Change @ to # in debugprint

doc/library/printing.txt

@@ -81,19 +81,19 @@ iteration number or other kinds of information in the name.
 2) The second function to print a graph is :func:`theano.printing.debugprint`
 
 >>> theano.printing.debugprint(f.maker.fgraph.outputs[0])  # doctest: +NORMALIZE_WHITESPACE
-Elemwise{mul,no_inplace} [@A] ''
- |TensorConstant{2.0} [@B]
- |x [@C]
+Elemwise{mul,no_inplace} [#A] ''
+ |TensorConstant{2.0} [#B]
+ |x [#C]
 
 Each line printed represents a Variable in the graph.
-The line ``|x [@C`` means the variable named ``x`` with debugprint identifier
-[@C] is an input of the Elemwise.  If you accidentally have two variables called ``x`` in
+The line ``|x [#C]`` means the variable named ``x`` with debugprint identifier
+[#C] is an input of the Elemwise.  If you accidentally have two variables called ``x`` in
 your graph, their different debugprint identifier will be your clue.
 
-The line ``|TensorConstant{2.0} [@B]`` means that there is a constant 2.0
+The line ``|TensorConstant{2.0} [#B]`` means that there is a constant 2.0
 with this debugprint identifier.
 
-The line ``Elemwise{mul,no_inplace} [@A] ''`` is indented less than
+The line ``Elemwise{mul,no_inplace} [#A] ''`` is indented less than
 the other ones, because it means there is a variable computed by multiplying
 the other (more indented) ones together.
 
@@ -106,25 +106,26 @@ printed?  Look for debugprint identifier using the Find feature of your text
 editor.
 
 >>> theano.printing.debugprint(gy)  # doctest: +NORMALIZE_WHITESPACE
-Elemwise{mul} [@A] ''
- |Elemwise{mul} [@B] ''
- | |Elemwise{second,no_inplace} [@C] ''
- | | |Elemwise{pow,no_inplace} [@D] ''
- | | | |x [@E]
- | | | |TensorConstant{2} [@F]
- | | |TensorConstant{1.0} [@G]
- | |TensorConstant{2} [@F]
- |Elemwise{pow} [@H] ''
-   |x [@E]
-   |Elemwise{sub} [@I] ''
-     |TensorConstant{2} [@F]
-     |DimShuffle{} [@J] ''
-       |TensorConstant{1} [@K]
+Elemwise{mul} [#A] ''
+ |Elemwise{mul} [#B] ''
+ | |Elemwise{second,no_inplace} [#C] ''
+ | | |Elemwise{pow,no_inplace} [#D] ''
+ | | | |x [#E]
+ | | | |TensorConstant{2} [#F]
+ | | |TensorConstant{1.0} [#G]
+ | |TensorConstant{2} [#F]
+ |Elemwise{pow} [#H] ''
+   |x [#E]
+   |Elemwise{sub} [#I] ''
+     |TensorConstant{2} [#F]
+     |DimShuffle{} [#J] ''
+       |TensorConstant{1} [#K]
 
 >>> theano.printing.debugprint(gy, depth=2)  # doctest: +NORMALIZE_WHITESPACE
-Elemwise{mul} [@A] ''   
- |Elemwise{mul} [@B] ''   
- |Elemwise{pow} [@C] ''   
+Elemwise{mul} [#A] ''
+ |Elemwise{mul} [#B] ''
+ |Elemwise{pow} [#C] ''
+
 
 If the depth parameter is provided, it limits the number of levels that are
 shown.

doc/tutorial/debug_faq.txt

@@ -392,8 +392,8 @@ can be achieved as follows:
    :options: +NORMALIZE_WHITESPACE
 
    *** NaN detected ***
-   Elemwise{Composite{(log(i0) * i0)}} [@A] ''
-    |x [@B]
+   Elemwise{Composite{(log(i0) * i0)}} [#A] ''
+    |x [#B]
    Inputs : [array(0.0)]
    Outputs: [array(nan)]
 

doc/tutorial/printing_drawing.txt

@@ -67,39 +67,39 @@ Debug Print
 The pre-compilation graph:
 
 >>> theano.printing.debugprint(prediction) # doctest: +NORMALIZE_WHITESPACE
-Elemwise{gt,no_inplace} [@A] ''
- |Elemwise{true_div,no_inplace} [@B] ''
- | |DimShuffle{x} [@C] ''
- | | |TensorConstant{1} [@D]
- | |Elemwise{add,no_inplace} [@E] ''
- |   |DimShuffle{x} [@F] ''
- |   | |TensorConstant{1} [@D]
- |   |Elemwise{exp,no_inplace} [@G] ''
- |     |Elemwise{sub,no_inplace} [@H] ''
- |       |Elemwise{neg,no_inplace} [@I] ''
- |       | |dot [@J] ''
- |       |   |x [@K]
- |       |   |w [@L]
- |       |DimShuffle{x} [@M] ''
- |         |b [@N]
- |DimShuffle{x} [@O] ''
-   |TensorConstant{0.5} [@P]
+Elemwise{gt,no_inplace} [#A] ''
+ |Elemwise{true_div,no_inplace} [#B] ''
+ | |DimShuffle{x} [#C] ''
+ | | |TensorConstant{1} [#D]
+ | |Elemwise{add,no_inplace} [#E] ''
+ |   |DimShuffle{x} [#F] ''
+ |   | |TensorConstant{1} [#D]
+ |   |Elemwise{exp,no_inplace} [#G] ''
+ |     |Elemwise{sub,no_inplace} [#H] ''
+ |       |Elemwise{neg,no_inplace} [#I] ''
+ |       | |dot [#J] ''
+ |       |   |x [#K]
+ |       |   |w [#L]
+ |       |DimShuffle{x} [#M] ''
+ |         |b [#N]
+ |DimShuffle{x} [#O] ''
+   |TensorConstant{0.5} [#P]
 
 The post-compilation graph:
 
 >>> theano.printing.debugprint(predict)  # doctest: +NORMALIZE_WHITESPACE
-Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}} [@A] ''   4
- |CGemv{inplace} [@B] ''   3
- | |AllocEmpty{dtype='float64'} [@C] ''   2
- | | |Shape_i{0} [@D] ''   1
- | |   |x [@E]
- | |TensorConstant{1.0} [@F]
- | |x [@E]
- | |w [@G]
- | |TensorConstant{0.0} [@H]
- |InplaceDimShuffle{x} [@I] ''   0
- | |b [@J]
- |TensorConstant{(1,) of 0.5} [@K]
+Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}} [#A] ''   4
+ |CGemv{inplace} [#B] ''   3
+ | |AllocEmpty{dtype='float64'} [#C] ''   2
+ | | |Shape_i{0} [#D] ''   1
+ | |   |x [#E]
+ | |TensorConstant{1.0} [#F]
+ | |x [#E]
+ | |w [#G]
+ | |TensorConstant{0.0} [#H]
+ |InplaceDimShuffle{x} [#I] ''   0
+ | |b [#J]
+ |TensorConstant{(1,) of 0.5} [#K]
 
 
 Picture Printing of Graphs

doc/tutorial/shape_info.txt

@@ -24,11 +24,11 @@ Currently, information regarding shape is used in two ways in Theano:
 >>> x = theano.tensor.matrix('x')
 >>> f = theano.function([x], (x ** 2).shape)
 >>> theano.printing.debugprint(f) # doctest: +NORMALIZE_WHITESPACE
-MakeVector{dtype='int64'} [@A] ''   2
- |Shape_i{0} [@B] ''   1
- | |x [@C]
- |Shape_i{1} [@D] ''   0
-   |x [@C]
+MakeVector{dtype='int64'} [#A] ''   2
+ |Shape_i{0} [#B] ''   1
+ | |x [#C]
+ |Shape_i{1} [#D] ''   0
+   |x [#C]
     
 
 The output of this compiled function does not contain any multiplication
@@ -51,24 +51,24 @@ can lead to errors. Consider this example:
 
 >>> f = theano.function([x, y], z.shape)
 >>> theano.printing.debugprint(f) # doctest: +NORMALIZE_WHITESPACE
-MakeVector{dtype='int64'} [@A] ''   4
- |Elemwise{Add}[(0, 0)] [@B] ''   3
- | |Shape_i{0} [@C] ''   1
- | | |x [@D]
- | |Shape_i{0} [@E] ''   2
- |   |y [@F]
- |Shape_i{1} [@G] ''   0
-   |x [@D]
+MakeVector{dtype='int64'} [#A] ''   4
+ |Elemwise{Add}[(0, 0)] [#B] ''   3
+ | |Shape_i{0} [#C] ''   1
+ | | |x [#D]
+ | |Shape_i{0} [#E] ''   2
+ |   |y [#F]
+ |Shape_i{1} [#G] ''   0
+   |x [#D]
 
 >>> f(xv, yv) # DOES NOT RAISE AN ERROR AS SHOULD BE.
 array([8, 4])
 
 >>> f = theano.function([x,y], z)# Do not take the shape.
 >>> theano.printing.debugprint(f) # doctest: +NORMALIZE_WHITESPACE
-Join [@A] ''   0
- |TensorConstant{0} [@B]
- |x [@C]
- |y [@D]
+Join [#A] ''   0
+ |TensorConstant{0} [#B]
+ |x [#C]
+ |y [#D]
 
 >>> f(xv, yv)  # doctest: +ELLIPSIS
 Traceback (most recent call last):
@@ -121,8 +121,8 @@ upgrade.  Here is the current state of what can be done:
 >>> x_specify_shape = theano.tensor.specify_shape(x, (2, 2))
 >>> f = theano.function([x], (x_specify_shape ** 2).shape)
 >>> theano.printing.debugprint(f) # doctest: +NORMALIZE_WHITESPACE
-DeepCopyOp [@A] ''   0
- |TensorConstant{(2,) of 2} [@B]
+DeepCopyOp [#A] ''   0
+ |TensorConstant{(2,) of 2} [#B]
 
 Future Plans
 ============

theano/compile/debugmode.py

@@ -647,11 +647,11 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
         if obj in done:
             id_str = done[obj]
         elif ids == "id":
-            id_str = "[@%s]" % str(id(r))
+            id_str = "[#%s]" % str(id(r))
         elif ids == "int":
-            id_str = "[@%s]" % str(len(done))
+            id_str = "[#%s]" % str(len(done))
         elif ids == "CHAR":
-            id_str = "[@%s]" % char_from_number(len(done))
+            id_str = "[#%s]" % char_from_number(len(done))
         elif ids == "":
             id_str = ""
         done[obj] = id_str