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.