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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.
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nouiz Merge pull request #6689 from justanhduc/fixed-frac-bilinear-upsample
added test for output shape of frac bilinear upsampling
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theano Merge pull request #6689 from justanhduc/fixed-frac-bilinear-upsample Mar 3, 2019
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README.rst

MILA will stop developing Theano.

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
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