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/datashuffler/Memory.py

@@ -79,9 +79,17 @@ class Memory(Base):
         indexes = numpy.array(range(self.data.shape[0]))
         numpy.random.shuffle(indexes)
 
-        selected_data = self.data[indexes[0:self.batch_size], ...]
+        selected_data = numpy.zeros(shape=self.shape)
+        for i in range(self.batch_size):
+
+            selected_data[i, ...] = self.data[indexes[i], ...]
+            # Scaling
+            selected_data[i, ...] = self.normalize_sample(selected_data[i, ...])
+
         selected_labels = self.labels[indexes[0:self.batch_size]]
 
+        
+
         # Applying the data augmentation
         if self.data_augmentation is not None:
             for i in range(selected_data.shape[0]):