Method make_callable

tensorflow/python/client/session.py:1194–1318

Returns a Python callable that runs a particular step. The returned callable will take `len(feed_list)` arguments whose types must be compatible feed values for the respective elements of `feed_list`. For example, if element `i` of `feed_list` is a `tf.Tensor`, the `i`th argument to

(self, fetches, feed_list=None, accept_options=False)

    return fetch_handler.build_results(self, results)

  def make_callable(self, fetches, feed_list=None, accept_options=False):
    """Returns a Python callable that runs a particular step.

    The returned callable will take `len(feed_list)` arguments whose types
    must be compatible feed values for the respective elements of `feed_list`.
    For example, if element `i` of `feed_list` is a `tf.Tensor`, the `i`th
    argument to the returned callable must be a numpy ndarray (or something
    convertible to an ndarray) with matching element type and shape. See
    `tf.Session.run` for details of the allowable feed key and value types.

    The returned callable will have the same return type as
    `tf.Session.run(fetches, ...)`. For example, if `fetches` is a `tf.Tensor`,
    the callable will return a numpy ndarray; if `fetches` is a `tf.Operation`,
    it will return `None`.

    Args:
      fetches: A value or list of values to fetch. See `tf.Session.run` for
        details of the allowable fetch types.
      feed_list: (Optional.) A list of `feed_dict` keys. See `tf.Session.run`
        for details of the allowable feed key types.
      accept_options: (Optional.) If `True`, the returned `Callable` will be
        able to accept `tf.compat.v1.RunOptions` and `tf.compat.v1.RunMetadata`
        as optional keyword arguments `options` and `run_metadata`,
        respectively, with the same syntax and semantics as `tf.Session.run`,
        which is useful for certain use cases (profiling and debugging) but will
        result in measurable slowdown of the `Callable`'s
        performance. Default: `False`.

    Returns:
      A function that when called will execute the step defined by
      `feed_list` and `fetches` in this session.

    Raises:
      TypeError: If `fetches` or `feed_list` cannot be interpreted
        as arguments to `tf.Session.run`.
    """
    if feed_list is not None:
      if not isinstance(feed_list, (list, tuple)):
        raise TypeError('`feed_list` must be a list or tuple.')
      # Delegate any non-empty feed lists to the existing `run()` logic.
      # TODO(mrry): Refactor the feed handling logic from
      # `Session._run()` so that we can convert the feeds to a list of
      # strings here.
      def _generic_run(*feed_args, **kwargs):
        feed_dict = {
            feed: feed_val for feed, feed_val in zip(feed_list, feed_args)
        }
        return self.run(fetches, feed_dict=feed_dict, **kwargs)

      return _generic_run

    # Ensure any changes to the graph are reflected in the runtime.
    # Note that we don't need to do this on subsequent calls to the
    # returned object, because the arguments to `fetches` must already be
    # in the graph.
    self._extend_graph()

    # Create a fetch handler to take care of the structure of fetches.