Blocks and Fuel: Frameworks for deep learning

By Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio. 2015

188 citations recorded by the source corpus.

We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support (Bastien et al., 2012; Bergstra et al., 2010). It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano’s symbolic computational graph, and providing an extensive set of utilities to assist training the networks, e.g. training algorithms, logging, monitoring, visualization, and serialization. Fuel provides a standard format for machine learning datasets. It allows the user to easily iterate over large datasets, performing many types of pre-processing on the fly.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, ML Systems & Hardware

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Citations in the corpus

Theano: A Python framework for fast computation of mathematical expressions (2016, background): “For instance, machine learning and deep learning packages, such as Pylearn2 [8], Blocks [9], Lasagne [10], and Keras [11], have been developed with the goal of making it easier to express the architecture of deep learning models, and training algorithms, as mathematical expressions to be evaluated b”
Semi-supervised Learning with Ladder Networks (2015, methodology): “The software for the simulations for this paper was based on Theano (Bastien et al., 2012; Bergstra et al., 2010) and Blocks (van Merriënboer et al., 2015).”
End-to-end attention-based large vocabulary speech recognition (2015, methodology): “The experiments were conducted using Theano [26, 27], Blocks and Fuel [28] libraries.”
Learning Language-Visual Embedding for Movie Understanding with Natural-Language (2016, methodology): “Our data pipeline has been implemented using FUEL framework [15].”
A Neural Autoregressive Approach to Collaborative Filtering (2016, methodology): “We implement CF-NADE using Theano (Bastien et al., 2012) and Blocks (van Merri¨enboer et al., 2015), and the code is available at https://github.com/Ian09/ CF-NADE .”
Batch normalized recurrent neural networks (2015, methodology): “The models were implemented using Theano [18] and Blocks [19].”
Twin Networks: Matching the Future for Sequence Generation (2017, methodology): “We would also like to thank the developers of Theano Theano Development Team (2016), Blocks and Fuel van Merriënboer et al. (2015), and Pytorch for developments of great frameworks.”