polished ignore
define AlexNet architecture

Swap order of lrn and max pooling layers to reflect Krizhevsky
architecture. Initialize biases to 0.1 where Krizhevsky had 1, as
training by initialization with 1 has flat loss.
Document AlexNet model, include download script
include pretrained snapshot and performance details
Merge pull request #327 from shelhamer/alexnet

Include Caffe model for Krizhevsky architecture (AlexNet)
note support for non-MKL installation in dev
Merge pull request #339 from sergeyk/dev

Update installation docs
Proofread install docs
Comment current forward/backward responsibilities
fix typo pointed out by @yinxusen
fix bug where DataLayerPrefetch creates its own Caffe singleton, causing
the phase to always be set to TRAIN (always random crops) and RNG failures
do the same as prev commit for ImageDataLayer
Merge pull request #348 from jeffdonahue/datalayer-singleton-bugfix

Fix singleton call in data layers

Note previous crash reports were not in fact due to RNG, although they led to improvements of the RNG code.
make CheckGradientExhaustive fail for topless layers

Without this commit, it is possible to mistakenly call
CheckGradientExhaustive on a layer with no top blobs (i.e., a loss
layer), causing the gradient check to silently succeed while doing
nothing. With this commit, doing so will cause the test to fail.
Merge pull request #350 from longjon/finicky-exhaustive

Make CheckGradientExhaustive fail for topless layers
add forward tests (via reference impl) for SigmoidCrossEntropyLayer
add random_seed field to SolverParameter and have solver use it --
already works for lenet, doesn't work for imagenet w/ rand() calls
add data layer crop tests
add tests for random crop sequence -- currently both fail
cleanup data_layer, add prefetch_rng_ field to it and use instead of
rand -- seeded tests still fail