testcode for doc/tutorial/debug_faq.txt

doc/tutorial/debug_faq.txt

@@ -23,7 +23,7 @@ Interpreting Error Messages
 Even in its default configuration, Theano tries to display useful error
 messages. Consider the following faulty code.
 
-.. code-block:: python
+.. testcode::
 
     import numpy as np
     import theano
@@ -38,7 +38,7 @@ messages. Consider the following faulty code.
 
 Running the code above we see:
 
-.. code-block:: bash
+.. testoutput::
 
     Traceback (most recent call last):
       File "test0.py", line 10, in <module>
@@ -71,7 +71,7 @@ the faulty line, while ``exception_verbosity=high`` will display a
 debugprint of the apply node. Using these hints, the end of the error
 message becomes :
 
-.. code-block:: bash
+.. testoutput::
 
     Backtrace when the node is created:
       File "test0.py", line 8, in <module>
@@ -101,7 +101,7 @@ following example. Here, we use ``exception_verbosity=high`` and
 ``optimizer=None`` would and it could therefore be used instead of test values.
 
 
-.. code-block:: python
+.. testcode:: testvalues
 
     import numpy
     import theano
@@ -137,7 +137,7 @@ following example. Here, we use ``exception_verbosity=high`` and
 
 Running the above code generates the following error message:
 
-.. code-block:: bash
+.. testoutput:: testvalues
 
     Traceback (most recent call last):
       File "test1.py", line 31, in <module>
@@ -166,7 +166,7 @@ Running the above code generates the following error message:
 If the above is not informative enough, by instrumenting the code ever
 so slightly, we can get Theano to reveal the exact source of the error.
 
-.. code-block:: python
+.. testcode:: testvalues
 
     # enable on-the-fly graph computations
     theano.config.compute_test_value = 'warn'
@@ -185,7 +185,7 @@ of error can thus be identified with much more precision and much earlier in
 the compilation pipeline. For example, running the above code yields the
 following error message, which properly identifies *line 24* as the culprit.
 
-.. code-block:: bash
+.. testoutput:: testvalues
 
     Traceback (most recent call last):
       File "test2.py", line 24, in <module>
@@ -228,7 +228,10 @@ The ``compute_test_value`` mechanism works as follows:
 
 Theano provides a 'Print' op to do this.
 
-.. code-block:: python
+.. testcode::
+
+    import numpy
+    import theano
 
     x = theano.tensor.dvector('x')
 
@@ -243,6 +246,9 @@ Theano provides a 'Print' op to do this.
     #this runs the graph with the message, and value printed
     assert numpy.all( f_with_print([1, 2, 3]) == [5, 10, 15])
 
+.. testoutput::
+
+    this is a very important value __str__ = [ 1.  2.  3.]
 
 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
@@ -324,7 +330,7 @@ You can use ``MonitorMode`` to inspect the inputs and outputs of each
 node being executed when the function is called. The code snipped below
 shows how to print all inputs and outputs:
 
-.. code-block:: python
+.. testcode::
 
     import theano
 
@@ -341,8 +347,9 @@ shows how to print all inputs and outputs:
                             post_func=inspect_outputs))
     f(3)
 
-    # The code will print the following:
-    #   0 Elemwise{mul,no_inplace}(TensorConstant{5.0}, x) input(s) value(s): [array(5.0), array(3.0)] output(s) value(s): [array(15.0)]
+.. testoutput::
+
+    0 Elemwise{mul,no_inplace}(TensorConstant{5.0}, x) input(s) value(s): [array(5.0), array(3.0)] output(s) value(s): [array(15.0)]
 
 When using these ``inspect_inputs`` and ``inspect_outputs`` functions
 with ``MonitorMode``, you should see [potentially a lot of] printed output.
@@ -357,7 +364,7 @@ position, or only if a particular value showed up in one of the inputs or output
 A typical example is to detect when NaN values are added into computations, which
 can be achieved as follows:
 
-.. code-block:: python
+.. testcode:: compiled
 
     import numpy
 
@@ -385,12 +392,13 @@ can be achieved as follows:
                             post_func=detect_nan))
     f(0)  # log(0) * 0 = -inf * 0 = NaN
 
-    # The code above will print:
-    #   *** NaN detected ***
-    #   Elemwise{Composite{[mul(log(i0), i0)]}} [@A] ''
-    #    |x [@B]
-    #   Inputs : [array(0.0)]
-    #   Outputs: [array(nan)]
+.. testoutput:: compiled
+
+    *** NaN detected ***
+    Elemwise{Composite{(log(i0) * i0)}} [@A] ''   
+     |x [@B]
+    Inputs : [array(0.0)]
+    Outputs: [array(nan)]
 
 To help understand what is happening in your graph, you can
 disable the ``local_elemwise_fusion`` and all ``inplace``
@@ -402,12 +410,12 @@ will not be able to see the input that was overwriten in the ``post_func``
 function. To disable those optimizations (with a Theano version after
 0.6rc3), define the MonitorMode like this:
 
-.. code-block:: python
+.. testcode:: compiled
 
    mode = theano.compile.MonitorMode(post_func=detect_nan).excluding(
-       'local_elemwise_fusion', 'inplace)
-    f = theano.function([x], [theano.tensor.log(x) * x],
-                        mode=mode)
+       'local_elemwise_fusion', 'inplace')
+   f = theano.function([x], [theano.tensor.log(x) * x],
+                       mode=mode)
 
 .. note::
 
@@ -422,7 +430,7 @@ the execution of the node can garbage collect its inputs that aren't
 needed anymore by the Theano function. This can be done with the Theano
 flag:
 
-.. code-block:: cfg
+.. testcode:: compiled
 
    allow_gc=False
 
@@ -443,7 +451,7 @@ functions.
 
 Consider this example script ("ex.py"):
 
-.. code-block:: python
+.. testcode::
 
         import theano
         import numpy
@@ -464,7 +472,7 @@ This is actually so simple the debugging could be done easily, but it's for
 illustrative purposes. As the matrices can't be multiplied element-wise
 (unsuitable shapes), we get the following exception:
 
-.. code-block:: text
+.. testoutput::
 
     File "ex.py", line 14, in <module>
       f(mat1, mat2)