You have multiple options to get up and running, though using pip is by far the easiest and most reliable.

B) Pulling Repositories [Optional]

If you want to use the more advanced features like convolution, pooling or upscaling, these depend on the latest code from Lasagne and Theano master branches. You can install them manually as follows:

> pip install -r

Once that’s done, you can grab this repository and install from in the exact same way:

> git clone
> cd scikit-neuralnetwork; python develop

This will make the sknn package globally available within Python as a reference to the current directory.

Running Tests

We encourage you to launch the tests to check everything is working using the following commands:

> pip install nose
> nosetests -v sknn

Use the additional command-line parameters in the test runner --processes=8 and --process-timeout=60 to speed things up on powerful machines. The result should look as follows in your terminal.


We strive to maintain 100% test coverage for all code-paths, to ensure that rapid changes in the underlying Lasagne and Theano libraries are caught automatically.