cut faq items that are moved to tutorial

doc/extending/debug_faq.txt

 
-.. _debug_faq:
+.. _extend_faq:
 
 =========================================
-Debugging Theano: FAQ and Troubleshooting
+Extending Theano: FAQ and Troubleshooting
 =========================================
 
-There are many kinds of bugs that might come up in a computer program.
-This page is structured as an FAQ.  It should provide recipes to tackle common
-problems, and introduce some of the tools that we use to find problems in our
-Theano code, and even (it happens) in Theano's internals, such as
-:ref:`using_debugmode`.
-
-
-
-
-How do I print an intermediate value in a Function/Method?
-----------------------------------------------------------
-
-Theano provides a 'Print' Op to do this.
-
-.. code-block:: python
-
-    x = theano.tensor.dvector('x')
-
-    x_printed = theano.Print('this is a very important value')(x)
-
-    f = theano.function([x], x * 5)
-    f_with_print = theano.function([x], x_printed * 5)
-
-    #this runs the graph without any printing
-    assert numpy.all( f([1,2,3]) == [5, 10, 15])
-
-    #this runs the graph with the message, and value printed
-    assert numpy.all( f_with_print([1,2,3]) == [5, 10, 15])
-
-
-Since Theano runs your program in a topological order, you won't have precise
-control over the order in which multiple Print() Ops are evaluted.  For a more
-precise inspection of what's being computed where, when, and how, see the
-:ref:`faq_wraplinker`.
-
-
-
-
-
 I wrote a new Op/Type, and weird stuff is happening...
 ------------------------------------------------------
 
@@ -69,48 +30,3 @@ Then try running your program in :ref:`using_debugmode`.  DebugMode might
 catch something that you're not seeing.
 
 
-The function I compiled is too slow, what's up?
------------------------------------------------
-
-First, make sure you're running in FAST_RUN mode, by passing
-``mode='FAST_RUN'`` to ``theano.function`` or ``theano.make``. Some
-operations have excruciatingly slow Python implementations and that
-can negatively effect the performance of FAST_COMPILE.
-
-Second, try the theano :ref:`using_profilemode`.  This will tell you which
-Apply nodes, and which Ops are eating up your CPU cycles.
-
-
-.. _faq_wraplinker:
-
-How do I step through a compiled function with the WrapLinker?
---------------------------------------------------------------
-
-This is not exactly an FAQ, but the doc is here for now...
-It's pretty easy to roll-your-own evaluation mode.
-Check out this one:
-
-.. code-block:: python
-
-    class PrintEverythingMode(Mode):
-        def __init__(self):
-            def print_eval(i, node, fn):
-                print i, node, [input[0] for input in fn.inputs],
-                fn()
-                print [output[0] for output in fn.outputs]
-            wrap_linker = theano.gof.WrapLinkerMany([theano.gof.OpWiseCLinker()], [print_eval])
-            super(PrintEverythingMode, self).__init__(wrap_linker, optimizer='fast_run')
-
-When you use ``mode=PrintEverythingMode()`` as the mode for Function or Method,
-then you should see a lot of output.  Every Apply node will be printed out,
-along with its position in the graph, the arguments to the ``perform`` or
-``c_code`` and the output it computed.  Admittedly, this is a huge amount of
-output to read through if you are using big tensors... but you can choose to
-put logic inside of the print_eval function  that would, for example, only
-print something out if a certain kind of Op was used, at a certain program
-position, or if a particular value shows up in one of the inputs or outputs.
-
-.. TODO: documentation for link.WrapLinkerMany
-
-This can be a really powerful debugging tool.  Note the call to ``fn`` inside the call to ``print_eval``; without it, the graph wouldn't get computed at all!
-