Method __init__

tensorflow/python/ops/data_flow_ops.py:920–998

Creates a barrier that persists across different graph executions. A barrier represents a key-value map, where each key is a string, and each value is a tuple of tensors. At runtime, the barrier contains 'complete' and 'incomplete' elements. A complete element has defined tensors f

(self, types, shapes=None, shared_name=None, name="barrier")

  """Represents a key-value map that persists across graph executions."""

  def __init__(self, types, shapes=None, shared_name=None, name="barrier"):
    """Creates a barrier that persists across different graph executions.

    A barrier represents a key-value map, where each key is a string, and
    each value is a tuple of tensors.

    At runtime, the barrier contains 'complete' and 'incomplete'
    elements. A complete element has defined tensors for all
    components of its value tuple, and may be accessed using
    take_many. An incomplete element has some undefined components in
    its value tuple, and may be updated using insert_many.

    The barrier call `take_many` outputs values in a particular order.
    First, it only outputs completed values.  Second, the order in which
    completed values are returned matches the order in which their very
    first component was inserted into the barrier.  So, for example, for this
    sequence of insertions and removals:

      barrier = Barrier((tf.string, tf.int32), shapes=((), ()))
      barrier.insert_many(0, keys=["k1", "k2"], values=["a", "b"]).run()
      barrier.insert_many(1, keys=["k1"], values=[1]).run()
      barrier.insert_many(0, keys=["k3"], values=["c"]).run()
      barrier.insert_many(1, keys=["k3"], values=[3]).run()
      barrier.insert_many(1, keys=["k2"], values=[2]).run()

      (indices, keys, values) = barrier.take_many(2)
      (indices_val, keys_val, values0_val, values1_val) =
         session.run([indices, keys, values[0], values[1]])

    The output will be (up to permutation of "k1" and "k2"):

      indices_val == (-2**63, -2**63)
      keys_val == ("k1", "k2")
      values0_val == ("a", "b")
      values1_val == (1, 2)

    Note the key "k2" was inserted into the barrier before "k3".  Even though
    "k3" was completed first, both are complete by the time
    take_many is called.  As a result, "k2" is prioritized and "k1" and "k2"
    are returned first.  "k3" remains in the barrier until the next execution
    of `take_many`.  Since "k1" and "k2" had their first insertions into
    the barrier together, their indices are the same (-2**63).  The index
    of "k3" will be -2**63 + 1, because it was the next new inserted key.

    Args:
      types: A single dtype or a tuple of dtypes, corresponding to the
        dtypes of the tensor elements that comprise a value in this barrier.
      shapes: Optional. Constraints on the shapes of tensors in the values:
        a single tensor shape tuple; a tuple of tensor shape tuples
        for each barrier-element tuple component; or None if the shape should
        not be constrained.
      shared_name: Optional. If non-empty, this barrier will be shared under
        the given name across multiple sessions.
      name: Optional name for the barrier op.

    Raises:
      ValueError: If one of the `shapes` indicate no elements.
    """