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?
define fully-convolutional imagenet model
release net surgery example from #455
[docs] contact us on webpage
[fix] adding requirements.txt for web_demo. Closes #704
continuous integration of master build

check make

- all
- test
- lint

but not runtest or warn since they are not yet ported to master.
[example] standardize imagenet leveldb names
Changed path in description to point to parse_log.sh

It seems that parse_log.sh has moved from /path/to/caffe/scripts/ to /path/to/caffe/tools/extra. The edit changes the description to reflect this change. Nothing major really.
define caffe.Net input preprocessing members by boost::python

define `Net.{mean, input_scale, channel_swap}` on the boost::python side
so that the members always exist. drop ugly initialization logic.
[example] add caffe to pythonpath in all examples
[example] fix example outputs

With the right input processing, the actual image classification output
is sensible.

- filter visualization example's top prediction is "tabby cat"
- net surgery fully-convolutional output map is better

Fix incorrect class names too.
[example] include prediction in classification, time on GTX 770
fix pycaffe input processing

- load an image as [0,1] single / np.float32 according to Python convention
- fix input scaling during preprocessing:
  - scale input for preprocessing by `raw_scale` e.g. to map an image
    to [0, 255] for the CaffeNet and AlexNet ImageNet models
  - scale feature space by `input_scale` after mean subtraction
  - switch examples to raw scale for ImageNet models
  - fix #525
- preserve type after resizing.
- resize 1, 3, or K channel images with special casing between
  skimage.transform (1 and 3) and scipy.ndimage (K) for speed
take array in pycaffe `Net.set_mean()` instead of file path
fix pycaffe context cropping with or without mean
drop np.asarray() in favor of declaration (~1.75x speedup)
Merge pull request #816 from shelhamer/pycaffe-labels-grayscale-attrs-examples

Improve and polish pycaffe
clarify the license and copyright terms of the project

As has been the case, contributions are copyright their respective
contributors and the project is BSD-2 licensed. By contributing to the
project, contributors release their contributions under these copyright
and license terms as declared in LICENSE.
LICENSE governs the whole project so strip file headers
[docs] detail attribution, license, and copyright for development