Do snapshot after computing loss and test accuracy
Merge pull request #51 from sguada/solver_test

Do snapshot after computing loss and test accuracy
return model weights
Paths for my local setup with anaconda
first pass at implementing WindowDataLayer: a data provider for training a detection net from image windows
replace tp_threshold with fg and bg thresholds
adjustments to try to match the setup for fine tuning with cuda-convnet
demo on how to get net weights using the matlab interface
some major bug fixes (includes some to-be-removed debugging code)
Code that was used to finetune with reasonable success
support for adding padding to windows in the window_data_layer
add missing file
10x learning rate for fine tuning makes a big difference
add initialization key for verifying state
support for tightest square mode while finetuning
fix paths
some cleanup
be less verbose
set default to the best value
Merge remote-tracking branch 'origin/finetuning' into finetuning

Conflicts:
	src/caffe/proto/caffe.proto