Added feature to allow different learning rates per layer in the NN

[Tgaaly]

Added feature to allow different learning rates per layer in the NN. This feature is useful for transfer learning where you pre-training parts of the NN and then fine-tuning additional layers on top. The learning rates should be higher for the new layers and lower for the pre-trained layers. This is similar to what was done here: http://caffe.berkeleyvision.org/gathered/examples/finetune_flickr_style.html

By default the nn.learningRatePerLayer=[] and this will not cause an error in the default case as I check to see if its empty. The changes are very simple and straight forward.

[oxinabox]

This is really useful.

In my PR: #128
for DBNs it is also supported.
Because DBNs are RBMs and each RBM has a learning rate (alpha) parameter (in my PR, before I believe it was specced for DBNs in opts to the training function).