everything in it, it now makes a python file as before but an additional binary
file with all the weights in it.  This way, if you are working with a network
with a very large number of weights you won't end up with a crazy large python
script.

the behavior of dlib's add_prev layers.

when the converter is run.

So I just removed the error check that was preventing the conversion from
proceeding in that case.  I also added more useful output messages about
setting input tensor dimensions.

to caffe models.  Not all layers are supported yet, but it works on the net
from the first dlib dnn example program.  Will add the rest of the layers soon.