How to input a list of lists with different sizes in tf.data.Dataset

You can use tf.data.Dataset.from_generator() to convert any iterable Python object (like a list of lists) into a Dataset:

t = [[4, 2], [3, 4, 5]]

dataset = tf.data.Dataset.from_generator(lambda: t, tf.int32, output_shapes=[None])

iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()

with tf.Session() as sess:
  print(sess.run(next_element))  # ==> '[4, 2]'
  print(sess.run(next_element))  # ==> '[3, 4, 5]'

For those working with TensorFlow 2 and looking for an answer I found the following to work directly with ragged tensors. which should be much faster than generator, as long as the entire dataset fits in memory.

t = [[[4,2]],
     [[3,4,5]]]

rt=tf.ragged.constant(t)
dataset = tf.data.Dataset.from_tensor_slices(rt)

for x in dataset:
  print(x)

produces

<tf.RaggedTensor [[4, 2]]>
<tf.RaggedTensor [[3, 4, 5]]>

For some reason, it's very particular about having at least 2 dimensions on the individual arrays.

In addition to @mrry's answer, the following code is also possible if you would like to create (images, labels) pair:

import itertools
data = tf.data.Dataset.from_generator(lambda: itertools.izip_longest(images, labels),
                                      output_types=(tf.float32, tf.float32),
                                      output_shapes=(tf.TensorShape([None, None, 3]), 
                                                     tf.TensorShape([None])))

iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()

with tf.Session() as sess:
    image, label = sess.run(next_element)  # ==> shape: [320, 420, 3], [20]
    image, label = sess.run(next_element)  # ==> shape: [1280, 720, 3], [40]