def bias_(self):
   hiddenlayer_bias = [load_variable(
       self._model_dir, name=("dnn/hiddenlayer_%d/biases" % i))
                       for i, _ in enumerate(self._hidden_units)]
   logits_bias = [load_variable(self._model_dir, name="dnn/logits/biases")]
   centered_bias = [load_variable(self._model_dir, name=_CENTERED_BIAS_WEIGHT)]
   return hiddenlayer_bias + logits_bias + centered_bias

 def bias_(self):
   hiddenlayer_bias = [load_variable(
       self._model_dir, name=("dnn/hiddenlayer_%d/biases" % i))
                       for i, _ in enumerate(self._hidden_units)]
   logits_bias = [load_variable(self._model_dir, name="dnn/logits/biases")]
   if self._estimator.params["enable_centered_bias"]:
     centered_bias = [
         load_variable(self._model_dir, name=_CENTERED_BIAS_WEIGHT)]
   else:
     centered_bias = []
   return hiddenlayer_bias + logits_bias + centered_bias

  def get_bias(self, model_dir):
    """Returns the bias of the model.

    Args:
      model_dir: Directory where model parameters, graph and etc. are saved.

    Returns:
      The bias weights created by this model.
    """
    return [
        load_variable(
            model_dir, name=(self._scope+"/hiddenlayer_%d/biases" % i))
        for i, _ in enumerate(self._hidden_units)
    ] + [load_variable(model_dir, name=(self._scope+"/logits/biases"))]

  def get_variable_value(self, name):
    """Returns value of the variable given by name.

    Args:
      name: string, name of the tensor.

    Returns:
      Numpy array - value of the tensor.
    """
    return load_variable(self.model_dir, name)

  def get_variable_value(self, name):
    """Returns value of the variable given by name.

    Args:
      name: string, name of the tensor.

    Returns:
      `Tensor` object.
    """
    return load_variable(self._model_dir, name)

 def weights_(self):
   values = {}
   optimizer_regex = r".*/"+self._optimizer.get_name() + r"(_\d)?$"
   for name, _ in list_variables(self._model_dir):
     if (name.startswith("linear/") and
         name != "linear/bias_weight" and
         not re.match(optimizer_regex, name)):
       values[name] = load_variable(self._model_dir, name)
   if len(values) == 1:
     return values[list(values.keys())[0]]
   return values

    def train(self, env, first_update=35, update_frequency=10, episodes=None, steps=None,
              hooks=None, max_steps=None, max_episodes=None):
        """Trains a model given an environment.

        Args:
            env: `Environment` instance.
            first_update: `int`. First timestep to calculate the loss and train_op for the model.
            update_frequency: `int`. The frequecncy at which to calcualate the loss and train_op.
            steps: Number of steps for which to train model. If `None`, train forever.
                'steps' works incrementally. If you call two times fit(steps=10) then
                training occurs in total 20 steps. If you don't want to have incremental
                behaviour please set `max_steps` instead. If set, `max_steps` must be
                `None`.
            hooks: 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. If set, `steps` must be `None`.
            max_episodes: Number of total episodes for which to train model. If `None`,
                train forever. If set, `episodes` must be `None`.

            Two calls to `fit(steps=100)` means 200 training iterations.
            On the other hand, two calls to `fit(max_steps=100)` means
            that the second call will not do any iteration since first call did all 100 steps.

        Returns:
            `self`, for chaining.
        """
        if not self.memory.can_sample(first_update):
            raise ValueError("Cannot update the model before gathering enough data")

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

        hooks = self._prepare_train(
            first_update, update_frequency, steps, hooks, max_steps, max_episodes)
        loss = self._train_model(env=env, first_update=first_update,
                                 update_frequency=update_frequency, hooks=hooks)
        logging.info('Loss for final step: %s.', loss)
        return self

    def _prepare_train(self, episodes=None, steps=None,
                       hooks=None, max_steps=None, max_episodes=None):
        hooks = super(BaseAgent, self)._prepare_train(steps=steps, hooks=hooks, max_steps=max_steps)

        if max_episodes is not None:
            try:
                start_episode = load_variable(self._model_dir, tf.GraphKeys.GLOBAL_EPISODE)
                if max_episodes <= start_episode:
                    logging.info('Skipping training since max_episode has already saved.')
                    return self
            except:  # pylint: disable=bare-except
                pass

        hooks = self._check_hooks(hooks)
        if steps is not None or max_steps is not None:
            hooks.append(plx_hooks.StopAtEpisodeHook(episodes, max_episodes))

        return hooks

  def get_weights(self, model_dir):
    """Returns weights per feature of the linear part.

    Args:
      model_dir: Directory where model parameters, graph and etc. are saved.

    Returns:
      The weights created by this model (without the optimizer weights).
    """
    all_variables = [name for name, _ in list_variables(model_dir)]
    values = {}
    optimizer_regex = r".*/" + self._get_optimizer().get_name() + r"(_\d)?$"
    for name in all_variables:
      if (name.startswith(self._scope + "/") and
          name != self._scope + "/bias_weight" and
          not re.match(optimizer_regex, name)):
        values[name] = load_variable(model_dir, name)
    if len(values) == 1:
      return values[list(values.keys())[0]]
    return values

    def train(self, input_fn=None, steps=None, hooks=None, max_steps=None):
        """Trains a model given training data `x` predictions and `y` labels.

        Args:
            input_fn: Input function returning a tuple of:
                features - `Tensor` or dictionary of string feature name to `Tensor`.
                labels - `Tensor` or dictionary of `Tensor` with labels.
            steps: Number of steps for which to train model. If `None`, train forever.
                'steps' works incrementally. If you call two times fit(steps=10) then
                training occurs in total 20 steps. If you don't want to have incremental
                behaviour please set `max_steps` instead. If set, `max_steps` must be
                `None`.
            hooks: 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. If set, `steps` must be `None`.

            Two calls to `fit(steps=100)` means 200 training iterations.
            On the other hand, two calls to `fit(max_steps=100)` means
            that the second call will not do any iteration since first call did all 100 steps.

        Returns:
            `self`, for chaining.
        """
        if max_steps is not None:
            try:
                start_step = load_variable(self._model_dir, ops.GraphKeys.GLOBAL_STEP)
                if max_steps <= start_step:
                    logging.info('Skipping training since max_steps has already saved.')
                    return self
            except:  # pylint: disable=bare-except
                pass

        hooks = self._prepare_train(steps, hooks, max_steps)
        loss = self._train_model(input_fn=input_fn, hooks=hooks)
        logging.info('Loss for final step: %s.', loss)
        return self

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.........

#.........这里部分代码省略.........
      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)

    scaffold = monitored_session.Scaffold(
        init_op=init_op,
        init_feed_dict=init_feed_dict,
        init_fn=init_fn,
        saver=monitored_session.Scaffold.get_or_default('saver',
                                                        ops.GraphKeys.SAVERS,
                                                        make_saver))

 def bias_(self):
   return load_variable(self._model_dir, name="linear/bias_weight")

 def weights_(self):
   hiddenlayer_weights = [load_variable(
       self._model_dir, name=("dnn/hiddenlayer_%d/weights" % i))
                          for i, _ in enumerate(self._hidden_units)]
   logits_weights = [load_variable(self._model_dir, name="dnn/logits/weights")]
   return hiddenlayer_weights + logits_weights

 def get_variable_value(self, name):
   return load_variable(self.model_dir, name)