*An implementation of AMC (the previous implementation
 code has moved to a new location under 
/distiller/examples/auto_compression/amc.  AMC is aligned
with the ‘master’ branch of Coach.
*compress_classifier.py is refactored.  The base code moved
to /distiller/apputils/image_classifier.py.  Further refactoring
will follow.
We want to provide a simple and small API to the basic features of
a classifier-compression application.
This will help applications that want to use the make features of a
classifier-compression application, without the standard training
regiment.
AMC is one example of a stand-alone application that needs to leverage
the capabilities of a classifier-compression application, but is currently
coupled to `compress_classifier.py`.
`multi-finetune.py` is another example.
* ranked_structures_pruner.py:
** Added support for grouping channels/filters
Sometimes we want to prune a group of structures: e.g. groups of
8-channels.  This feature does not force the groups to be adjacent,
so it is more like a set of structures.  E.g. in the case of pruning
channels from a 64-channels convolution, grouped by 8 channels, we 
will prune exactly one of 0/8/16/24/32/40/48/56 channels.  I.e. 
always a multiple of 8-channels, excluding the set of all 64 channels.
** Added FMReconstructionChannelPruner – this is channel
pruning using L1-magnitude to rank and select channels to
remove, and feature-map reconstruction to improve the
resilience to the pruning.
* Added a script to run multiple instances of an 
experiment, in different processes:
 examples/classifier_compression/multi-run.py
* Set the seed value even when not specified by the command-line
arguments, so that we can try and recreate the session.
* Added pruning ranking noise -
Ranking noise introduces Gaussian noise when ranking channels/filters
using Lp-norm.  The noise is introduced using the epsilon-greedy
methodology, where ranking using exact Lp-norm is considered greedy.
* Added configurable rounding of pruning level: choose whether to 
Round up/down when rounding the number of structures to prune 
(rounding is always to an integer).  

amc-ft-frequency:
Sometimes we may want to fine-tune the weights after
‘n’ number of episode steps (action-steps).   This new
argument  controls the frequency of this fine-tuning (FT)
How many action-steps between fine-tuning
By default, there is no fine-tuning between steps.

amc-reward-frequency:
By default, we only provide a non-zero reward at the end
episodes.  This argument allows us to provide rewards at
a higher frequency.

This commit also reorders the ResNet layer names, so that
layers are processed by near-topological order.  This is simply
to help interpret the data in the AMC Jupyter notebooks.

--amc-reward-frequency
Computing the reward requires running the evaluated network on the Test
dataset (or parts of it) and may involve short-term fine-tuning before
the evaluation (depending on the configuration).
Use this new argument to configure the number of steps/iterations between
reward computation.

Merging the 'amc' branch with 'master'.
This updates the automated compression code in 'master', and adds a greedy filter-pruning algorithm.