Method from_string_handle

tensorflow/python/data/ops/iterator_ops.py:223–298

Creates a new, uninitialized `Iterator` based on the given handle. This method allows you to define a "feedable" iterator where you can choose between concrete iterators by feeding a value in a `tf.Session.run` call. In that case, `string_handle` would be a `tf.compat.v1.placeholder`, a

(string_handle,
                         output_types,
                         output_shapes=None,
                         output_classes=None)

  @staticmethod
  def from_string_handle(string_handle,
                         output_types,
                         output_shapes=None,
                         output_classes=None):
    """Creates a new, uninitialized `Iterator` based on the given handle.

    This method allows you to define a "feedable" iterator where you can choose
    between concrete iterators by feeding a value in a `tf.Session.run` call.
    In that case, `string_handle` would be a `tf.compat.v1.placeholder`, and you
    would
    feed it with the value of `tf.data.Iterator.string_handle` in each step.

    For example, if you had two iterators that marked the current position in
    a training dataset and a test dataset, you could choose which to use in
    each step as follows:

    ```python
    train_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    train_iterator_handle = sess.run(train_iterator.string_handle())

    test_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    test_iterator_handle = sess.run(test_iterator.string_handle())

    handle = tf.compat.v1.placeholder(tf.string, shape=[])
    iterator = tf.data.Iterator.from_string_handle(
        handle, train_iterator.output_types)

    next_element = iterator.get_next()
    loss = f(next_element)

    train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
    test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
    ```

    Args:
      string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates to
        a handle produced by the `Iterator.string_handle()` method.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.
      output_classes: (Optional.) A nested structure of Python `type` objects
        corresponding to each component of an element of this iterator. If
        omitted, each component is assumed to be of type `tf.Tensor`.

    Returns:
      An `Iterator`.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(output_types,
                                               tensor_shape.as_shape,
                                               output_shapes)
    if output_classes is None: