getting rid of some bugs

doc/tutorial/faq.txt → doc/tutorial/faq_tutorial.txt

@@ -2,7 +2,8 @@
 
 ===========================
 Frequently Asked Questions
-==================================
+===========================
+
 How to update a subset of weights?
 ==================================
 If you want to update only a subset of a weight matrix (such as
@@ -18,36 +19,37 @@ the only rows that should get updated are those containing embeddings
 used during the forward propagation. 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.
->>> slice = lookup_table[vector_of_indices]
->>>
->>> # From now on, use only 'slice'.
->>> # Do not call lookup_table[vector_of_indices] 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.
->>> updates = inc_subtensor(slice, g*lr)
->>> # 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
+
+Getting a subset of the table (some rows or some columns) by passing
+an integer vector of indices corresponding to those rows or columns.
+
+>>> subset = lookup_table[vector_of_indices]
+
+From now on, use only 'subset'. Do not call lookup_table[vector_of_indices]
+again. This causes problems with grad as this will create new variables.
+
+Defining cost which depends only on subset and not the entire lookup_table
+
+>>> cost = something that depends on subset
+>>> g = theano.grad(cost, subset)
+
+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(subset, g*lr)
+OR
+>>> updates = set_subtensor(subset, subset + g*lr)
+
+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(..., updates=updates)
 
 Note that you can compute the gradient of the cost function w.r.t.

doc/tutorial/index.txt

@@ -46,4 +46,4 @@ you out.
     extending_theano_c
     python-memory-management
     multi_cores
-    faq
+    faq_tutorial