洞察探索open banking如何通过小程序容器技术助力金融企业实现数据安全和数字化转型
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2022-10-24
fuel-机器学习的数据管道框架
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 moreThe ability to iterate over your data in a variety of ways, such as in minibatches with shuffled/sequential examplesA 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 picklable_itertools 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.
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