Generalize loss by allowing any top blob to be used as a loss in which
its elements are summed with a scalar coefficient.  Forward for layers
no longer returns a loss; instead all loss layers must have top blobs.
Existing loss layers are given a top blob automatically by Net::Init,
with an associated top_loss_weight of 1 (set in LossLayer::FurtherSetUp).
Due to the increased amount of common SetUp logic, the SetUp interface
is modified such that all subclasses should normally override FurtherSetUp
only, which is called by SetUp.
-Add tests for the case of multiple losses, one in the middle of the
network.  Make this case work by inserting split layers (and add another
SplitInsertion test).
-Add test that we can call Backward with an ACCURACY layer.  This
currently fails, but should be possible now that we explicitly associate a
loss weight with each top blob.
Net::Init can determine that layers don't need backward if they are not
used to compute the loss.
AccuracyLayer only dies when necessary.
Disallow in-place computation in SPLIT layer -- has strange effects in
backward pass when input into a loss.
Also display outputs in the train net.  (Otherwise, why have them?)
Add ACCURACY layer and softmax_error output to lenet_consolidated_solver example.
Store loss coefficients in layer; use for prettier training output.