Fix doc warning/error.

doc/library/gof/index.txt

@@ -13,7 +13,7 @@
 .. toctree::
     :maxdepth: 1
 
-    fgraph
+    fg
     toolbox
     type
 

doc/library/scan.txt

@@ -145,32 +145,32 @@ downcast** of the latter.
 .. code-block:: python
 
 
-import numpy as np
-import theano
-import theano.tensor as T
-
-up_to = T.iscalar("up_to")
-
-# define a named function, rather than using lambda
-def accumulate_by_adding(arange_val, sum_to_date):
-    return sum_to_date + arange_val
-seq = T.arange(up_to)
-
-# An unauthorized implicit downcast from the dtype of 'seq', to that of 
-# 'T.as_tensor_variable(0)' which is of dtype 'int8' by default would occur
-# if this instruction were to be used instead of the next one:
-# outputs_info = T.as_tensor_variable(0)
-
-outputs_info = T.as_tensor_variable(np.asarray(0, seq.dtype))
-scan_result, scan_updates = theano.scan(fn=accumulate_by_adding,
-                                        outputs_info=outputs_info,
-                                        sequences=seq)
-triangular_sequence = theano.function(inputs=[up_to], outputs=scan_result)
-
-# test
-some_num = 15
-print triangular_sequence(some_num)
-print [n * (n + 1) // 2 for n in xrange(some_num)]
+    import numpy as np
+    import theano
+    import theano.tensor as T
+
+    up_to = T.iscalar("up_to")
+
+    # define a named function, rather than using lambda
+    def accumulate_by_adding(arange_val, sum_to_date):
+        return sum_to_date + arange_val
+    seq = T.arange(up_to)
+
+    # An unauthorized implicit downcast from the dtype of 'seq', to that of 
+    # 'T.as_tensor_variable(0)' which is of dtype 'int8' by default would occur
+    # if this instruction were to be used instead of the next one:
+    # outputs_info = T.as_tensor_variable(0)
+
+    outputs_info = T.as_tensor_variable(np.asarray(0, seq.dtype))
+    scan_result, scan_updates = theano.scan(fn=accumulate_by_adding,
+                                            outputs_info=outputs_info,
+                                            sequences=seq)
+    triangular_sequence = theano.function(inputs=[up_to], outputs=scan_result)
+
+    # test
+    some_num = 15
+    print triangular_sequence(some_num)
+    print [n * (n + 1) // 2 for n in xrange(some_num)]
 
 
 Another simple example

doc/tutorial/adding.txt

@@ -186,6 +186,4 @@ Modify and execute this code to compute this expression: a**2 + b**2 + 2*a*b.
 
 .. TODO: repair this link
 
-:download:`Solution<../adding_solution_1.py>`
-
--------------------------------------------
+:download:`Solution<adding_solution_1.py>`

doc/tutorial/debug_faq.txt

@@ -145,7 +145,7 @@ The ``compute_test_value`` mechanism works as follows:
 
 
 "How do I Print an Intermediate Value in a Function/Method?"
-----------------------------------------------------------
+------------------------------------------------------------
 
 Theano provides a 'Print' op to do this.
 

doc/tutorial/examples.txt

@@ -314,7 +314,7 @@ Here's a brief example.  The setup code is:
 Here, 'rv_u' represents a random stream of 2x2 matrices of draws from a uniform
 distribution.  Likewise,  'rv_n' represents a random stream of 2x2 matrices of
 draws from a normal distribution.  The distributions that are implemented are
-defined in :class:`RandomStreams` and, at a lower level, in :ref:`raw_random<_libdoc_tensor_raw_random>`.
+defined in :class:`RandomStreams` and, at a lower level, in :ref:`raw_random<libdoc_tensor_raw_random>`.
 
   .. TODO: repair the latter reference on RandomStreams
 

doc/tutorial/faq.txt

@@ -38,7 +38,7 @@ We try to list in this `wiki page <https://github.com/Theano/Theano/wiki/Related
 
 
 "What are Theano's Limitations?"
--------------------------------
+--------------------------------
 
 Theano offers a good amount of flexibility, but has some limitations too.
 You must answer for yourself the following question: How can my algorithm be cleverly written 

doc/tutorial/gpu_data_convert.txt

@@ -74,7 +74,7 @@ CudaNdarrays. Here is an example from the file ``theano/misc/tests/test_pycuda_t
 
 
 Theano Op using a PyCUDA function
--------------------------------
+---------------------------------
 
 You can use a GPU function compiled with PyCUDA in a Theano op:
 

doc/tutorial/loop.txt

@@ -96,7 +96,4 @@ Modify and execute the polynomial example to have the reduction done by ``scan``
 
 .. TODO: repair this link as well as the code in the target file
 
-:download:`Solution<../loop_solution_1.py>`
-
-
--------------------------------------------
+:download:`Solution<loop_solution_1.py>`

doc/tutorial/modes.txt

@@ -129,7 +129,7 @@ as it will be useful later on.
 
 .. TODO: repair this link
 
-:download:`Solution<../modes_solution_1.py>`
+:download:`Solution<modes_solution_1.py>`
 
 -------------------------------------------
 

doc/tutorial/using_gpu.txt

@@ -391,7 +391,7 @@ What can be done to further increase the speed of the GPU version? Put your idea
 
 .. TODO: repair this link
 
-:download:`Solution<../using_gpu_solution_1.py>`
+:download:`Solution<using_gpu_solution_1.py>`
 
 -------------------------------------------
 
@@ -608,8 +608,6 @@ have to be jointly optimized explicitly in the code.)
 
 Modify and execute to support *stride* (i.e. so as not constrain the input to be *C-contiguous*).
 
--------------------------------------------
-
 
 
 

theano/printing.py

@@ -530,7 +530,7 @@ def pydotprint(fct, outfile=None,
     blue boxes are outputs variables of the graph
     grey boxes are variables that are not outputs and are not used
     red ellipses are transfers from/to the gpu (ops with names GpuFromHost,
-       HostFromGpu)
+    HostFromGpu)
 
     """
     if colorCodes is None: