Blocks is a framework that helps you build neural network models on top of Theano. Currently it supports and provides:

• Constructing parametrized Theano operations, called "bricks"
• Pattern matching to select variables and bricks in large models
• Algorithms to optimize your model
• Saving and resuming of training
• Monitoring and analyzing values during training progress (on the training set as well as on test sets)
• Application of graph transformations, such as dropout

In the future we also hope to support:

• Dimension, type and axes-checking

See Also:

• Fuel, the data processing engine developed primarily for Blocks.
• Blocks-examples for maintained examples of scripts using Blocks.
• Blocks-extras for semi-maintained additional Blocks components.

Citing Blocks

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.

Contributing

If you want to contribute, please make sure to read the developer guidelines.