Python training_util._get_or_create_global_step_read方法代码示例

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [as 别名]
def begin(self):
        """Called once before graph finalization.

        Is called once before the default graph in the active tensorflow session is
        finalized and the training has starts.
        The hook can modify the graph by adding new operations to it.
        After the begin() call the graph will be finalized and the other callbacks can not modify
        the graph anymore. Second call of begin() on the same graph, should not change the graph.
        """
        # Create a summary writer if possible.
        if self._summary_writer is None and self._output_dir:
            self._summary_writer = summary_io.SummaryWriterCache.get(self._output_dir)

        # Get read access to the global step tensor.
        # pylint: disable=protected-access
        self._global_step_tensor = training_util._get_or_create_global_step_read()
        if self._global_step_tensor is None:
            raise RuntimeError("Global step should be created to use StepCounterHook.") 

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [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() 

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [as 别名]
def begin(self):
    """Build eval graph and restoring op."""
    self._timer.reset()
    self._graph = ops.Graph()
    self._global_step_tensor = training_util._get_or_create_global_step_read()  # pylint: disable=protected-access
    with self._graph.as_default():
      (self._scaffold, self._update_op, self._eval_dict,
       self._all_hooks) = self._estimator._evaluate_build_graph(
           self._input_fn, self._hooks, checkpoint_path=None)

      for h in self._all_hooks:
        if isinstance(h, tpu_estimator.TPUInfeedOutfeedSessionHook):
          h._should_initialize_tpu = False  # pylint: disable=protected-access

      if self._scaffold.saver is not None:
        raise ValueError('InMemoryEval does not support custom saver')
      if self._scaffold.init_fn is not None:
        raise ValueError('InMemoryEval does not support custom init_fn')

      self._var_name_to_eval_var = {
          v.name: v for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
      }
      self._var_name_to_placeholder = {
          v.name: array_ops.placeholder(v.dtype)
          for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
      } 

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [as 别名]
def _train_model_default(self, input_fn, hooks, saving_listeners):
    """Initiate training with `input_fn`, without `DistributionStrategies`.

    Args:
      input_fn: A function that provides input data for training as minibatches.
      hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
        callbacks inside the training loop.
      saving_listeners: list of `tf.train.CheckpointSaverListener` objects. Used
        for callbacks that run immediately before or after checkpoint savings.

    Returns:
      Loss from training
    """
    worker_hooks = []
    with tf.Graph().as_default() as g, g.device(self._device_fn):
      tf.compat.v1.random.set_random_seed(self._config.tf_random_seed)
      global_step_tensor = self._create_and_assert_global_step(g)

      # Skip creating a read variable if _create_and_assert_global_step
      # returns None (e.g. tf.contrib.estimator.SavedModelEstimator).
      if global_step_tensor is not None:
        training_util._get_or_create_global_step_read(g)  # pylint: disable=protected-access

      features, labels, input_hooks = (
          self._get_features_and_labels_from_input_fn(input_fn, ModeKeys.TRAIN))
      worker_hooks.extend(input_hooks)
      estimator_spec = self._call_model_fn(features, labels, ModeKeys.TRAIN,
                                           self.config)
      global_step_tensor = tf.compat.v1.train.get_global_step(g)
      return self._train_with_estimator_spec(estimator_spec, worker_hooks,
                                             hooks, global_step_tensor,
                                             saving_listeners) 

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [as 别名]
def begin(self):
    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 StopAtCheckpointStepHook.') 

# 需要导入模块: from tensorflow.python.training import training_util [as 别名]
# 或者: from tensorflow.python.training.training_util import _get_or_create_global_step_read [as 别名]
def begin(self):
    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 StopAtStepHook.")