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

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Example #1

Source File: variables.py From tensornets with MIT License

6 votes

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

  This API is deprecated. Use core framework training version instead.

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

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step tensor is already defined.
  """
  return training_util.create_global_step(graph)

Example #2

Source File: variables.py From lambda-packs with MIT License

6 votes

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

  This API is deprecated. Use core framework training version instead.

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

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step tensor is already defined.
  """
  return training_util.create_global_step(graph)

Example #3

Source File: variables.py From tf-slim with Apache License 2.0

6 votes

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

  This API is deprecated. Use core framework training version instead.

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

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step tensor is already defined.
  """
  return training_util.create_global_step(graph)

Example #4

Source File: estimator.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License

5 votes

def _save_first_checkpoint(keras_model, estimator, custom_objects,
                           keras_weights):
  """Save first checkpoint for the keras Estimator.

  Args:
    keras_model: an instance of compiled keras model.
    estimator: keras estimator.
    custom_objects: Dictionary for custom objects.
    keras_weights: A flat list of Numpy arrays for weights of given keras_model.

  Returns:
    The model_fn for a keras Estimator.
  """
  with ops.Graph().as_default() as g, g.device(estimator._device_fn):
    random_seed.set_random_seed(estimator.config.tf_random_seed)
    training_util.create_global_step()
    model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
                                   custom_objects)

    if isinstance(model, models.Sequential):
      model = model.model
    # Load weights and save to checkpoint if there is no checkpoint
    latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
    if not latest_path:
      with session.Session() as sess:
        model.set_weights(keras_weights)
        # Make update ops and initialize all variables.
        if not model.train_function:
          # pylint: disable=protected-access
          model._make_train_function()
          K._initialize_variables(sess)
          # pylint: enable=protected-access
        saver = saver_lib.Saver()
        saver.save(sess, estimator.model_dir + '/')