Blocks and Fuel: Frameworks for deep learning
This work provides tools to streamline deep learning workflows for researchers and practitioners, but it is incremental as it builds on existing technologies like Theano.
The authors introduced Blocks and Fuel, two Python frameworks designed to train neural networks on large datasets, with Blocks simplifying model training through Theano-based utilities and Fuel standardizing dataset handling and preprocessing.
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. 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.