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/Disk.py

@@ -139,7 +139,7 @@ class Disk(Base):
     def get_batch_epoch(self):
 
       # this is done to rebuild the whole list (i.e. at the end of one epoch)
-      rebuild_indexes = False
+      epoch_done = False
 
       # returned mini-batch
       selected_data = numpy.zeros(shape=self.shape)
@@ -151,7 +151,7 @@ class Disk(Base):
 
         print "should add examples to the current minibatch {0}".format(len(self.indexes))
         # since we reached the end of an epoch, we'll hace to reconsider all the data
-        rebuild_indexes = True
+        epoch_done = True
         number_of_examples_to_add = self.batch_size - len(self.indexes) 
         added_examples = 0
         
@@ -173,7 +173,9 @@ class Disk(Base):
       for i in range(self.batch_size):
 
         current_index = self.batch_size - i - 1
-        
+       
+        # TODO: try/catch for file loading
+
         # get the data example
         file_name = self.data[self.indexes[current_index]]
         data = self.load_from_file(file_name)
@@ -193,8 +195,8 @@ class Disk(Base):
         selected_labels = numpy.array(selected_labels)
 
       # rebuild whole randomly shuffled training dataset
-      if rebuild_indexes:
+      if epoch_done:
         self.indexes = numpy.array(range(self.data.shape[0]))
         numpy.random.shuffle(self.indexes)
 
-      return [selected_data.astype("float32"), selected_labels.astype("int64")]
+      return [selected_data.astype("float32"), selected_labels.astype("int64"), epoch_done]