How Can I Use Tf.data Datasets In Eager Execution Mode?

In the tf.data talk at the TensorFlow Dev Summit 2018, Derek Murray presented a way to combine the tf.data API with TensorFlow's eager execution mode (at 10:54). I tried out a simp

Solution 1:

make_one_shot_iterator() should work in TensorFlow 1.8, but for now (i.e., for TensorFlow 1.7), do the following:

import tensorflow.contrib.eager as tfe

dataset = tf.data.Dataset.from_tensor_slices(tf.random_uniform([50, 10]))
dataset = dataset.batch(5)
for batch in tfe.Iterator(dataset):
     print(batch)

Solution 2:

With TF 2.1,

You can create an iterator like so:

iterator = iter(dataset)

And get the next batch of values:

batch = iterator.get_next()