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Blocks

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_.

.. _documentation: http://blocks.readthedocs.org .. _developer guidelines: http://blocks.readthedocs.org/en/latest/development/index.html .. _Blocks and Fuel\: Frameworks for deep learning: http://arxiv.org/abs/1506.00619 .. _Blocks-examples: https://github.com/mila-udem/blocks-examples .. _Blocks-extras: https://github.com/mila-udem/blocks-extras .. _Fuel: https://github.com/mila-udem/fuel