correcting the code part of faq.txt

doc/tutorial/faq.txt

@@ -2,9 +2,9 @@
 
 ===========================
 Frequently Asked Questions
-===========================
-Updating a Subset of Weights
-=============================
+==================================
+How to update a subset of weights?
+==================================
 If you want to update only a subset of a weight matrix (such as
 some rows or some columns) that are used in the forward propogation
 of this iteration, then the cost function should be defined in a way
@@ -18,36 +18,36 @@ iteration only the rows of the matrix should get updated that their
 corresponding words were used in the forward propogation. Here is
 how the theano function should be written:
 
-# defining a shared variable for the lookup table
+>>> # defining a shared variable for the lookup table
 >>> lookup_table = theano.shared(matrix_ndarray).
-
-# getting a subset of the table (some rows
-# or some columns) by passing an integer vector of
-# indices corresponding to those rows or columns.
+>>>
+>>> # getting a subset of the table (some rows
+>>> # or some columns) by passing an integer vector of
+>>> # indices corresponding to those rows or columns.
 >>> slice = lookup_table[vector_of_indeces]
-
-# From now on, use only 'slice'.
-# Do not call lookup_table[vector_of_indeces] again.
-# This causes problems with grad as this will create new variables.
-
-# defining cost which depends only on slice
-# and not the entire lookup_table
+>>>
+>>> # From now on, use only 'slice'.
+>>> # Do not call lookup_table[vector_of_indeces] again.
+>>> # This causes problems with grad as this will create new variables.
+>>>
+>>> # defining cost which depends only on slice
+>>> # and not the entire lookup_table
 >>> cost = something that depends on slice
 >>> g = theano.grad(cost, slice)
-
-# There are two ways for updating the parameters:
-# either use inc_subtensor or set_subtensor.
-# It is recommended to use inc_subtensor.
-# Some theano optimizations do the conversion between
-# the two functions, but not in all cases.
+>>>
+>>> # There are two ways for updating the parameters:
+>>> # either use inc_subtensor or set_subtensor.
+>>> # It is recommended to use inc_subtensor.
+>>> # Some theano optimizations do the conversion between
+>>> # the two functions, but not in all cases.
 >>> updates = inc_subtensor(slice, g*lr)
-# OR
+>>> # OR
 >>> updates = set_subtensor(slice, slice + g*lr)
-
-# Note that currently we just cover the case here,
-# not if you use inc_subtensor or set_subtensor with other types of indexing.
-
-#defining the theano function
+>>>
+>>> # Note that currently we just cover the case here,
+>>> # not if you use inc_subtensor or set_subtensor with other types of indexing.
+>>>
+>>> #defining the theano function
 >>> f=theano.function(..., update=updates)
 
 Note that you can compute the gradient of the cost function w.r.t.