CUDA dnn just alive.
Tons of bugs fixed. Ein = 0 for the first time.
Seperate train and predict as the way libSVM did.
Parser for libSVM input format.

Dense format of feature is also supported.
Split data into training and validation set.
[Bug Fixed] Set beta = 0 when adding bias.
Add mini-batch stochastic gradient descent.
[Refactor] Move training procedure into class DNN.
Change interface of feedforward and back-propagate.
Add data member _dw instead of passing it around.
Combine _w and _dw into class AffineTransform
Move feedForward and backPropagate to AffineTransfrom.
Redefine I/O interface of AffineTransform.
Softmax feedforward & backpropagte passed compiler.
Hold AffineTransform* instead of AffineTransform.

In class DNN, I changed the object type in _transforms (which is a
std::vector<> ) from AffineTransform to AffineTransform* to enable
polymorphism.
Keep label and probabilities in dataset at the same time.
Fixed inf problem in softmax by substracting max first.
[Bug Fixed] Assign prob = 0.001 for prob < 0.001 .
[Bug Fixed] priority of operators in backpropagate.
Multiclass classification done. 66% on ML2013Final.