How to use tf.data's initializable iterators w

问题:

I would like to manage my training with a tf.estimator.Estimator but have some trouble to use it alongside the tf.data API.

I have something like this:

def model_fn(features, labels, params, mode):
  # Defines model's ops.
  # Initializes with tf.train.Scaffold.
  # Returns an tf.estimator.EstimatorSpec.

def input_fn():
  dataset = tf.data.TextLineDataset("test.txt")
  # map, shuffle, padded_batch, etc.

  iterator = dataset.make_initializable_iterator()

  return iterator.get_next()

estimator = tf.estimator.Estimator(model_fn)
estimator.train(input_fn)

As I can't use a make_one_shot_iterator for my use case, my issue is that input_fn contains an iterator that should be initialized within model_fn (here, I use tf.train.Scaffold to initialize local ops).

Also, I understood that we can't only use input_fn = iterator.get_next otherwise the other ops will not be added to the same graph.

What is the recommended way to initialize the iterator?

回答1:

As of TensorFlow 1.5, it is possible to make input_fn return a tf.data.Dataset, e.g.:

def input_fn():
  dataset = tf.data.TextLineDataset("test.txt")
  # map, shuffle, padded_batch, etc.
  return dataset

See c294fcfd.

---

For previous versions, you can add the iterator's initializer in the tf.GraphKeys.TABLE_INITIALIZERS collections and rely on the default initializer.

tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)