Skip to content
/ Theano Public
forked from Theano/Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

License

Notifications You must be signed in to change notification settings

HHiroki/Theano

 
 

Repository files navigation

To install the package, see this page:

   http://deeplearning.net/software/theano/install.html

For the documentation, see the project website:

   http://deeplearning.net/software/theano/

Related Projects:

   https://github.com/Theano/Theano/wiki/Related-projects

It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package.

In order to build the documentation yourself, you will need sphinx. Issue the following command:
    python ./doc/scripts/docgen.py

Documentation is built into html/

The PDF of the documentation can be found at html/theano.pdf


DIRECTORY LAYOUT

Theano (current directory) is the distribution directory.
    * Theano/theano contains the package
    * Theano/theano has several submodules:
        * gof + compile are the core
        * scalar depends upon core
        * tensor depends upon scalar
        * sparse depends upon tensor
        * sandbox can depend on everything else
    * Theano/examples are copies of the example found on the wiki
    * Theano/benchmark and Theano/examples are in the distribution, but not in
      the Python package
    * Theano/bin contains executable scripts that are copied to the bin folder
      when the Python package is installed
    * Tests are distributed and are part of the package, i.e. fall in
      the appropriate submodules
    * Theano/doc contains files and scripts used to generate the documentation
    * Theano/html is where the documentation will be generated

About

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 92.8%
  • Cuda 2.7%
  • C++ 2.4%
  • C 2.0%
  • Shell 0.1%
  • HTML 0.0%