fixed all warnings in doc. added the sphinx -m flag in docgen

doc/library/compile/shared.txt

@@ -5,7 +5,7 @@
 :mod:`shared` - defines theano.shared
 ===========================================
 
-.. module:: shared
+.. module:: theano.compile.sharedvalue
    :platform: Unix, Windows
    :synopsis: defines theano.shared and related classes
 .. moduleauthor:: LISA
@@ -47,7 +47,7 @@
 
         :type: class:`Container`
 
-.. autofunction:: theano.compile.sharedvalue.shared
+.. autofunction:: shared
 
 .. function:: shared_constructor(ctor)
 

doc/library/tensor/nnet/conv.txt

@@ -104,7 +104,7 @@ TODO: Give examples on how to use these things! They are pretty complicated.
       as a manual replacement for nnet.conv2d.
     - :func:`GpuCorrMM <theano.sandbox.cuda.blas.GpuCorrMM>`
       This is a GPU-only 2d correlation implementation taken from
-      `caffe <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu>`_
+      `caffe's CUDA implementation <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu>`_
       and also used by Torch. It does not flip the kernel.
 
       For each element in a batch, it first creates a
@@ -122,7 +122,7 @@ TODO: Give examples on how to use these things! They are pretty complicated.
       If using it, please see the warning about a bug in CUDA 5.0 to 6.0 below.
     - :func:`CorrMM <theano.tensor.nnet.corr.CorrMM>`
       This is a CPU-only 2d correlation implementation taken from
-      `caffe <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp>`_
+      `caffe's cpp implementation <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp>`_
       and also used by Torch. It does not flip the kernel. As it provides a gradient,
       you can use it as a replacement for nnet.conv2d. For convolutions done on
       CPU, nnet.conv2d will be replaced by CorrMM. To explicitly disable it, set

doc/library/tensor/shared_randomstreams.txt

@@ -21,7 +21,7 @@ object for each such variable, and draw from it as necessary.  We will call this
 random numbers a *random stream*.
 
 For an example of how to use random numbers, see
-:ref:`using_random_numbers`.
+:ref:`Using Random Numbers <using_random_numbers>`.
 
 
 Reference

doc/proposals/pfunc.txt

@@ -89,7 +89,7 @@ The proposal is for two new ways of creating a *shared* variable:
     def shared(value, name=None, strict=False, **kwargs):
         """Return a SharedVariable Variable, initialized with a copy or reference of `value`.
 
-        This function iterates over constructor functions (see `shared_constructor`) to find a
+        This function iterates over constructor functions (see :func:`shared_constructor`) to find a
         suitable SharedVariable subclass.
 
         :note:

doc/scripts/docgen.py

@@ -56,7 +56,7 @@ if __name__ == '__main__':
     def call_sphinx(builder, workdir, extraopts=None):
         import sphinx
         if extraopts is None:
-            extraopts = []  # '-W']
+            extraopts = ['-W']
         if not options['--cache'] and files is None:
             extraopts.append('-E')
         docpath = os.path.join(throot, 'doc')

doc/tutorial/profiling.txt

@@ -27,9 +27,7 @@ functions using either of the following two options:
       :attr:`profiling.n_ops` and :attr:`profiling.min_memory_size`
       to modify the quantify of information printed.
 
-2. Pass the argument :attr:`profile=True` to the function
-   :func:`theano.function <function.function>`. And then call
-   :attr:`f.profile.print_summary()` for a single function.
+2. Pass the argument :attr:`profile=True` to the function :func:`theano.function <function.function>`. And then call :attr:`f.profile.print_summary()` for a single function.
     - Use this option when you want to profile not all the
       functions but one or more specific function(s).
     - You can also combine the profile of many functions: 

theano/compile/sharedvalue.py

@@ -200,20 +200,25 @@ def shared_constructor(ctor, remove=False):
 
 
 def shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
-    """
-    Return a SharedVariable Variable, initialized with a copy or
+    """Return a SharedVariable Variable, initialized with a copy or
     reference of `value`.
 
-    This function iterates over
-    :ref:`constructor functions <shared_constructor>`
-    to find a suitable SharedVariable subclass.
-    The suitable one is the first constructor that accept the given value.
+    This function iterates over constructor functions to find a
+    suitable SharedVariable subclass.  The suitable one is the first
+    constructor that accept the given value.  See the documentation of
+    :func:`shared_constructor` for the definition of a contructor
+    function.
 
     This function is meant as a convenient default.  If you want to use a
     specific shared variable constructor, consider calling it directly.
 
     ``theano.shared`` is a shortcut to this function.
 
+    .. attribute:: constructors
+
+    A list of shared variable constructors that will be tried in reverse
+    order.
+
     Notes
     -----
     By passing kwargs, you effectively limit the set of potential constructors
@@ -229,11 +234,6 @@ def shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
     This parameter allows you to create for example a `row` or `column` 2d
     tensor.
 
-    .. attribute:: constructors
-
-    A list of shared variable constructors that will be tried in reverse
-    order.
-
     """
 
     try:

theano/sparse/basic.py

@@ -273,7 +273,7 @@ def sp_ones_like(x):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         The same as `x` with data changed for ones.
 
     """
@@ -293,7 +293,7 @@ def sp_zeros_like(x):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         The same as `x` with zero entries for all element.
 
     """
@@ -1765,7 +1765,7 @@ def row_scale(x, s):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         A sparse matrix in the same format as `x` whose each row has been
         multiplied by the corresponding element of `s`.
 
@@ -2070,7 +2070,7 @@ def clean(x):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         The same as `x` with indices sorted and zeros
         removed.
 
@@ -2166,7 +2166,7 @@ y
 
 Returns
 -------
-matrix
+A sparse matrix
     The sum of the two sparse matrices element wise.
 
 Notes
@@ -2270,7 +2270,7 @@ y
 
 Returns
 -------
-matrix
+A sparse matrix
     A sparse matrix containing the addition of the vector to
     the data of the sparse matrix.
 
@@ -2297,7 +2297,7 @@ def add(x, y):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         `x` + `y`
 
     Notes
@@ -2348,7 +2348,7 @@ def sub(x, y):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         `x` - `y`
 
     Notes
@@ -2547,7 +2547,7 @@ y
 
 Returns
 -------
-matrix
+A sparse matrix
     The product x * y element wise.
 
 Notes
@@ -2572,7 +2572,7 @@ def mul(x, y):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         `x` + `y`
 
     Notes
@@ -3720,7 +3720,7 @@ def structured_dot(x, y):
 
     Returns
     -------
-    matrix
+    A sparse matrix
         The dot product of `a` and `b`.
 
     Notes

theano/tensor/elemwise.py

@@ -461,7 +461,7 @@ class Elemwise(OpenMPOp):
     scalar.ScalarOp to get help about controlling the output type)
 
     Parameters
-    -----------
+    ----------
     scalar_op
         An instance of a subclass of scalar.ScalarOp which works uniquely
         on scalars.