How to replace make_one_shot_iterator () from google machine learning crash course

Question

I am following the Google Machine Learning Intensive Course. But it uses version 1.x of TensorFlow, so I was planning to change the exercises to be able to run them in TensorFlow 2.0. But I am stuck in that exercise:

https://colab.research.google.com/notebooks/mlcc/first_steps_with_tensor_flow.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=firststeps-colab&hl=es#scrollTo=7UwqGbbxP53O

Specifically the code:

    def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    """Trains a linear regression model of one feature.

    Args:
      features: pandas DataFrame of features
      targets: pandas DataFrame of targets
      batch_size: Size of batches to be passed to the model
      shuffle: True or False. Whether to shuffle the data.
      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely
    Returns:
      Tuple of (features, labels) for next data batch
    """

    # Convert pandas data into a dict of np arrays.
    features = {key:np.array(value) for key,value in dict(features).items()}                                           

    # Construct a dataset, and configure batching/repeating.
    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
    ds = ds.batch(batch_size).repeat(num_epochs)

    # Shuffle the data, if specified.
    if shuffle:
      ds = ds.shuffle(buffer_size=10000)

    # Return the next batch of data.
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

I have replaced features, labels = ds.make_one_shot_iterator().get_next() with features, labels = tf.compat.v1.data.make_one_shot_iterator(ds).get_next()

and it seems to work but make_one_shot_iterator() is depreceated, so, how can i replace it?

Also according to https://github.com/tensorflow/tensorflow/issues/29252 , I have tried

 features, labels = ds.__iter__()
    next(ds.__iter__())
    return features, labels

but it returns the error __iter __ () is only supported inside of tf.function or when eager execution is enabled.

I am quite inexperienced in python and follow the course as a hobbyist. Any ideas on how to solve it? Thank you.

Answer 1

After several tests, the python hang was a local problem. To replace features, labels = ds.make_one_shot_iterator (). Get_next () I have tried several things:

features, labels = ds.__iter__().get_next()

iterator = ds.__iter__()
features, labels = iterator.get_next()

it = iter(ds)
features, labels = next(it)

All three cases return __iter__() is only supported inside of tf.function or when eager execution is enabled. so I tried:

features, labels = ds
return ds

And also just:

return features, labels

And both returns the same error, finally I tried:

return ds

And mysteriously it worked, I have no idea why, but it did.

Answer 2

1). I doubt, that you've really got what you wanted. Because if your input really needed to be multi-input - then your ds unlikely suits, you just need the list... something like this:

features = tf.compat.v1.data.make_one_shot_iterator(train_dataset).get_next()
image, label = features['image'], features['label']

2). Concerning Iterator - now it is belonging to 'tf.data' - with 'tf.data.Iterator.get_next()' method as opposed to previous tf.data.Datasetds.make_one_shot_iterator() -- 'Dependency Invertion' (D from SOLID principles of dev.) perhaps was done, perhaps to refactor.... New Iterator-entity now could be used for tf.data.Dataset.from_generator() objects feeding from fn_generator in async-mode each chunk of data yielded -- here is example of Custom-tfds.core.GeneratorBasedBuilder overwritting...

I think, the overall architecture of tf-lib was refactored a little-bit, because the input started to eat batch-by-batch itself (due to dev.'s implementations) -- & make_one_shot_iterator applied for Dataset no more needed... Even for debugging there is .as_numpy_iterator(), & make_one_shot_iterator no more considered to be needed by developers

though sometimes people use:

iterator = iter(batched_dataset)
next_element = iterator.get_next()

• cannot assume where this could be needed yet

P.S. BTW, as I remember smth from Debugger, if your container is hashable or not iterable (or correct me) - you can try:

  iterator = iter(dataset)
  # batch_features, batch_labels = iterator.get_next()
  el = iterator.get_next()
  batch_features= el[:]
  print(batch_features)
  batch_labels= el[:-1]
  print(batch_labels)

• works OK