Python tensorflow.python.training.training_util.get_global_step() Examples

def assert_or_get_global_step(graph=None, global_step_tensor=None):
  """Verifies that a global step tensor is valid or gets one if None is given.

  If `global_step_tensor` is not None, check that it is a valid global step
  tensor (using `assert_global_step`). Otherwise find a global step tensor using
  `get_global_step` and return it.

  Args:
    graph: The graph to find the global step tensor for.
    global_step_tensor: The tensor to check for suitability as a global step. If
      None is given (the default), find a global step tensor.

  Returns:
    A tensor suitable as a global step, or `None` if none was provided and none
    was found.
  """
  if global_step_tensor is None:
    # Get the global step tensor the same way the supervisor would.
    global_step_tensor = get_global_step(graph)
  else:
    assert_global_step(global_step_tensor)
  return global_step_tensor 

def assert_or_get_global_step(graph=None, global_step_tensor=None):
  """Verifies that a global step tensor is valid or gets one if None is given.

  If `global_step_tensor` is not None, check that it is a valid global step
  tensor (using `assert_global_step`). Otherwise find a global step tensor using
  `get_global_step` and return it.

  Args:
    graph: The graph to find the global step tensor for.
    global_step_tensor: The tensor to check for suitability as a global step.
      If None is given (the default), find a global step tensor.

  Returns:
    A tensor suitable as a global step, or `None` if none was provided and none
    was found.
  """
  if global_step_tensor is None:
    # Get the global step tensor the same way the supervisor would.
    global_step_tensor = get_global_step(graph)
  else:
    assert_global_step(global_step_tensor)
  return global_step_tensor 

def assert_or_get_global_step(graph=None, global_step_tensor=None):
  """Verifies that a global step tensor is valid or gets one if None is given.

  If `global_step_tensor` is not None, check that it is a valid global step
  tensor (using `assert_global_step`). Otherwise find a global step tensor using
  `get_global_step` and return it.

  Args:
    graph: The graph to find the global step tensor for.
    global_step_tensor: The tensor to check for suitability as a global step. If
      None is given (the default), find a global step tensor.

  Returns:
    A tensor suitable as a global step, or `None` if none was provided and none
    was found.
  """
  if global_step_tensor is None:
    # Get the global step tensor the same way the supervisor would.
    global_step_tensor = get_global_step(graph)
  else:
    assert_global_step(global_step_tensor)
  return global_step_tensor 

def begin(self):
    self._global_step_tensor = training_util.get_global_step()
    self._stop_var = self._get_or_create_stop_var_with_aggregation()
    assert distribution_strategy_context.in_cross_replica_context()

    strategy = distribution_strategy_context.get_strategy()
    self._stop_placeholder = None

    def stop_op_fn(var):
      placeholder = array_ops.placeholder_with_default(
          0, tuple(), name='stop_value')
      if self._stop_placeholder is None:
        self._stop_placeholder = placeholder
      return var.assign_add(placeholder)

    self._stop_op = strategy.run(
        stop_op_fn, args=(self._stop_var,)) 

def assert_or_get_global_step(graph=None, global_step_tensor=None):
  """Verifies that a global step tensor is valid or gets one if None is given.

  If `global_step_tensor` is not None, check that it is a valid global step
  tensor (using `assert_global_step`). Otherwise find a global step tensor using
  `get_global_step` and return it.

  Args:
    graph: The graph to find the global step tensor for.
    global_step_tensor: The tensor to check for suitability as a global step.
      If None is given (the default), find a global step tensor.

  Returns:
    A tensor suitable as a global step, or `None` if none was provided and none
    was found.
  """
  if global_step_tensor is None:
    # Get the global step tensor the same way the supervisor would.
    global_step_tensor = get_global_step(graph)
  else:
    assert_global_step(global_step_tensor)
  return global_step_tensor 

def assert_or_get_global_step(graph=None, global_step_tensor=None):
  """Verifies that a global step tensor is valid or gets one if None is given.

  If `global_step_tensor` is not None, check that it is a valid global step
  tensor (using `assert_global_step`). Otherwise find a global step tensor using
  `get_global_step` and return it.

  Args:
    graph: The graph to find the global step tensor for.
    global_step_tensor: The tensor to check for suitability as a global step.
      If None is given (the default), find a global step tensor.

  Returns:
    A tensor suitable as a global step, or `None` if none was provided and none
    was found.
  """
  if global_step_tensor is None:
    # Get the global step tensor the same way the supervisor would.
    global_step_tensor = get_global_step(graph)
  else:
    assert_global_step(global_step_tensor)
  return global_step_tensor 

def create_global_step(graph=None):
  """Create global step tensor in graph.

  Args:
    graph: The graph in which to create the global step. If missing, use default
        graph.

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step key is already defined.
  """
  graph = ops.get_default_graph() if graph is None else graph
  if get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    collections = [ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]
    return variable(ops.GraphKeys.GLOBAL_STEP, shape=[], dtype=dtypes.int64,
                    initializer=init_ops.zeros_initializer, trainable=False,
                    collections=collections) 

def _create_global_step(graph):
  graph = graph or ops.get_default_graph()
  if training.get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    return variable_scope.get_variable(
        ops.GraphKeys.GLOBAL_STEP,
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        use_resource=True,
        collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]) 

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() 

def end(self, session):
    last_step = session.run(training_util.get_global_step())
    if last_step != self._timer.last_triggered_step():
      self._save(last_step, session)
    for l in self._listeners:
      l.end(session, last_step) 

def begin(self):
    if self._summary_writer is None and self._output_dir:
      self._summary_writer = SummaryWriterCache.get(self._output_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 StepCounterHook.")
    self._summary_tag = self._global_step_tensor.op.name + "/sec" 

def begin(self):
    if self._summary_writer is None and self._output_dir:
      self._summary_writer = SummaryWriterCache.get(self._output_dir)
    self._next_step = None
    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 SummarySaverHook.") 

def get_global_step(graph=None):
  return training_util.get_global_step(graph) 

def create_global_step(graph=None):
  """Create global step tensor in graph.

  Args:
    graph: The graph in which to create the global step. If missing, use default
        graph.

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step key is already defined.
  """
  graph = ops.get_default_graph() if graph is None else graph
  if get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    collections = [ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]
    return variable(
        ops.GraphKeys.GLOBAL_STEP,
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        collections=collections) 

def _create_global_step(graph):
  graph = graph or ops.get_default_graph()
  if training.get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    return variable_scope.get_variable(
        ops.GraphKeys.GLOBAL_STEP,
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        use_resource=True,
        collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]) 

def get_or_create_global_step(graph=None):
  """Returns and create (if necessary) the global step variable.

  Args:
    graph: The graph in which to create the global step. If missing, use default
        graph.

  Returns:
    the tensor representing the global step variable.
  """
  graph = ops.get_default_graph() if graph is None else graph
  globalstep = get_global_step(graph)
  if globalstep is None:
    globalstep = create_global_step(graph)
  return globalstep 

def begin(self):
    self._global_step_tensor = training_util.get_global_step()
    if self._global_step_tensor is None:
      raise RuntimeError('Global step should be created.')

    self._iterations_per_loop_var = _create_or_get_iterations_per_loop() 

def begin(self):
    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 StopAtStepHook.") 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
        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 StepCounterHook.") 

def __init__(self,
                 checkpoint_dir,
                 display_steps=100,
                 maximum_train_steps=None,
                 do_summary=True,
                 is_chief=True):
        """ Initializes the hook.

        Args:
            checkpoint_dir: A string, base directory for the checkpoint files.
            display_steps: A python integer, display every N steps.
            maximum_train_steps: A python integer, the maximum training steps.
            do_summary: Whether to save summaries when display.
            is_chief: Whether this is the chief process.do_summary:
        """

        tf.logging.info("Create DisplayHook.")
        self._checkpoint_dir = checkpoint_dir
        # display steps
        self._display_steps = display_steps
        self._maximum_train_steps = maximum_train_steps
        self._do_summary = do_summary
        self._is_chief = is_chief  # not used now

        # display values
        global_step = training_util.get_global_step()
        display_keys = ops.get_collection(Constants.DISPLAY_KEY_COLLECTION_NAME)
        display_values = ops.get_collection(Constants.DISPLAY_VALUE_COLLECTION_NAME)
        self._display_args = dict(zip(display_keys, display_values))
        self._display_args["global_step"] = global_step
        # timer & summary writer
        self._timer = None
        self._logging_timer = None
        self._summary_writer = None 

def __init__(self,
                 checkpoint_dir,
                 save_checkpoint_steps=1000,
                 saver=None,
                 pretrain_model=None,
                 problem_name=None,
                 model_name="njunmt.models.SequenceToSequence",
                 do_summary=True,
                 is_chief=True):
        """ Initializes the hook.

        Args:
            checkpoint_dir: A string, base directory for the checkpoint files.
            save_checkpoint_steps: A python integer, save every N steps.
            saver: `Saver` object, used for saving.
            pretrain_model: The pretrained model dir.
            problem_name: A string.
            model_name: The model name.
            do_summary: Whether to save summaries.
            is_chief: Whether this is the chief process.
        """
        tf.logging.info("Create CheckpointSaverHook.")
        if saver is None:
            saver = get_saver_or_default(max_to_keep=8)  # pylint: disable=protected-access
        self._saver = saver
        self._checkpoint_dir = checkpoint_dir
        self._save_path = os.path.join(checkpoint_dir, Constants.MODEL_CKPT_FILENAME)
        self._pretrain_model = pretrain_model
        self._problem_name = problem_name
        self._model_name = model_name
        # save every n steps
        self._save_checkpoint_steps = save_checkpoint_steps
        # variable for session.run
        self._global_step = training_util.get_global_step()
        # for after create session
        self._do_summary = do_summary
        self._is_chief = is_chief
        # timer & summary writer
        self._timer = None
        self._summary_writer = None 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
    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 StepCounterHook.') 

def begin(self):
    self._global_step_tensor = training_util.get_global_step()
    if self._global_step_tensor is None:
      raise RuntimeError('Global step should be created.')

    self._iterations_per_loop_var = _create_or_get_iterations_per_loop() 

def _create_global_step(graph):
  graph = graph or ops.get_default_graph()
  if training.get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    return variable_scope.get_variable(
        ops.GraphKeys.GLOBAL_STEP,
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        use_resource=True,
        collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]) 

def begin(self):
    self._global_step_tensor = training_util.get_global_step()
    if self._global_step_tensor is None:
      raise RuntimeError('Global step should be created.')

    self._iterations_per_loop_var = _create_or_get_iterations_per_loop()