Gan

Ok, so I think that the modifications I made here can be merged ...

Two main changes as compared to the master:

1. Added some layers
2. Modified the way variable name and scope is handled in layers (and in SequenceNetwork as well)

Change 1 should by no means affect what has been done until now (on the master). However, change 2 may require a bit of adaptation, especially when defining architectures and layers ...

@tiago.pereira I'll let you check and then we can discuss

Also, I think that this will be irrelevant when we'll go to tf-slim anyway !

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -65,7 +65,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         self.pickle_architecture = pickle.dumps(self.sequence_net)
         self.deployment_shape = shape
 
-    def compute_graph(self, input_data, feature_layer=None, training=True):
+    def compute_graph(self, input_data, feature_layer=None, training=True, scope=None):
         """Given the current network, return the Tensorflow graph
 
          **Parameter**
@@ -77,18 +77,18 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
           training: If `True` will generating the graph for training
         """
-
         input_offset = input_data
         for k in self.sequence_net.keys():
             current_layer = self.sequence_net[k]
 
             if training or not isinstance(current_layer, Dropout):
-                current_layer.create_variables(input_offset)
+                current_layer.create_variables(input_offset, scope=scope)
                 input_offset = current_layer.get_graph(training_phase=training)
 
                 if feature_layer is not None and k == feature_layer:
                     return input_offset
 
+
         return input_offset
 
     def compute_inference_graph(self, feature_layer=None):
@@ -148,9 +148,9 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
                 variables[self.sequence_net[k].b.name] = self.sequence_net[k].b
 
                 # Dumping batch norm variables
-                if self.sequence_net[k].batch_norm:
-                    variables[self.sequence_net[k].beta.name] = self.sequence_net[k].beta
-                    variables[self.sequence_net[k].gamma.name] = self.sequence_net[k].gamma
+                #if self.sequence_net[k].batch_norm:
+                    #variables[self.sequence_net[k].beta.name] = self.sequence_net[k].beta
+                    #variables[self.sequence_net[k].gamma.name] = self.sequence_net[k].gamma
                     #variables[self.sequence_net[k].mean.name] = self.sequence_net[k].mean
                     #variables[self.sequence_net[k].var.name] = self.sequence_net[k].var