Method from_structure

tensorflow/python/data/ops/iterator_ops.py:119–220

Creates a new, uninitialized `Iterator` with the given structure. This iterator-constructing method can be used to create an iterator that is reusable with many different datasets. The returned iterator is not bound to a particular dataset, and it has no `initializer`. To initializ

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

  @staticmethod
  def from_structure(output_types,
                     output_shapes=None,
                     shared_name=None,
                     output_classes=None):
    """Creates a new, uninitialized `Iterator` with the given structure.

    This iterator-constructing method can be used to create an iterator that
    is reusable with many different datasets.

    The returned iterator is not bound to a particular dataset, and it has
    no `initializer`. To initialize the iterator, run the operation returned by
    `Iterator.make_initializer(dataset)`.

    The following is an example

    ```python
    iterator = Iterator.from_structure(tf.int64, tf.TensorShape([]))

    dataset_range = Dataset.range(10)
    range_initializer = iterator.make_initializer(dataset_range)

    dataset_evens = dataset_range.filter(lambda x: x % 2 == 0)
    evens_initializer = iterator.make_initializer(dataset_evens)

    # Define a model based on the iterator; in this example, the model_fn
    # is expected to take scalar tf.int64 Tensors as input (see
    # the definition of 'iterator' above).
    prediction, loss = model_fn(iterator.get_next())

    # Train for `num_epochs`, where for each epoch, we first iterate over
    # dataset_range, and then iterate over dataset_evens.
    for _ in range(num_epochs):
      # Initialize the iterator to `dataset_range`
      sess.run(range_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break

      # Initialize the iterator to `dataset_evens`
      sess.run(evens_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break
    ```

    Args:
      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.
      shared_name: (Optional.) If non-empty, this iterator will be shared under
        the given name across multiple sessions that share the same devices
        (e.g. when using a remote server).