# HG changeset patch
# User Olivier Delalleau <delallea@iro>
# Date 1289579790 18000
# Node ID 01157763c2d7c7a53189c2064d8e5104aa9ba839
# Parent  18b2ebec6bca1a6c7dd269b250929d5d4ab5fa1d
Reply to Razvan

diff -r 18b2ebec6bca -r 01157763c2d7 doc/v2_planning/datalearn.txt
--- a/doc/v2_planning/datalearn.txt	Fri Nov 12 11:11:49 2010 -0500
+++ b/doc/v2_planning/datalearn.txt	Fri Nov 12 11:36:30 2010 -0500
@@ -219,6 +219,13 @@
 transformation rooted in a dataset, and if you want same transformation
 for a different dataset you have to re-write everything.
 
+OD replies: Still not sure I understand. If you have a "graph" function that
+takes a dataset as input and outputs a new dataset, you can use this same
+function with both (1) and (2). With (2) it is:
+    theano.function([index], graph(my_dataset)[index].variable)
+while with (1) the same function is compiled implicitly with:
+    for sample in graph(my_dataset):
+        ...
 
  - in approach (1) the initial dataset object (the one that loads the data)
    decides if you will use shared variables and indices to deal with the