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

def _initialize_in_memory_eval(estimator):
  """Setup TPUEstimator for in-memory evaluation."""

  # estimator.evaluate calls _create_global_step unconditionally, override this.
  def _global_step(_):
    with variable_scope.variable_scope('', use_resource=True):
      return training_util.get_or_create_global_step()

  estimator._create_global_step = _global_step  #  pylint: disable=protected-access
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.EVAL] = error_handling.ErrorRendezvous(3)
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.PREDICT] = error_handling.ErrorRendezvous(3)


# pylint: disable=protected-access 

def _initialize_in_memory_eval(estimator):
  """Setup TPUEstimator for in-memory evaluation."""

  # estimator.evaluate calls _create_global_step unconditionally, override this.
  def _global_step(_):
    with variable_scope.variable_scope('', use_resource=True):
      return training_util.get_or_create_global_step()

  estimator._create_global_step = _global_step  #  pylint: disable=protected-access
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.EVAL] = error_handling.ErrorRendezvous(3)
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.PREDICT] = error_handling.ErrorRendezvous(3)


# pylint: disable=protected-access 

def _initialize_in_memory_eval(estimator):
  """Setup TPUEstimator for in-memory evaluation."""

  # estimator.evaluate calls _create_global_step unconditionally, override this.
  def _global_step(_):
    with variable_scope.variable_scope('', use_resource=True):
      return training_util.get_or_create_global_step()

  estimator._create_global_step = _global_step  #  pylint: disable=protected-access
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.EVAL] = error_handling.ErrorRendezvous(3)
  estimator._rendezvous[  # pylint: disable=protected-access
      model_fn_lib.ModeKeys.PREDICT] = error_handling.ErrorRendezvous(3)


# pylint: disable=protected-access 

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

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

  Returns:
    The global step tensor.
  """
  return training_util.get_or_create_global_step(graph) 

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

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

  Returns:
    The global step tensor.
  """
  return training_util.get_or_create_global_step(graph) 

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():
      with variable_scope.variable_scope('', use_resource=True):
        training_util.get_or_create_global_step()
      features, input_hooks = self._estimator._get_features_from_input_fn(  # pylint: disable=protected-access
          self._input_fn, model_fn_lib.ModeKeys.PREDICT)
      estimator_spec = self._estimator._call_model_fn(  # pylint: disable=protected-access
          features, None, model_fn_lib.ModeKeys.PREDICT, self._estimator.config)

      self._all_hooks = list(input_hooks) + list(estimator_spec.prediction_hooks)
      self._predictions = self._estimator._extract_keys(  # pylint: disable=protected-access
          estimator_spec.predictions,
          predict_keys=None)
      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)
      }
      logging.info('Placeholders: %s', self._var_name_to_placeholder)

      for h in self._all_hooks:
        logging.info('Hook: %s', h)
        if isinstance(h, tpu_estimator.TPUInfeedOutfeedSessionHook):
          h._should_initialize_tpu = False  # pylint: disable=protected-access 

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():
      with variable_scope.variable_scope('', use_resource=True):
        training_util.get_or_create_global_step()
      features, input_hooks = self._estimator._get_features_from_input_fn(  # pylint: disable=protected-access
          self._input_fn, model_fn_lib.ModeKeys.PREDICT)
      estimator_spec = self._estimator._call_model_fn(  # pylint: disable=protected-access
          features, None, model_fn_lib.ModeKeys.PREDICT, self._estimator.config)

      self._all_hooks = list(input_hooks) + list(estimator_spec.prediction_hooks)
      self._predictions = self._estimator._extract_keys(  # pylint: disable=protected-access
          estimator_spec.predictions,
          predict_keys=None)
      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)
      }
      logging.info('Placeholders: %s', self._var_name_to_placeholder)

      for h in self._all_hooks:
        logging.info('Hook: %s', h)
        if isinstance(h, tpu_estimator.TPUInfeedOutfeedSessionHook):
          h._should_initialize_tpu = False  # pylint: disable=protected-access 

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

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

  Returns:
    The global step tensor.
  """
  return training_util.get_or_create_global_step(graph) 

def begin(self):
    if self._replace_summary_op:
      # This can still remain None if there are no summaries.
      self._summary_op = summary.merge_all()
    self._global_step = training_util.get_or_create_global_step() 

def _init_global_step(self):
        self.global_step = training_util.get_or_create_global_step()
        self._training_ops.update({
            'increment_global_step': training_util._increment_global_step(1)
        }) 

def _init_global_step(self):
        self.global_step = training_util.get_or_create_global_step()
        self._training_ops.update({
            'increment_global_step': training_util._increment_global_step(1)
        }) 

def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    """Wraps the original apply_gradient of the optimizer.

    Args:
      grads_and_vars: List of (gradient, variable) pairs as returned by
        `compute_gradients()`.
      global_step: Optional `Variable` to increment by one after the
        variables have been updated.
      name: Optional name for the returned operation.  Default to the
        name passed to the `Optimizer` constructor.
    Returns:
      An `Operation` that applies the specified gradients. If `global_step`
      was not None, that operation also increments `global_step`.
    """
    pre_op = self._before_apply_gradients(grads_and_vars)
    with ops.control_dependencies([pre_op]):
      optimizer_update = self._optimizer.apply_gradients(
          grads_and_vars, global_step=global_step, name=name)
    # We get the default one after calling the super.apply_gradient(), since
    # we want to preserve original behavior of the optimizer: don't increment
    # anything if no global_step is passed. But we need the global step for
    # the mask_update.
    global_step = (global_step if global_step is not None
                   else training_util.get_or_create_global_step())
    self._global_step = global_step
    with ops.control_dependencies([optimizer_update]):
      return self.cond_mask_update_op(global_step, control_flow_ops.no_op) 

def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    """Wraps the original apply_gradient of the optimizer.

    Args:
      grads_and_vars: List of (gradient, variable) pairs as returned by
        `compute_gradients()`.
      global_step: Optional `Variable` to increment by one after the
        variables have been updated.
      name: Optional name for the returned operation.  Default to the
        name passed to the `Optimizer` constructor.
    Returns:
      An `Operation` that applies the specified gradients. If `global_step`
      was not None, that operation also increments `global_step`.
    """
    pre_op = self._before_apply_gradients(grads_and_vars)
    with ops.control_dependencies([pre_op]):
      # Call this to create slots.
      _ = self._optimizer.apply_gradients(
          grads_and_vars, global_step=global_step, name=name)
      def apply_gradient_op():
        optimizer_update = self._optimizer.apply_gradients(
            grads_and_vars, global_step=global_step, name=name)
        return optimizer_update
      # We get the default one after calling the super.apply_gradient(), since
      # we want to preserve original behavior of the optimizer: don't increment
      # anything if no global_step is passed. But we need the global step for
      # the mask_update.
      global_step = (global_step if global_step is not None
                     else training_util.get_or_create_global_step())
      self._global_step = global_step
      return self.cond_mask_update_op(global_step, apply_gradient_op) 

def _clone_and_build_model(mode,
                           keras_model,
                           custom_objects,
                           features=None,
                           labels=None):
  """Clone and build the given keras_model.

  Args:
    mode: training mode.
    keras_model: an instance of compiled keras model.
    custom_objects: Dictionary for custom objects.
    features:
    labels:

  Returns:
    The newly built model.
  """
  # Set to True during training, False for inference.
  K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN)

  # Clone keras model.
  input_tensors = None if features is None else _create_ordered_io(
      keras_model, features)
  if custom_objects:
    with CustomObjectScope(custom_objects):
      model = models.clone_model(keras_model, input_tensors=input_tensors)
  else:
    model = models.clone_model(keras_model, input_tensors=input_tensors)

  # Compile/Build model
  if mode is model_fn_lib.ModeKeys.PREDICT and not model.built:
    model.build()
  else:
    optimizer_config = keras_model.optimizer.get_config()
    optimizer = keras_model.optimizer.__class__.from_config(optimizer_config)
    optimizer.iterations = training_util.get_or_create_global_step()

    # Get list of outputs.
    if labels is None:
      target_tensors = None
    elif isinstance(labels, dict):
      target_tensors = _create_ordered_io(keras_model, labels, is_input=False)
    else:
      target_tensors = [
          sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)
      ]

    model.compile(
        optimizer,
        keras_model.loss,
        metrics=keras_model.metrics,
        loss_weights=keras_model.loss_weights,
        sample_weight_mode=keras_model.sample_weight_mode,
        weighted_metrics=keras_model.weighted_metrics,
        target_tensors=target_tensors)

  if isinstance(model, models.Sequential):
    model = model.model
  return model