begun work on CompositeTensor for multi-input/targets
begining to better integrate batch with dataset/datatensor and models
DataSet and Batch now inherit BaseSet
ramblings about how to integrate criteria into model
added some abstract classes. removed ramblings
datatensor:copy :emptyClone, and initialization of datatensor from existing datatensor
dataset can generate emptyBatch(). basetensors can create emptyClone()s.
BaseTensors:sub()
added stuff to init. compiles
compositetensor++. torch.concat
torch.classof, torch.emptyClone, torch.concat, compositeTensor:feature()
torch.concat, etc. Starting to adapt nn.Criterion with dp.Loss.
begun work on distributed models (command.lua)
torch object transmission over network test
torch tcp tests++. Started work on Master
integrating async into distributed framework
confirmed distributed architecture (test-station*)
Merged global and carry. Input and output states are now dp.BaseTensors
propagators now work with new forward/backward interface
node, model and loss