Save from python for net surgery

0. Scheme desired parameters.
1. Do surgery on the net through `net.params['name'][idx].data[...] = `.
2. Save post-operation net params by `net.save('fname')`.

Handwoven deep nets, anyone?

Surgeries done in this way:

- Collecting R-CNN into a single Caffe model binaryproto: take the svm weights and bias and transplant to fc8.
- Making a learned image classifier fully-convolutional for sliding window detection. Take the inner product layer weights, translate to convolution weights, and transplant the weight blobs to the new architecture. By re-formulating the classifiers as convolutions, a spatial classification map is generated on inputs of any size (> than the input dimensionality of the classifier).