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Fuel

Fuel provides your machine learning models with the data they need to learn.

• Interfaces to common datasets such as MNIST, CIFAR-10 (image datasets), Google's One Billion Words (text), and many more
• The ability to iterate over your data in a variety of ways, such as in minibatches with shuffled/sequential examples
• A pipeline of preprocessors that allow you to edit your data on-the-fly, for example by adding noise, extracting n-grams from sentences, extracting patches from images, etc.
• Ensure that the entire pipeline is serializable with pickle; this is a requirement for being able to checkpoint and resume long-running experiments. For this, we rely heavily on the picklableitertools library.

Fuel is developed primarily for use by Blocks_, a Theano toolkit that helps you train neural networks.

If you have questions, don't hesitate to write to the mailing list_.

Citing Fuel If you use Blocks or Fuel in your work, we'd really appreciate it if you could cite the following paper:

Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, and Yoshua Bengio, "Blocks and Fuel: Frameworks for deep learning_," arXiv preprint arXiv:1506.00619 [cs.LG], 2015.

Documentation Please see the documentation_ for more information.

.. _picklable_itertools: http://github.com/dwf/picklable_itertools .. _Blocks: http://github.com/mila-udem/blocks .. _mailing list: https://groups.google.com/d/forum/fuel-users .. _documentation: http://fuel.readthedocs.org/en/latest/ .. _Blocks and Fuel\: Frameworks for deep learning: http://arxiv.org/abs/1506.00619