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

Question

I have a long list of lists of integers (representing sentences, each one of different sizes) that I want to feed using the tf.data library. Each list (of the lists of list) has different length, and I get an error, which I can reproduce here:

t = [[4,2], [3,4,5]] dataset = tf.data.Dataset.from_tensor_slices(t) 

The error I get is:

ValueError: Argument must be a dense tensor: [[4, 2], [3, 4, 5]] - got shape [2], but wanted [2, 2]. 

Is there a way to do this?

EDIT 1: Just to be clear, I don't want to pad the input list of lists (it's a list of sentences containing over a million elements, with varying lengths) I want to use the tf.data library to feed, in a proper way, a list of lists with varying length.

Answer 1

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]' 

Answer 2

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.