Add "lintclean" target to remove current lint outputs -- forces lint to
be run again next time "make lint" is run.
Enable the 'build/include_alpha' rules to make lint check that the
includes are correctly alphabetized.
Fix header alphabetization lint errors.
Fix conflict on setting of LRN layers between train/test net and deploy net
Merge pull request #859 from beam2d/fix-cifar-lrn-region

Fix conflict on setting of LRN layers between train/test net and deploy net
Merge pull request #856 from jeffdonahue/lint-tweaks

add header alphabetization to lint checks
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

- represent images as [0, 255] single / np.float32 throughout to agree
  with data layers.
- scale feature space after mean subtraction (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
turn off default scaling in classify.py and detector.py

since input preprocessing works properly now.
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)