Save from python for net surgery

[shelhamer]

1. Scheme desired parameters.
2. Do surgery on the net through net.params['name'][idx].data[...] =.
3. 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).

[shelhamer]

As requested in #450. Thanks for the reminder to push this.

[shelhamer]

I'll add an example notebook of doing a surgery and seeing the change in results, then let's merge.

[Yangqing]

Rowland Depp did a similar task to convert imagenet pretrained models to fully convolutional, involving some late night ipython hacking - it's a great-to-have feature :)

[shelhamer]

@Yangqing I added a notebook detailing that very same model conversion.

Merging now that an example of net surgery is included.