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tf.data.experimental.make_saveable_from_iterator

TensorFlow 1 version View source on GitHub

Returns a SaveableObject for saving/restoring iterator state using Saver.

tf.data.experimental.make_saveable_from_iterator(
    iterator, external_state_policy='fail'
)

Args:

  • iterator: Iterator.
  • external_state_policy: A string that identifies how to handle input pipelines that depend on external state. Possible values are 'ignore': The external state is silently ignored. 'warn': The external state is ignored, logging a warning. 'fail': The operation fails upon encountering external state. By default we set it to 'fail'.

Returns:

A SaveableObject for saving/restoring iterator state using Saver.

Raises:

  • ValueError: If iterator does not support checkpointing.
  • ValueError: If external_state_policy is not one of 'warn', 'ignore' or 'fail'.

For example:

with tf.Graph().as_default():
  ds = tf.data.Dataset.range(10)
  iterator = ds.make_initializable_iterator()
  # Build the iterator SaveableObject.
  saveable_obj = tf.data.experimental.make_saveable_from_iterator(iterator)
  # Add the SaveableObject to the SAVEABLE_OBJECTS collection so
  # it can be automatically saved using Saver.
  tf.compat.v1.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable_obj)
  saver = tf.compat.v1.train.Saver()

  while continue_training:
    ... Perform training ...
    if should_save_checkpoint:
      saver.save()