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Blocks

一个用于构建和训练神经网络的 Theano 框架。「A Theano framework for building and training neural networks」

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  • 所有者: mila-iqia/blocks
  • 平台: Linux, Mac, Web browsers, Windows
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Blocks

Blocks 是一个帮助你在 Theano 之上构建神经网络模型的框架。目前它支持并提供：

  • 构建参数化的 Theano 操作，称为 "bricks"。
  • 在大型模型中选择变量和 bricks 的模式匹配。
  • 优化模型的算法。
  • 训练的保存和恢复。
  • 在训练过程中监测和分析数值（在训练集和测试集上）。
  • 图形变换的应用，如 dropout 等。

在未来，我们也希望能够支持：

  • 尺寸、类型和轴检查

另见

  • Fuel，主要为 Blocks 开发的数据处理引擎。
  • Blocks-examples，用于维护使用 Blocks 的脚本示例。
  • Blocks-extras：半维护的附加 Blocks 组件。

引用Blocks

如果您在工作中使用了 Blocks 或 Fuel，如果您能引用以下论文，我们将非常感激：

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.

文档介绍

更多信息请参见文档。

贡献

如果你想做出贡献，请务必阅读开发者指南。


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概览
名称与所有者mila-iqia/blocks
主编程语言Python
编程语言Python (语言数: 1)
平台Linux, Mac, Web browsers, Windows
许可证Other
所有者活动
创建于2014-10-06 00:08:32
推送于2019-02-19 12:41:38
最后一次提交2018-09-06 12:02:54
发布数4
最新版本名称0.2 (发布于 )
第一版名称v0.0.1 (发布于 )
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.. image:: https://img.shields.io/coveralls/mila-udem/blocks.svg
:target: https://coveralls.io/r/mila-udem/blocks

.. image:: https://travis-ci.org/mila-udem/blocks.svg?branch=master
:target: https://travis-ci.org/mila-udem/blocks

.. image:: https://readthedocs.org/projects/blocks/badge/?version=latest
:target: https://blocks.readthedocs.org/

.. image:: https://img.shields.io/scrutinizer/g/mila-udem/blocks.svg
:target: https://scrutinizer-ci.com/g/mila-udem/blocks/

.. image:: https://requires.io/github/mila-udem/blocks/requirements.svg?branch=master
:target: https://requires.io/github/mila-udem/blocks/requirements/?branch=master

.. image:: https://img.shields.io/badge/license-MIT-blue.svg
:target: https://github.com/mila-udem/blocks/blob/master/LICENSE

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