CODEWITHSUNDEEP
pythontensorflowtensorflow-datasets
Escachator
Escachator

Reputation: 1881

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

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.

Upvotes: 31

Views: 20659

Answers (4)

FlashDD
FlashDD

Reputation: 438

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.

Upvotes: 10

Dat
Dat

Reputation: 5803

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]

Upvotes: 1

mrry
mrry

Reputation: 126154

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]'

Upvotes: 27

scrpy
scrpy

Reputation: 1021

I don't think tensorflow supports tensors with varying numbers of elements along a given dimension.

However, a simple solution is to pad the nested lists with trailing zeros (where necessary):

t = [[4,2], [3,4,5]]
max_length = max(len(lst) for lst in t)
t_pad = [lst + [0] * (max_length - len(lst)) for lst in t]
print(t_pad)
dataset = tf.data.Dataset.from_tensor_slices(t_pad)
print(dataset)

Outputs:

[[4, 2, 0], [3, 4, 5]]
<TensorSliceDataset shapes: (3,), types: tf.int32>

The zeros shouldn't be a big problem for the model: semantically they're just extra sentences of size zero at the end of each list of actual sentences.

Upvotes: 0

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