[src] batch renormalization finished

[GaofengCheng]

I have finished the draft of batch-renorm.
I tested it under Switchboard + 2-layer-TDNN-F(without dropout) + 3epoch + 64 batch-size
A: batch-renorm r:1.0 d:0.0
B: batch-renorm r:1.0 d:0.0 -> r:1.2 d:0.4 (at iter 8) -> r:1.6 d:0.8 (at iter 45)

 A         B
# WER on train_dev(tg)      18.37     18.47
# WER on train_dev(fg)      16.69     16.79
# WER on eval2000(tg)        20.7      20.7
# WER on eval2000(fg)        18.8      18.6
# WER on rt03(tg)            25.7      25.6
# WER on rt03(fg)            22.2      22.2
# Final train prob         -0.121    -0.113
# Final valid prob         -0.130    -0.128
# Final train prob (xent)        -2.545    -2.525
# Final valid prob (xent)       -2.4666   -2.4552
# Num-parameters               20992380  20992380

Some notes:

1. I have verified that the batch-renorm with r-1.0 d-0.0 performs almost the same as the previous batch norm
2. for batch renorm, I picked the moving averages of one of the parallel jobs instead of averaging them
3. for debuggin, i kept the sum_mean_/uvar_ of batch-renorm, but they are useless for batch-renorm, we can remove them later
4. I have checked my derivatives of BP, I should do it right. Maybe you can double check with the original equations from the authors (I have checked it, but this is a fundamental component, I think it's necessary to do it twice).

[danpovey]

You need to keep the documentation up to date!
But I may rewrite parts of this so this may no end up mattering.
My concern is that the original formulation of BatchNorm does not make sense when minibatch sizes differ and where the stats may differ substantially (e.g. because the language differs).

[danpovey]

This is against the Google style guide; should be ScaleTraining.
However, I think it would be better to just use the regular Scale() function, you can see how I've done it in my version.