Merge pull request #5331 from gvtulder/f-sphinx-latex_font_size

doc/conf.py

@@ -222,11 +222,16 @@ def linkcode_resolve(domain, info):
 # Options for LaTeX output
 # ------------------------
 
-# The paper size ('letter' or 'a4').
-#latex_paper_size = 'letter'
+latex_elements = {
+    # The paper size ('letter' or 'a4').
+    #latex_paper_size = 'letter',
 
-# The font size ('10pt', '11pt' or '12pt').
-latex_font_size = '11pt'
+    # The font size ('10pt', '11pt' or '12pt').
+    'pointsize': '11pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #latex_preamble = '',
+}
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title, author, document class
@@ -245,9 +250,6 @@ latex_logo = 'images/theano_logo_allblue_200x46.png'
 # not chapters.
 #latex_use_parts = False
 
-# Additional stuff for the LaTeX preamble.
-#latex_preamble = ''
-
 # Documents to append as an appendix to all manuals.
 #latex_appendices = []
 

doc/library/tensor/basic.txt

@@ -1582,7 +1582,7 @@ Linear Algebra
     :param Y: right term
     :type X: symbolic tensor
     :type Y: symbolic tensor
-    :rtype: symbolic matrix or vector
+    :rtype: `symbolic matrix or vector`
     :return: the inner product of `X` and `Y`.
 
 .. function:: outer(X, Y)

theano/gpuarray/dnn.py

@@ -945,6 +945,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
         and 'float64'. Default is the value of
         :attr:`config.dnn.conv.precision`.
 
+
     .. warning:: The cuDNN library only works with GPUs that have a compute
         capability of 3.0 or higer. This means that older GPUs will not
         work with this Op.
@@ -1064,6 +1065,7 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),
         and 'float64'. Default is the value of
         :attr:`config.dnn.conv.precision`.
 
+
     .. warning:: The cuDNN library only works with GPUs that have a compute
         capability of 3.0 or higer. This means that older GPUs will not
         work with this Op.
@@ -1497,6 +1499,7 @@ def dnn_pool(img, ws, stride=None, mode='max', pad=None):
         (padX, padY) or (padX, padY, padZ)
         default: (0, 0) or (0, 0, 0)
 
+
     .. warning:: The cuDNN library only works with GPU that have a compute
         capability of 3.0 or higer.  This means that older GPU will not
         work with this Op.

theano/gradient.py

@@ -173,7 +173,7 @@ def Rop(f, wrt, eval_points):
                described by `f`
     :type eval_points: Variable or list of Variables
                        evalutation points for each of the variables in `wrt`
-    :rtype: Variable or list/tuple of Variables depending on type of f
+    :rtype: :class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
     :return: symbolic expression such that
         R_op[i] = sum_j ( d f[i] / d wrt[j]) eval_point[j]
         where the indices in that expression are magic multidimensional
@@ -320,7 +320,7 @@ def Lop(f, wrt, eval_points, consider_constant=None,
     :type eval_points: Variable or list of Variables
                         evalutation points for each of the variables in `f`
 
-    :rtype: Variable or list/tuple of Variables depending on type of f
+    :rtype: :class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
     :return: symbolic expression such that
         L_op[i] = sum_i ( d f[i] / d wrt[j]) eval_point[i]
         where the indices in that expression are magic multidimensional
@@ -372,10 +372,10 @@ def grad(cost, wrt, consider_constant=None,
 
     Parameters
     ----------
-    cost : scalar (0-dimensional) tensor variable or None
+    cost : :class:`~theano.gof.Variable` scalar (0-dimensional) tensor variable or None
         Value with respect to which we are differentiating.  May be
         `None` if known_grads is provided.
-    wrt : variable or list of variables
+    wrt : :class:`~theano.gof.Variable` or list of Variables
         term[s] for which we want gradients
     consider_constant : list of variables
         expressions not to backpropagate through
@@ -646,7 +646,7 @@ def subgraph_grad(wrt, end, start=None, cost=None, details=False):
       to the variables in `end` (they are used as known_grad in
       theano.grad).
 
-    :type cost: scalar (0-dimensional) variable
+    :type cost: :class:`~theano.gof.Variable` scalar (0-dimensional) variable
     :param cost:
       Additional costs for which to compute the gradients.  For
       example, these could be weight decay, an l1 constraint, MSE,

theano/printing.py

@@ -60,7 +60,7 @@ def debugprint(obj, depth=-1, print_type=False,
                used_ids=None):
     """Print a computation graph as text to stdout or a file.
 
-    :type obj: Variable, Apply, or Function instance
+    :type obj: :class:`~theano.gof.Variable`, Apply, or Function instance
     :param obj: symbolic thing to print
     :type depth: integer
     :param depth: print graph to this depth (-1 for unlimited)

theano/sandbox/cuda/dnn.py

@@ -2029,6 +2029,7 @@ def dnn_pool(img, ws, stride=None, mode='max', pad=None):
         pad_d is the number of zero-valued pixels added to each of the front
         and back borders (3D pooling only).
 
+
     .. warning:: The cuDNN library only works with GPU that have a compute
       capability of 3.0 or higer.  This means that older GPU will not
       work with this Op.

theano/tensor/extra_ops.py

@@ -358,6 +358,7 @@ def cumsum(x, axis=None):
         The axis along which the cumulative sum is computed.
         The default (None) is to compute the cumsum over the flattened array.
 
+
     .. versionadded:: 0.7
 
     """
@@ -483,6 +484,7 @@ def cumprod(x, axis=None):
         The axis along which the cumulative product is computed.
         The default (None) is to compute the cumprod over the flattened array.
 
+
     .. versionadded:: 0.7
 
     """
@@ -554,6 +556,7 @@ def diff(x, n=1, axis=-1):
     axis
         The axis along which the difference is taken, default is the last axis.
 
+
     .. versionadded:: 0.6
 
     """
@@ -582,6 +585,7 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
         every input x is nonnegative.
         Optional.
 
+
     .. versionadded:: 0.6
 
     """
@@ -788,7 +792,8 @@ def repeat(x, repeats, axis=None):
     ----------
     x
         Input data, tensor variable.
-    repeats : int, scalar or tensor variable
+    repeats
+        int, scalar or tensor variable
     axis : int, optional
 
     See Also

theano/tensor/nnet/abstract_conv.py

@@ -864,7 +864,7 @@ def bilinear_upsampling(input,
         mini-batch of feature map stacks, of shape (batch size,
         input channels, input rows, input columns) that will be upsampled.
 
-    ratio: int or Constant or Scalar Tensor of int* dtype
+    ratio: `int or Constant or Scalar Tensor of int* dtype`
         the ratio by which the input is upsampled in the 2D space (row and
         col size).
 

theano/tensor/nnet/nnet.py

@@ -2181,7 +2181,7 @@ def relu(x, alpha=0):
     ----------
     x : symbolic tensor
         Tensor to compute the activation function for.
-    alpha : scalar or tensor, optional
+    alpha : `scalar or tensor, optional`
         Slope for negative input, usually between 0 and 1. The default value
         of 0 will lead to the standard rectifier, 1 will lead to
         a linear activation function, and any value in between will give a

theano/tensor/slinalg.py

@@ -278,15 +278,15 @@ Note
 
 Parameters
 ----------
-a : (M, M) symbolix matrix
+a : `(M, M) symbolix matrix`
     A square matrix
-b : (M,) or (M, N) symbolic vector or matrix
+b : `(M,) or (M, N) symbolic vector or matrix`
     Right hand side matrix in ``a x = b``
 
 
 Returns
 -------
-x : (M, ) or (M, N) symbolic vector or matrix
+x : `(M, ) or (M, N) symbolic vector or matrix`
     x will have the same shape as b
 """
 # lower and upper triangular solves

theano/tensor/var.py

@@ -314,6 +314,7 @@ class _tensor_py_operators(object):
             The length of the shape. Passing None here means for
             Theano to try and guess the length of `shape`.
 
+
         .. warning:: This has a different signature than numpy's
                      ndarray.reshape!
                      In numpy you do not need to wrap the shape arguments