Python SummaryWriterCache.get方法代码示例

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def after_run(self, run_context, run_values):
        results = run_values.results
        global_step = results.get('global_step')

        if self._draw_images:
            self._timer.update_last_triggered_step(global_step)
            prediction_dict = results.get('prediction_dict')
            if prediction_dict is not None:
                summaries = image_vis_summaries(
                    prediction_dict, config=self._config,
                    image_visualization_mode=self._image_visualization_mode,
                    image=results.get('image'),
                    gt_bboxes=results.get('gt_bboxes')
                )
                if self._summary_writer is not None:
                    for summary in summaries:
                        self._summary_writer.add_summary(summary, global_step)

        self._next_step = global_step + 1 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def __init__(self,
               every_n_steps=100,
               every_n_secs=None,
               output_dir=None,
               summary_writer=None):
    self._summary_tag = "global_step/sec"

    if (every_n_steps is None) == (every_n_secs is None):
      raise ValueError(
          "exactly one of every_n_steps and every_n_secs should be provided.")
    self._timer = _SecondOrStepTimer(every_steps=every_n_steps,
                                     every_secs=every_n_secs)

    self._summary_writer = summary_writer
    if summary_writer is None and output_dir:
      self._summary_writer = SummaryWriterCache.get(output_dir) 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def after_run(self, run_context, run_values):
    _ = run_context

    stale_global_step = run_values.results
    if self._timer.should_trigger_for_step(stale_global_step+1):
      # get the real value after train op.
      global_step = run_context.session.run(self._global_step_tensor)
      if self._timer.should_trigger_for_step(global_step):
        elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
            global_step)
        if elapsed_time is not None:
          steps_per_sec = elapsed_steps / elapsed_time
          if self._summary_writer is not None:
            summary = Summary(value=[Summary.Value(
                tag=self._summary_tag, simple_value=steps_per_sec)])
            self._summary_writer.add_summary(summary, global_step)
          logging.info("%s: %g", self._summary_tag, steps_per_sec) 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def __init__(self,
               checkpoint_dir,
               save_secs=None,
               save_steps=None,
               saver=None,
               checkpoint_basename="model.ckpt",
               scaffold=None,
               listeners=None):
    """Initializes a `CheckpointSaverHook`.

    Args:
      checkpoint_dir: `str`, base directory for the checkpoint files.
      save_secs: `int`, save every N secs.
      save_steps: `int`, save every N steps.
      saver: `Saver` object, used for saving.
      checkpoint_basename: `str`, base name for the checkpoint files.
      scaffold: `Scaffold`, use to get saver object.
      listeners: List of `CheckpointSaverListener` subclass instances.
        Used for callbacks that run immediately before or after this hook saves
        the checkpoint.

    Raises:
      ValueError: One of `save_steps` or `save_secs` should be set.
      ValueError: Exactly one of saver or scaffold should be set.
    """
    logging.info("Create CheckpointSaverHook.")
    if saver is not None and scaffold is not None:
      raise ValueError("You cannot provide both saver and scaffold.")
    if saver is None and scaffold is None:
      saver = saver_lib._get_saver_or_default()  # pylint: disable=protected-access
    self._saver = saver
    self._checkpoint_dir = checkpoint_dir
    self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)
    self._scaffold = scaffold
    self._timer = SecondOrStepTimer(every_secs=save_secs,
                                    every_steps=save_steps)
    self._listeners = listeners or [] 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def begin(self):
    self._summary_writer = SummaryWriterCache.get(self._checkpoint_dir)
    self._global_step_tensor = training_util.get_global_step()
    if self._global_step_tensor is None:
      raise RuntimeError(
          "Global step should be created to use CheckpointSaverHook.")
    for l in self._listeners:
      l.begin() 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def __init__(self,
               checkpoint_dir,
               save_secs=None,
               save_steps=None,
               saver=None,
               checkpoint_basename="model.ckpt",
               scaffold=None,
               listeners=None):
    """Initialize CheckpointSaverHook monitor.

    Args:
      checkpoint_dir: `str`, base directory for the checkpoint files.
      save_secs: `int`, save every N secs.
      save_steps: `int`, save every N steps.
      saver: `Saver` object, used for saving.
      checkpoint_basename: `str`, base name for the checkpoint files.
      scaffold: `Scaffold`, use to get saver object.
      listeners: List of `CheckpointSaverListener` subclass instances.
        Used for callbacks that run immediately after the corresponding
        CheckpointSaverHook callbacks, only in steps where the
        CheckpointSaverHook was triggered.

    Raises:
      ValueError: One of `save_steps` or `save_secs` should be set.
      ValueError: Exactly one of saver or scaffold should be set.
    """
    logging.info("Create CheckpointSaverHook.")
    if ((saver is None and scaffold is None) or
        (saver is not None and scaffold is not None)):
      raise ValueError("Exactly one of saver or scaffold must be provided.")
    self._saver = saver
    self._checkpoint_dir = checkpoint_dir
    self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)
    self._scaffold = scaffold
    self._timer = SecondOrStepTimer(every_secs=save_secs,
                                    every_steps=save_steps)
    self._listeners = listeners or [] 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def visualize_embeddings(logdir, var_list, tsv_list):
  assert len(var_list) == len(tsv_list), 'Inconsistent length of lists'

  config = projector.ProjectorConfig()
  for v, f in zip(var_list, tsv_list):
    embedding = config.embeddings.add()
    embedding.tensor_name = v.name
    if f is not None:
      _, filename = os.path.split(f)
      meta_tsv = os.path.join(logdir, filename)
      tf.gfile.Copy(f, meta_tsv)  
      embedding.metadata_path = filename  # save relative path

  writer = SummaryWriterCache.get(logdir)
  projector.visualize_embeddings(writer, config) 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def get_inference_input():
    """Set up placeholders for input features/labels.

    Returns the feature, output tensors that get passed into model_fn."""
    return (tf.placeholder(tf.float32,
                           [None, go.N, go.N, features.NEW_FEATURES_PLANES],
                           name='pos_tensor'),
            {'pi_tensor': tf.placeholder(tf.float32, [None, go.N * go.N + 1]),
             'value_tensor': tf.placeholder(tf.float32, [None])}) 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def begin(self):
        # These calls only works because the SessionRunHook api guarantees this
        # will get called within a graph context containing our model graph.

        self.summary_writer = SummaryWriterCache.get(self.working_dir)
        self.weight_tensors = tf.trainable_variables()
        self.global_step = tf.train.get_or_create_global_step() 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def __init__(self,
               checkpoint_dir,
               save_secs=None,
               save_steps=None,
               saver=None,
               checkpoint_basename="model.ckpt",
               scaffold=None,
               listeners=None):
    """Initializes a `CheckpointSaverHook`.

    Args:
      checkpoint_dir: `str`, base directory for the checkpoint files.
      save_secs: `int`, save every N secs.
      save_steps: `int`, save every N steps.
      saver: `Saver` object, used for saving.
      checkpoint_basename: `str`, base name for the checkpoint files.
      scaffold: `Scaffold`, use to get saver object.
      listeners: List of `CheckpointSaverListener` subclass instances. Used for
        callbacks that run immediately before or after this hook saves the
        checkpoint.

    Raises:
      ValueError: One of `save_steps` or `save_secs` should be set.
      ValueError: At most one of `saver` or `scaffold` should be set.
    """
    logging.info("Create AsyncCheckpointSaverHook.")
    if saver is not None and scaffold is not None:
      raise ValueError("You cannot provide both saver and scaffold.")
    self._saver = saver
    self._save_thread = None
    self._write_graph_thread = None
    self._checkpoint_dir = checkpoint_dir
    self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)
    self._scaffold = scaffold
    self._timer = basic_session_run_hooks.SecondOrStepTimer(
        every_secs=save_secs, every_steps=save_steps)
    self._listeners = listeners or []
    self._steps_per_run = 1
    self._summary_writer = None
    self._global_step_tensor = None 

# 需要导入模块: from tensorflow.python.training.summary_io import SummaryWriterCache [as 别名]
# 或者: from tensorflow.python.training.summary_io.SummaryWriterCache import get [as 别名]
def begin(self):
    self._summary_writer = SummaryWriterCache.get(self._checkpoint_dir)
    self._global_step_tensor = training_util._get_or_create_global_step_read()  # pylint: disable=protected-access
    if self._global_step_tensor is None:
      raise RuntimeError(
          "Global step should be created to use CheckpointSaverHook.")
    for l in self._listeners:
      l.begin()