Release 0.7: Native Layers, Batch Normalization, pandas.DataFrame and Memory Mapped Arrays

The sixth official release of scikit-neuralnetwork — version 0.7 — is available on PYPI from the following URL:
https://pypi.python.org/pypi/scikit-neuralnetwork

Or simply type this to install the latest version directly from the command-line with pip:

pip install scikit-neuralnetwork

This release includes a variety of improvements such as native layers (that let you include any Lasagne code directly), batch normalization, support for various data formats such as pandas.DataFrame and numpy Memory Mapped Arrays, as well as a variety of fixes and improvements. Read on for details!

Consult the stable documentation for more information:
http://scikit-neuralnetwork.readthedocs.org/en/stable/

The release file is attached here for reference too.

Features

• Native Layers to support backend features directly. #195
• Back to official Theano releases, now that Theano-0.8.0 is out. #191
• Batch normalization support, specified per-layer. #187
• Support for pandas.DataFrame as input data to fit, both X and y. #186
• Multi-feature classification returns list of arrays for convenience. #185
• Performance improvements and fixes in batch iterator. #170
• Support for numpy's Memory Mapped Arrays. [39b40d8]

Bug Fixes

• Calling set_parameters() even before initialize works. [0d42fa7]
• Fix unused parameter warning when using convolution layers. [1f6fb25]
• Fixed regularization support, L1 and L2 were not working correctly. [74e1778]
• Chaining get_parameters and set_parameters on another network. [24d9b3c]