Add NeuralNetIrisTest to JUnitRunnerDebug
Cosmetics for NN Mnist viz: Add layer number to each plot
Minor code cleanup
Add ability to pass a random seed to the frame splitter
Use ~user instead of /home/user/
Add support for dropout on input layer, can lead to higher accuracy.

Note: Only for multi-threaded, not yet for MapReduce framework.
Use dropout on input layer for Mnist sample code
Use the threaded version for NN visualization.
Merge branch 'master' into experimental/nn
Rename "Neural Network" to "Single Node Neural Network", to prevent users from
using the Threaded version on multiple nodes (it won't be faster there, but it
should be correct).
Merge branch 'master' into experimental/nn
Add a toggle to select whether NN is run serially, threaded or via MapReduce.
Speedup input dropout code, and remove unfinished Spike version
Add parameter for max squared weight per unit.
Note: defaults to 15, which is Mnist-specific => need to tune for other datasets
Add support for user-given random initialization of weights.  One of Auto, Uniform, Normal.
For Uniform and Normal, an extra parameter is expected to specify the range (-val...val or stddev).
Disallow certain NeuralNet inputs under certain conditions
Scale random initialization amplitude for Softmax and MaxOut by a factor of 4.0
For dropout, properly multiply weights by p (0.5) instead of the activation values.

Add TanhWithDropout
Add model parameters to NeuralNetModel
Rename initial weight distribution parameters
Add support for rectifier to NeuralNet MLP reference implementation, needs testing.
Add support for loss function selection throughout ( MSE / CrossEntropy)