Release Candidate for the LRP Toolbox 1.2.0

Code work is done and functionality is complete.
Future changes, up to version 1.2.0 will be concerned with documentation.

New in this version:
For python and matlab:

• Conv-Layer support and sum- and maxpooling have been implemented
• rudimentary network training support for all existing layers
• pre-setting lrp decomposition parameters per layer implemented
• w² and flat weight decompositions implemented for all layers (where applicable)
• demo code demonstrating LRP on LeNet-5, showing off the new features
• a slight change in the plain text model description. Backwards-compatibility for older toolbox versions is ensured.

Caffe:

• heatmapping applications producing minimal outputs have been implemented. Instead of writing out the network input and render heatmaps as images, only plain text relevance maps and the top-10 network predictions are produced per sample.
• w² and flat weight decomposition methods implemented, including a layer selection index whilch allows for a treatment with either w² or flat decomposition for all layers blow (inclusive) this layer index, and either eps- or alpha-decomposition for all upper layers. This can be used to control the resolution and semantics of the explanatory heatmaps. (See http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7532763)