SImplify Neuron parameter passing.
Comment out missing value handling code (not needed as FrameTask masks it).
Pass the chunk row offset to ChunkInit() and make random numbers deterministic across chunks/VMs.
Improve seeding of random numbers for NN training across VMs.
Also don't initialize RNGs for dropout testing (no randomness needed there).
Obtain the same behavior between NeuralNet and NN code, as demonstrated by the
Iris/Iris2 tests (Note: Iris2 agrees with reference only if fast_mode=false).
Tested for Tanh and ReLu with and without Dropout.
Add back the (1-m) learning rate reduction factor for training with momentum.
Note that this breaks the Iris2 test for momentum (we only test for m=0 now).
Adapt Iris2 test to handle non-zero momentum, after the 1-m correction.
Make NeuralNet and NN logically deterministic via per-row seeding.  Hogwild
will still lead to non-determinism.
Fix input dropout.
Cleanup NNModel toString().
Improve key creation/deletion for NN.
Change Web GUI 2 auto-refresh (polling) time from 2 seconds to 5 seconds.
Allow input dropout for all activation functions.
Clean up input dropout usage (placed in Input layer).
Separate makeNeurons into makeNeuronsFor{Training,Testing}.
Pass global row id to every layer's fprop() and to input layer's setInput().
Add JUnit tests for makeNeuronsFor{Training,Testing} and Dropout.