_steps=100,
                      feed_fn=None,
                      steps=None,
                      fail_on_nan_loss=True,
                      monitors=None,
                      max_steps=None):
  """Train a model via supervised_session.

  Given `graph`, a directory to write outputs to (`output_dir`), and some ops,
  run a training loop. The given `train_op` performs one step of training on the
  model. The `loss_op` represents the objective function of the training. It is
  expected to increment the `global_step_tensor`, a scalar integer tensor
  counting training steps. This function uses `Supervisor` to initialize the
  graph (from a checkpoint if one is available in `output_dir`), write summaries
  defined in the graph, and write regular checkpoints as defined by
  `supervisor_save_model_secs`.

  Training continues until `global_step_tensor` evaluates to `max_steps`, or, if
  `fail_on_nan_loss`, until `loss_op` evaluates to `NaN`. In that case the
  program is terminated with exit code 1.

  Args:
    graph: A graph to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A directory to write outputs to.
    train_op: An op that performs one training step when run.
    loss_op: A scalar loss tensor.
    global_step_tensor: A tensor representing the global step. If none is given,
      one is extracted from the graph using the same logic as in `Supervisor`.
    init_op: An op that initializes the graph. If `None`, use `Supervisor`'s
      default.
    init_feed_dict: A dictionary that maps `Tensor` objects to feed values.
      This feed dictionary will be used when `init_op` is evaluated.
    init_fn: Optional callable passed to Supervisor to initialize the model.
    log_every_steps: Output logs regularly. The logs contain timing data and the
      current loss.
    supervisor_is_chief: Whether the current process is the chief supervisor in
      charge of restoring the model and running standard services.
    supervisor_master: The master string to use when preparing the session.
    supervisor_save_model_steps: Save a checkpoint every
      `supervisor_save_model_steps` steps when training.
    keep_checkpoint_max: The maximum number of recent checkpoint files to
      keep. As new files are created, older files are deleted. If None or 0,
      all checkpoint files are kept. This is simply passed as the max_to_keep
      arg to tf.Saver constructor.
    supervisor_save_summaries_steps: Save summaries every
      `supervisor_save_summaries_steps` seconds when training.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    steps: Trains for this many steps (e.g. current global step + `steps`).
    fail_on_nan_loss: If true, raise `NanLossDuringTrainingError` if `loss_op`
      evaluates to `NaN`. If false, continue training as if nothing happened.
    monitors: List of `BaseMonitor` subclass instances. Used for callbacks
      inside the training loop.
    max_steps: Number of total steps for which to train model. If `None`,
      train forever. Two calls fit(steps=100) means 200 training iterations.
      On the other hand two calls of fit(max_steps=100) means, second call
      will not do any iteration since first call did all 100 steps.

  Returns:
    The final loss value.

  Raises:
    ValueError: If `output_dir`, `train_op`, `loss_op`, or `global_step_tensor`
      is not provided. See `tf.contrib.framework.get_global_step` for how we
      look up the latter if not provided explicitly.
    NanLossDuringTrainingError: If `fail_on_nan_loss` is `True`, and loss ever
      evaluates to `NaN`.
    ValueError: If both `steps` and `max_steps` are not `None`.
  """
  if (steps is not None) and (max_steps is not None):
    raise ValueError('Can not provide both steps and max_steps.')
  if not output_dir:
    raise ValueError('Output directory should be non-empty %s.' % output_dir)
  if train_op is None:
    raise ValueError('Missing train_op.')
  if loss_op is None:
    raise ValueError('Missing loss_op.')
  if monitors is None:
    monitors = []
  if not isinstance(monitors, list):
    raise ValueError('Monitors should be a list.')
  with graph.as_default():
    global_step_tensor = contrib_variables.assert_or_get_global_step(
        graph, global_step_tensor)
  if global_step_tensor is None:
    raise ValueError('No "global_step" was provided or found in the graph.')
#.........这里部分代码省略.........

#.........这里部分代码省略.........
    fail_on_nan_loss: If true, raise `NanLossDuringTrainingError` if `loss_op`
      evaluates to `NaN`. If false, continue training as if nothing happened.
    hooks: List of `SessionRunHook` subclass instances. Used for callbacks
      inside the training loop.
    max_steps: Number of total steps for which to train model. If `None`,
      train forever. Two calls fit(steps=100) means 200 training iterations.
      On the other hand two calls of fit(max_steps=100) means, second call
      will not do any iteration since first call did all 100 steps.

  Returns:
    The final loss value.

  Raises:
    ValueError: If `output_dir`, `train_op`, `loss_op`, or `global_step_tensor`
      is not provided. See `tf.contrib.framework.get_global_step` for how we
      look up the latter if not provided explicitly.
    NanLossDuringTrainingError: If `fail_on_nan_loss` is `True`, and loss ever
      evaluates to `NaN`.
    ValueError: If both `steps` and `max_steps` are not `None`.
  """
  if (steps is not None) and (max_steps is not None):
    raise ValueError('Can not provide both steps and max_steps.')
  if not output_dir:
    raise ValueError('Output directory should be non-empty %s.' % output_dir)
  if train_op is None:
    raise ValueError('Missing train_op.')
  if loss_op is None:
    raise ValueError('Missing loss_op.')
  if hooks is None:
    hooks = []
  if not isinstance(hooks, list):
    raise ValueError('Hooks should be a list.')
  with graph.as_default():
    global_step_tensor = contrib_variables.assert_or_get_global_step(
        graph, global_step_tensor)
  if global_step_tensor is None:
    raise ValueError('No "global_step" was provided or found in the graph.')

  if max_steps is not None:
    try:
      start_step = load_variable(output_dir, global_step_tensor.name)
      if max_steps <= start_step:
        logging.info('Skipping training since max_steps has already saved.')
        return None
    except:  # pylint: disable=bare-except
      pass

  # Adapted SessionRunHooks such as ExportMonitor depend on the
  # CheckpointSaverHook to be executed before they should be executed.
  # The `hooks` param comprises of deprecated monitor hooks
  # (such as ExportMonitor). Appending them after the basic_session_run_hooks.
  all_hooks = []
  with graph.as_default():
    all_hooks.append(basic_session_run_hooks.NanTensorHook(
        loss_op, fail_on_nan_loss=fail_on_nan_loss))
    if log_every_steps > 0:
      all_hooks.append(basic_session_run_hooks.LoggingTensorHook({
          'loss': loss_op.name,
          'step': global_step_tensor.name
      }, every_n_iter=log_every_steps))

    def make_saver():
      return tf_saver.Saver(
          sharded=True, max_to_keep=keep_checkpoint_max, defer_build=True,
          write_version=saver_pb2.SaverDef.V1)

def train(graph,
          output_dir,
          train_op,
          loss_op,
          global_step_tensor=None,
          init_op=None,
          init_feed_dict=None,
          init_fn=None,
          log_every_steps=10,
          supervisor_is_chief=True,
          supervisor_master='',
          supervisor_save_model_secs=600,
          supervisor_save_summaries_steps=100,
          feed_fn=None,
          steps=None,
          fail_on_nan_loss=True,
          monitors=None):
  """Train a model.

  Given `graph`, a directory to write outputs to (`output_dir`), and some ops,
  run a training loop. The given `train_op` performs one step of training on the
  model. The `loss_op` represents the objective function of the training. It is
  expected to increment the `global_step_tensor`, a scalar integer tensor
  counting training steps. This function uses `Supervisor` to initialize the
  graph (from a checkpoint if one is available in `output_dir`), write summaries
  defined in the graph, and write regular checkpoints as defined by
  `supervisor_save_model_secs`.

  Training continues until `global_step_tensor` evaluates to `max_steps`, or, if
  `fail_on_nan_loss`, until `loss_op` evaluates to `NaN`. In that case the
  program is terminated with exit code 1.

  Args:
    graph: A graph to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A directory to write outputs to.
    train_op: An op that performs one training step when run.
    loss_op: A scalar loss tensor.
    global_step_tensor: A tensor representing the global step. If none is given,
      one is extracted from the graph using the same logic as in `Supervisor`.
    init_op: An op that initializes the graph. If `None`, use `Supervisor`'s
      default.
    init_feed_dict: A dictionary that maps `Tensor` objects to feed values.
      This feed dictionary will be used when `init_op` is evaluated.
    init_fn: Optional callable passed to Supervisor to initialize the model.
    log_every_steps: Output logs regularly. The logs contain timing data and the
      current loss.
    supervisor_is_chief: Whether the current process is the chief supervisor in
      charge of restoring the model and running standard services.
    supervisor_master: The master string to use when preparing the session.
    supervisor_save_model_secs: Save a checkpoint every
      `supervisor_save_model_secs` seconds when training.
    supervisor_save_summaries_steps: Save summaries every
      `supervisor_save_summaries_steps` seconds when training.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    steps: Trains for this many steps (e.g. current global step + `steps`).
    fail_on_nan_loss: If true, raise `NanLossDuringTrainingError` if `loss_op`
      evaluates to `NaN`. If false, continue training as if nothing happened.
    monitors: List of `BaseMonitor` subclass instances. Used for callbacks
      inside the training loop.

  Returns:
    The final loss value.

  Raises:
    ValueError: If `global_step_tensor` is not provided. See
        `tf.contrib.framework.get_global_step` for how we look it up if not
        provided explicitly.
    NanLossDuringTrainingError: If `fail_on_nan_loss` is `True`, and loss ever
        evaluates to `NaN`.
  """
  if not output_dir:
    raise ValueError('Output directory should be non-empty.')

  with graph.as_default():
    global_step_tensor = contrib_variables.assert_or_get_global_step(
        graph, global_step_tensor)
    if global_step_tensor is None:
      raise ValueError('No "global_step" was provided or found in the graph.')

    # Get current step.
    try:
      start_step = checkpoints.load_variable(
          output_dir, global_step_tensor.name)
    except (errors.NotFoundError, ValueError):
      start_step = 0

    summary_writer = (get_summary_writer(output_dir)
                      if supervisor_is_chief else None)

    # TODO(ipolosukhin): Replace all functionality of Supervisor with Monitors.
    if not supervisor_is_chief:
      # monitors should run only on the chief.
      monitors = []
    elif not monitors:
      monitors = monitors_lib.get_default_monitors(
          loss_op=loss_op,
          summary_op=logging_ops.get_summary_op(),
          save_summary_steps=supervisor_save_summaries_steps,
#.........这里部分代码省略.........

def evaluate(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             update_op=None,
             global_step_tensor=None,
             supervisor_master='',
             log_every_steps=10,
             feed_fn=None,
             max_steps=None):
  """Evaluate a model loaded from a checkpoint.

  Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
  to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
  loop for `max_steps` steps.

  In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
  every `log_every_steps` steps, they are logged. At the very end of evaluation,
  a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
  and written to `output_dir`.

  Args:
    graph: A `Graph` to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A string containing the directory to write a summary to.
    checkpoint_path: A string containing the path to a checkpoint to restore.
      Can be `None` if the graph doesn't require loading any variables.
    eval_dict: A `dict` mapping string names to tensors to evaluate. It is
      evaluated in every logging step. The result of the final evaluation is
      returned. If update_op is None, then it's evaluated in every step.
    update_op: A `Tensor` which is run in every step.
    global_step_tensor: A `Variable` containing the global step. If `None`,
      one is extracted from the graph using the same logic as in `Supervisor`.
      Used to place eval summaries on training curves.
    supervisor_master: The master string to use when preparing the session.
    log_every_steps: Integer. Output logs every `log_every_steps` evaluation
      steps. The logs contain the `eval_dict` and timing information.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    max_steps: Integer. Evaluate `eval_dict` this many times.

  Returns:
    A tuple `(eval_results, global_step)`:
    eval_results: A `dict` mapping `string` to numeric values (`int`, `float`)
      that are the result of running eval_dict in the last step. `None` if no
      eval steps were run.
    global_step: The global step this evaluation corresponds to.
  """
  global_step_tensor = contrib_variables.assert_or_get_global_step(
      graph, global_step_tensor)

  # Add scalar summaries for every tensor in evaluation dict if there is not
  # one existing already or it's a string.
  existing_tags = [tensor_util.constant_value(summary.op.inputs[0])
                   for summary in ops.get_collection(ops.GraphKeys.SUMMARIES)]
  for key, value in eval_dict.items():
    if key in existing_tags:
      continue
    if isinstance(value, ops.Tensor):
      summaries.summarize_tensor(value, tag=key)

  # Create or get summary op, global_step and saver.
  summary_op = logging_ops.get_summary_op()
  saver = _get_saver()
  local_init_op = _get_local_init_op()
  ready_op = _get_ready_op()

  session_manager = session_manager_lib.SessionManager(
      local_init_op=local_init_op,
      ready_op=ready_op)
  session, initialized = session_manager.recover_session(
      master=supervisor_master,
      saver=saver,
      checkpoint_dir=checkpoint_path)

  # Start queue runners.
  coord = coordinator.Coordinator()
  threads = _start_queue_runners(session, coord)

  with session:
    if not initialized:
      logging.warning('Failed to initialize from %s.', checkpoint_path)
      # TODO(ipolosukhin): This should be failing, but old code relies on that.
      session.run(variables.initialize_all_variables())
      if checkpoint_path:
        _restore_from_checkpoint(session, graph, checkpoint_path, saver)

    current_global_step = session.run(global_step_tensor)
    eval_results = None
    # TODO(amodei): Fix this to run through the eval set exactly once.
    step = 0
    logging.info('Eval steps [%d,%s) for training step %d.', step,
                 'inf' if max_steps is None
                 else str(max_steps), current_global_step)
    try:
      try:
        while (max_steps is None) or (step < max_steps):
          start_time = time.time()
          feed_dict = feed_fn() if feed_fn is not None else None
          eval_results = None
#.........这里部分代码省略.........

def evaluate(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             global_step_tensor=None,
             init_op=None,
             supervisor_master='',
             log_every_steps=10,
             feed_fn=None,
             max_steps=None):
  """Evaluate a model loaded from a checkpoint.

  Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
  to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
  loop for `max_steps` steps.

  In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
  every `log_every_steps` steps, they are logged. At the very end of evaluation,
  a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
  and written to `output_dir`.

  Args:
    graph: A `Graph` to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A string containing the directory to write a summary to.
    checkpoint_path: A string containing the path to a checkpoint to restore.
      Can be `None` if the graph doesn't require loading any variables.
    eval_dict: A `dict` mapping string names to tensors to evaluate for in every
      eval step.
    global_step_tensor: A `Variable` containing the global step. If `None`,
      one is extracted from the graph using the same logic as in `Supervisor`.
      Used to place eval summaries on training curves.
    init_op: An op that initializes the graph. If `None`, use `Supervisor`'s
      default.
    supervisor_master: The master string to use when preparing the session.
    log_every_steps: Integer. Output logs every `log_every_steps` evaluation
      steps. The logs contain the `eval_dict` and timing information.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    max_steps: Integer. Evaluate `eval_dict` this many times.

  Returns:
    A tuple `(eval_results, global_step)`:
    eval_results: A `dict` mapping `string` to numeric values (`int`, `float`)
      that are the eval results from the last step of the eval.  None if no
      eval steps were run.
    global_step: The global step this evaluation corresponds to.
  """
  global_step_tensor = contrib_variables.assert_or_get_global_step(
      graph, global_step_tensor)

  # Add scalar summaries for every tensor in evaluation dict if there is not
  # one existing already or it's a string.
  existing_tags = [tensor_util.constant_value(summary.op.inputs[0])
                   for summary in ops.get_collection(ops.GraphKeys.SUMMARIES)]
  for key, value in eval_dict.items():
    if key in existing_tags:
      continue
    if isinstance(value, ops.Tensor):
      summaries.summarize_tensor(value, tag=key)

  # TODO(wicke): Don't use supervisor here, or switch to output_dir=eval_dir.
  supervisor, session = _prepare_session(
      graph=graph,
      output_dir=None,  # Must be None to avoid writing an event file
      start_services=False,
      global_step_tensor=global_step_tensor,
      init_op=init_op,
      supervisor_is_chief=True,
      supervisor_master=supervisor_master,
      supervisor_save_model_secs=None,
      supervisor_save_summaries_secs=None)
  global_step_tensor = supervisor.global_step

  with session:
    if checkpoint_path:
      _restore_from_checkpoint(
          session, graph, checkpoint_path, supervisor.saver)

    current_global_step = session.run(global_step_tensor)
    eval_results = None
    # TODO(amodei): Fix this to run through the eval set exactly once.
    step = 0
    logging.info('Eval steps [%d,%s)', step, 'inf' if max_steps is None
                 else str(max_steps))
    try:
      try:
        while not supervisor.ShouldStop() and (
            (max_steps is None) or (step < max_steps)):
          start_time = time.time()
          feed_dict = feed_fn() if feed_fn is not None else None
          eval_results = _run_dict(session, eval_dict, feed_dict=feed_dict)
          # TODO(wicke): We should assert that the global step hasn't changed.
          step += 1
          if step % log_every_steps == 0:
            duration = time.time() - start_time
            logging.info('Results after %d steps (%.3f sec/batch): %s.',
                         step, float(duration),
                         ', '.join('%s = %s' % (k, v)
                                   for k, v in eval_results.items()))
#.........这里部分代码省略.........

def train(graph,
          output_dir,
          train_op,
          loss_op,
          global_step_tensor=None,
          init_op=None,
          init_fn=None,
          log_every_steps=10,
          supervisor_is_chief=True,
          supervisor_master='',
          supervisor_save_model_secs=600,
          supervisor_save_summaries_secs=10,
          feed_fn=None,
          max_steps=None,
          fail_on_nan_loss=True):
  """Train a model.

  Given `graph`, a directory to write outputs to (`output_dir`), and some ops,
  run a training loop. The given `train_op` performs one step of training on the
  model. The `loss_op` represents the objective function of the training. It is
  expected to increment the `global_step_tensor`, a scalar integer tensor
  counting training steps. This function uses `Supervisor` to initialize the
  graph (from a checkpoint if one is available in `output_dir`), write summaries
  defined in the graph, and write regular checkpoints as defined by
  `supervisor_save_model_secs`.

  Training continues until `global_step_tensor` evaluates to `max_steps`, or, if
  `fail_on_nan_loss`, until `loss_op` evaluates to `NaN`. In that case the
  program is terminated with exit code 1.

  Args:
    graph: A graph to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A directory to write outputs to.
    train_op: An op that performs one training step when run.
    loss_op: A scalar loss tensor.
    global_step_tensor: A tensor representing the global step. If none is given,
      one is extracted from the graph using the same logic as in `Supervisor`.
    init_op: An op that initializes the graph. If `None`, use `Supervisor`'s
      default.
    init_fn: Optional callable passed to Supervisor to initialize the model.
    log_every_steps: Output logs regularly. The logs contain timing data and the
      current loss.
    supervisor_is_chief: Whether the current process is the chief supervisor in
      charge of restoring the model and running standard services.
    supervisor_master: The master string to use when preparing the session.
    supervisor_save_model_secs: Save a checkpoint every
      `supervisor_save_model_secs` seconds when training.
    supervisor_save_summaries_secs: Save summaries every
      `supervisor_save_summaries_secs` seconds when training.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    max_steps: Train until `global_step_tensor` evaluates to this value.
    fail_on_nan_loss: If true, raise `NanLossDuringTrainingError` if `loss_op`
      evaluates to `NaN`. If false, continue training as if nothing happened.

  Returns:
    The final loss value.

  Raises:
    ValueError: If `global_step_tensor` is not provided. See
        `tf.contrib.framework.get_global_step` for how we look it up if not
        provided explicitly.
    NanLossDuringTrainingError: If `fail_on_nan_loss` is `True`, and loss ever
        evaluates to `NaN`.
  """
  global_step_tensor = contrib_variables.assert_or_get_global_step(
      graph, global_step_tensor)
  if global_step_tensor is None:
    raise ValueError('No "global_step" was provided or found in the graph.')
  supervisor, session = _prepare_session(
      graph=graph,
      output_dir=output_dir,
      start_services=True,
      global_step_tensor=global_step_tensor,
      init_op=init_op,
      init_fn=init_fn,
      supervisor_is_chief=supervisor_is_chief,
      supervisor_master=supervisor_master,
      supervisor_save_model_secs=supervisor_save_model_secs,
      supervisor_save_summaries_secs=supervisor_save_summaries_secs)

  with session:
    get_current_step = lambda: session.run(global_step_tensor)

    start_step = get_current_step()
    last_step = start_step
    last_log_step = start_step
    loss_value = None
    logging.info('Training steps [%d,%s)', last_step, 'inf'
                 if max_steps is None else str(max_steps))
    excinfo = None
    try:
      while not supervisor.ShouldStop() and (
          (max_steps is None) or (last_step < max_steps)):
        start_time = time.time()
        feed_dict = feed_fn() if feed_fn is not None else None
        _, loss_value = session.run([train_op, loss_op], feed_dict=feed_dict)
        if np.isnan(loss_value):
          failure_message = 'Model diverged with loss = NaN.'
#.........这里部分代码省略.........

def evaluate(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             global_step_tensor=None,
             init_op=None,
             supervisor_master='',
             log_every_steps=10,
             max_steps=None,
             max_global_step=None,
             tuner=None,
             tuner_metric=None):
  """Evaluate a model loaded from a checkpoint.

  Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
  to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
  loop for `max_steps` steps.

  In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
  every `log_every_steps` steps, they are logged. At the very end of evaluation,
  a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
  and written to `output_dir`.

  Args:
    graph: A `Graph` to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A string containing the directory to write a summary to.
    checkpoint_path: A string containing the path to a checkpoint to restore.
      Can be `None` if the graph doesn't require loading any variables.
    eval_dict: A `dict` mapping string names to tensors to evaluate for in every
      eval step.
    global_step_tensor: A `Variable` containing the global step. If `None`,
      one is extracted from the graph using the same logic as in `Supervisor`.
      Used to place eval summaries on training curves.
    init_op: An op that initializes the graph. If `None`, use `Supervisor`'s
      default.
    supervisor_master: The master string to use when preparing the session.
    log_every_steps: Integer. Output logs every `log_every_steps` evaluation
      steps. The logs contain the `eval_dict` and timing information.
    max_steps: Integer. Evaluate `eval_dict` this many times.
    max_global_step: Integer.  If the global_step is larger than this, skip
      the eval and return None.
    tuner: A `Tuner` that will be notified of eval completion and updated
      with objective metrics.
    tuner_metric: A `string` that specifies the eval metric to report to
      `tuner`.

  Returns:
    A tuple `(eval_results, should_stop)`:
    eval_results: A `dict` mapping `string` to numeric values (`int`, `float`)
      that are the eval results from the last step of the eval.  None if no
      eval steps were run.
    should stop: A `bool`, indicating whether it was detected that eval should
      stop.

  Raises:
    ValueError: if the caller specifies max_global_step without providing
      a global_step.
  """
  # TODO(mdan): Move the tuner code outside eval.
  # The func returns sufficient information for the tuner to use.
  if tuner and not tuner_metric:
    raise ValueError('A tuner metric must be specified when using a tuner.')
  global_step_tensor = contrib_variables.assert_or_get_global_step(
      graph, global_step_tensor)

  # TODO(wicke): Don't use supervisor here, or switch to output_dir=eval_dir.
  supervisor, session = _prepare_session(
      graph=graph,
      output_dir=None,  # Must be None to avoid writing an event file
      start_services=False,
      global_step_tensor=global_step_tensor,
      init_op=init_op,
      supervisor_is_chief=True,
      supervisor_master=supervisor_master,
      supervisor_save_model_secs=None,
      supervisor_save_summaries_secs=None)
  global_step_tensor = supervisor.global_step

  with session:
    if checkpoint_path:
      _restore_from_checkpoint(
          session, graph, checkpoint_path, supervisor.saver)

    # When detecting that eval should stop, we still run one final measurement.
    should_stop = False
    # Check the global step.
    current_global_step = session.run(global_step_tensor)
    if max_global_step > 0 and current_global_step >= max_global_step:
      should_stop = True
      # TODO(mdan): Add support for more complex stop conditions.
    # Check for stop requests, if a tuner is present.
    if tuner and tuner.should_experiment_stop():
      should_stop = True

    eval_results = None
    # TODO(amodei): Fix this to run through the eval set exactly once.
    step = 0
    logging.info('Eval steps [%d,%s)', step, 'inf' if max_steps is None
                 else str(max_steps))
#.........这里部分代码省略.........