test_gradient_check_util: blobid -> blob_id
gradient checker optimization with forward pass loss: only need to run
backward pass to compute analytic gradient (the thing being checked) now
revert unnecessary reordering of lines in softmaxwithlosslayer backward
remove accidentally added empty line
fix softmax loss layer bug; all tests pass
loss in forward pass for concat layer (thought i'd rebased to latest dev
but apparently not)
null pointer defaults for forward loss outputs
post rebase fixes: images layer and padding layer compute loss in
forward
Merge pull request #161 from kloudkl/simplify_feature_extraction

Feature extraction, feature binarization and image retrieval examples
Fix to #161
- signficantly change the documentation file
- link to it from index.md
- remove the image resizing script, since (a) it does not work, (b) is obviated by using ImagesLayer
- add sample prototxt that uses ImagesLayer.
Back-merge documentation and script fixes

fix script path incantation
convert css indentation to spaces
fix cifar10 leveldb creation path
wget without checking certificate for dropbox (dodge complaint on linux)
docs: added list of contributors
minor style update of docs
Merge pull request #209 from jeffdonahue/loss-in-forward-pass

Compute loss in the forward pass
loss in forward pass fix for window data layer
Merge pull request #247 from jeffdonahue/loss-in-forward-window-data-layer

Loss in forward pass fix for window data layer
compile caffe without MKL (dependency replaced by boost::random, Eigen3)

- examples, test and pycaffe compile without problem (matcaffe not tested)
- tests show some errors (on cpu gradient tests), to be investigated
- random generators need to be double checked
- mkl commented code needs to be removed
Fixed uniform distribution upper bound to be inclusive
Fixed FlattenLayer Backward_cpu/gpu have no return value
Fix test stochastic pooling stepsize/threshold to be same as max pooling
Fix math funcs, add tests, change Eigen Map to unaligned for lrn_layer

[shelhamer: removed math function tests, since they were merged via
other branches]
relax precision of MultinomialLogisticLossLayer test