Merge pull request #1219 from nouiz/doc_syntax

doc/install.txt

@@ -657,8 +657,8 @@ Theano dependencies is easy, but be aware that it will take a long time
 Homebrew
 ~~~~~~~~
 
-There are some :ref:`instructions
-<https://github.com/samueljohn/homebrew-python>` by Samuel John on how to install
+There are some `instructions
+<https://github.com/samueljohn/homebrew-python>`__ by Samuel John on how to install
 Theano dependencies with Homebrew instead of MacPort.
 
 

doc/library/tensor/basic.txt

@@ -1229,6 +1229,7 @@ Linear Algebra
                  If an integer i, it is converted to an array containing
                  the last i dimensions of the first tensor and the first
                  i dimensions of the second tensor:
+
                      axes = [range(a.ndim - i, b.ndim), range(i)]
 
                  If an array, its two elements must contain compatible axes
@@ -1251,6 +1252,8 @@ Linear Algebra
     are compatible. The resulting tensor will have shape (2, 5, 6) -- the
     dimensions that are not being summed:
 
+    .. code-block:: python
+
         a = np.random.random((2,3,4))
         b = np.random.random((5,6,4,3))
 
@@ -1284,6 +1287,8 @@ Linear Algebra
     In an extreme case, no axes may be specified. The resulting tensor
     will have shape equal to the concatenation of the shapes of a and b:
 
+    .. code-block:: python
+
         c = np.tensordot(a, b, 0)
         print(a.shape) #(2,3,4)
         print(b.shape) #(5,6,4,3)

doc/library/tensor/nnet/conv.txt

@@ -7,8 +7,11 @@
 .. note::
 
     Two similar implementation exists for conv2d:
-    :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
-    :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`. The former implements a traditional
+
+        :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
+        :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
+
+    The former implements a traditional
     2D convolution, while the latter implements the convolutional layers
     present in convolutional neural networks (where filters are 3D and pool
     over several input channels).

doc/library/tensor/signal/conv.txt

@@ -7,8 +7,11 @@
 .. note::
 
     Two similar implementation exists for conv2d:
-    :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
-    :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>. The former implements a traditional
+
+        :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
+        :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
+
+    The former implements a traditional
     2D convolution, while the latter implements the convolutional layers
     present in convolutional neural networks (where filters are 3D and pool
     over several input channels).

theano/sandbox/cuda/basic_ops.py

@@ -2455,7 +2455,7 @@ class GpuIncSubtensor(tensor.IncSubtensor, GpuOp):
 
             :return: C code expression to make a copy of x
 
-            Base class uses PyArrayObject *, subclasses may override for
+            Base class uses `PyArrayObject *`, subclasses may override for
             different types of arrays.
         """
         return """(CudaNdarray*) CudaNdarray_Copy(%(x)s)""" % locals()

theano/sandbox/linalg/ops.py

@@ -433,16 +433,14 @@ class CholeskyGrad(Op):
         return Apply(self, [x, l, dz], [x.type()])
 
     def perform(self, node, inputs, outputs):
-        """
-        Implements the "reverse-mode" gradient for the Cholesky factorization
-        of a positive-definite matrix.
+        """Implements the "reverse-mode" gradient [1]_ for the
+        Cholesky factorization of a positive-definite matrix.
 
-        References
-        ----------
         .. [1] S. P. Smith. "Differentiation of the Cholesky Algorithm".
                Journal of Computational and Graphical Statistics,
                Vol. 4, No. 2 (Jun.,1995), pp. 134-147
                http://www.jstor.org/stable/1390762
+
         """
         x = inputs[0]
         L = inputs[1]