Replace pydotprint_variables by pydotprint and minor changes

doc/cifarSC2011/advanced_theano.txt

@@ -304,6 +304,8 @@ Consider the following logistic regression model:
 >>> train = theano.function(inputs=[x,y], outputs=[prediction, xent], updates=[[w, w-0.01*gw], [b, b-0.01*gb]], name = "train")
 >>> predict = theano.function(inputs=[x], outputs=prediction, name = "predict")
 
+We will now make use of Theano's printing features to compare the unoptimized
+graph (``prediction``) to the optimized graph (``predict``).
 
 - Pretty Printing
 
@@ -314,6 +316,8 @@ TensorConstant{0.5})'
 
 - Debug Print
 
+The graph before optimization:
+
 >>> theano.printing.debugprint(prediction) # doctest: +NORMALIZE_WHITESPACE
     Elemwise{gt,no_inplace} [@A] ''
     |Elemwise{true_div,no_inplace} [@B] ''
@@ -333,6 +337,8 @@ TensorConstant{0.5})'
     |DimShuffle{x} [@O] ''
       |TensorConstant{0.5} [@P]
 
+The graph after optimization:
+
 >>> theano.printing.debugprint(predict) # doctest: +NORMALIZE_WHITESPACE
     Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}} [@A] ''   4
      |CGemv{inplace} [@B] ''   3
@@ -352,18 +358,24 @@ TensorConstant{0.5})'
 - Picture Printing of Graphs
 ``pydotprint`` requires graphviz and pydot.
 
+The graph before optimization:
+
 >>> theano.printing.pydotprint(prediction, outfile="pics/logreg_pydotprint_prediction.png", var_with_name_simple=True)
 The output file is available at pics/logreg_pydotprint_prediction.png
 
 .. image:: ./pics/logreg_pydotprint_prediction.png
    :width: 800 px
 
+The graph after optimization:
+
 >>> theano.printing.pydotprint(predict, outfile="pics/logreg_pydotprint_predict.png", var_with_name_simple=True)
 The output file is available at pics/logreg_pydotprint_predict.png
 
 .. image:: ./pics/logreg_pydotprint_predict.png
    :width: 800 px
 
+The optimized training graph:
+
 >>> theano.printing.pydotprint(train, outfile="pics/logreg_pydotprint_train.png", var_with_name_simple=True)
 The output file is available at pics/logreg_pydotprint_train.png
 

doc/hpcs2011_tutorial/simple_example.py

@@ -5,5 +5,5 @@ f = theano.function([a], b)   # compile function
 print f([0,1,2])
 # prints `array([0,2,1026])`
 
-theano.printing.pydotprint_variables(b, outfile="pics/f_unoptimized.png", var_with_name_simple=True)
+theano.printing.pydotprint(b, outfile="pics/f_unoptimized.png", var_with_name_simple=True)
 theano.printing.pydotprint(f, outfile="pics/f_optimized.png", var_with_name_simple=True)

doc/tutorial/symbolic_graphs.txt

@@ -159,12 +159,14 @@ as we apply it. Consider the following example of optimization:
 >>> f = theano.function([a], b)        # compile function
 >>> print f([0, 1, 2])                 # prints `array([0,2,1026])`
 [    0.     2.  1026.]
+>>> theano.printing.pydotprint(b, outfile="./pics/symbolic_graph_unopt.png", var_with_name_simple=True)
+The output file is available at ./pics/symbolic_graph_unopt.png
 >>> theano.printing.pydotprint(f, outfile="./pics/symbolic_graph_opt.png", var_with_name_simple=True)
 The output file is available at ./pics/symbolic_graph_opt.png
 
 
-.. |g1| image:: ../hpcs2011_tutorial/pics/f_unoptimized.png
-    :width: 300 px
+.. |g1| image:: ./pics/symbolic_graph_unopt.png
+    :width: 500 px
 
 .. |g2| image:: ./pics/symbolic_graph_opt.png
     :width: 500 px