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September 6, 2018 11:41
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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.

Please see the documentation for more information.
If you want to contribute, please make sure to read the developer guidelines.