This commit contains the main fix for issue #85.  It contains a couple of changes to the YAML structure pruning API, with examples.
I urge you to read the documentation in the Wiki (https://github.com/NervanaSystems/distiller/wiki/Pruning-Filters-&-Channels).

New syntax for defining Structured AGP.  I tried to make the syntax similar to fine-grained
(i.e. element-wise) pruning.  All you need to do is add: ```group_type: Filters```.
```
  low_pruner:
    class: L1RankedStructureParameterPruner_AGP
    initial_sparsity : 0.10
    final_sparsity: 0.50
    group_type: Filters
    weights: [module.layer3.0.conv2.weight,
              module.layer3.0.downsample.0.weight,
              module.layer3.1.conv2.weight,
              module.layer3.2.conv2.weight]
```

If you want to define “leader-based” pruning dependencies, add ```group_dependency: Leader```:
```
  low_pruner:
    class: L1RankedStructureParameterPruner_AGP
    initial_sparsity : 0.10
    final_sparsity: 0.50
    group_type: Filters
    group_dependency: Leader
    weights: [module.layer3.0.conv2.weight,
              module.layer3.0.downsample.0.weight,
              module.layer3.1.conv2.weight,
              module.layer3.2.conv2.weight]
```

Retired the old ```reg_regims``` API for describing one-shot structured-pruning.

The new YAML API is very similar to AGP structured-pruning, which is much better
than before.
The new API also allows us to describe data-dependencies when doing one-shot
structure pruning, just like AGP structured-pruning.

This commit also includes further code refactoring.

Old API:
```
  filter_pruner:
     class: 'L1RankedStructureParameterPruner'
     reg_regims:
       'module.layer1.0.conv1.weight': [0.6, '3D']
       'module.layer1.1.conv1.weight': [0.6, '3D']
```

New API:
```
 filter_pruner:
    class: 'L1RankedStructureParameterPruner'
    group_type: Filters
    desired_sparsity: 0.6
    weights: [
      module.layer1.0.conv1.weight,
      module.layer1.1.conv1.weight]
```

thresholding.py – separate the generation of the binary_map from the pruning_mask so that we
can cache the binary map and share it between several modules.

pruning/automated_gradual_pruner.py – major refactoring to supported “leader-based”
sub-graph pruning dependencies.  The concept is explained in issue #85


agp-pruning/resnet20_filters.schedule_agp.yaml
agp-pruning/resnet20_filters.schedule_agp_2.yaml
agp-pruning/resnet20_filters.schedule_agp_3.yaml
network_trimming/resnet56_cifar_activation_apoz.yaml
network_trimming/resnet56_cifar_activation_apoz_v2.yaml