Fix docstring warning when generating the doc.

theano/gradient.py

@@ -64,7 +64,7 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
     :param graph_inputs: variables considered to be constant
         (do not backpropagate through them)
 
-    :rtype: dictionary whose keys and values are of type `Variable`
+    :rtype: dictionary whose keys and values are of type Variable
 
     :return: mapping from each Variable encountered in the backward
         traversal to the gradient with respect to that Variable.
@@ -182,16 +182,15 @@ def Rop(f, wrt, eval_points):
     in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
     to `wrt` right muliplied by the eval points.
 
-    :type f: `Variable` or list of `Variable`s
+    :type f: Variable or list of Variables
              `f` stands for the output of the computational graph to which you
              want to apply the R operator
-    :type wrt: `Variable` or list of `Variables`s
+    :type wrt: Variable or list of `Variables`s
                variables for which you compute the R operator of the expression
                described by `f`
-    :type eval_points: `Variable` or list of `Variable`s
+    :type eval_points: Variable or list of Variables
                        evalutation points for each of the variables in `wrt`
-
-    :rtype: `Variable` or list/tuple of `Variable`s depending on type of f
+    :rtype: 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
@@ -295,16 +294,16 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
     in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
     to `wrt` left muliplied by the eval points.
 
-    :type f: `Variable` or list of `Variable`s
+    :type f: Variable or list of Variables
         `f` stands for the output of the computational graph to which you
         want to apply the L operator
-    :type wrt: `Variable` or list of `Variables`s
+    :type wrt: Variable or list of `Variables`s
         variables for which you compute the L operator of the expression
         described by `f`
-    :type eval_points: `Variable` or list of `Variable`s
+    :type eval_points: Variable or list of Variables
                         evalutation points for each of the variables in `f`
 
-    :rtype: `Variable` or list/tuple of `Variable`s depending on type of f
+    :rtype: 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
@@ -374,9 +373,9 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
 def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
          disconnected_inputs='raise'):
     """
-    :type cost: Scalar (0-dimensional) `Variable`
-    :type wrt: `Variable` or list of `Variable`s.
-    :type g_cost: Scalar `Variable`, or None
+    :type cost: Scalar (0-dimensional) Variable.
+    :type wrt: Variable or list of Variables.
+    :type g_cost: Scalar Variable, or None.
     :param g_cost: an expression for the gradient through cost.  The default is
         ``ones_like(cost)``.
     :param consider_constant: a list of expressions not to backpropagate
@@ -393,7 +392,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
         - 'warn': consider the gradient zero, and print a warning.
         - 'raise': raise an exception.
 
-    :rtype: `Variable` or list/tuple of `Variable`s (depending upon `wrt`)
+    :rtype: Variable or list/tuple of Variables (depending upon `wrt`)
 
     :return: symbolic expression of gradient of `cost` with respect to `wrt`.
              If an element of `wrt` is not differentiable with respect
@@ -841,8 +840,8 @@ verify_grad.E_grad = GradientError
 def jacobian(expression, wrt, consider_constant=None, warn_type=False,
              disconnected_inputs='raise'):
     """
-    :type expression: Vector (1-dimensional) `Variable`
-    :type wrt: 'Variable' or list of `Variables`s
+    :type expression: Vector (1-dimensional) Variable
+    :type wrt: Variable or list of Variables
 
     :param consider_constant: a list of expressions not to backpropagate
         through
@@ -858,7 +857,7 @@ def jacobian(expression, wrt, consider_constant=None, warn_type=False,
         - 'warn': consider the gradient zero, and print a warning.
         - 'raise': raise an exception.
 
-    :return: either a instance of `Variable` or list/tuple of `Variable`s
+    :return: either a instance of Variable or list/tuple of Variables
             (depending upon `wrt`) repesenting the jacobian of `expression`
             with respect to (elements of) `wrt`. If an element of `wrt` is not
             differentiable with respect to the output, then a zero
@@ -914,9 +913,9 @@ def jacobian(expression, wrt, consider_constant=None, warn_type=False,
 def hessian(cost, wrt, consider_constant=None, warn_type=False,
              disconnected_inputs='raise'):
     """
-    :type cost: Scalar (0-dimensional) `Variable`
+    :type cost: Scalar (0-dimensional) Variable.
     :type wrt: Vector (1-dimensional tensor) 'Variable' or list of
-            vectors (1-dimensional tensors) `Variable`s
+               vectors (1-dimensional tensors) Variables
 
     :param consider_constant: a list of expressions not to backpropagate
         through
@@ -932,7 +931,7 @@ def hessian(cost, wrt, consider_constant=None, warn_type=False,
         - 'warn': consider the gradient zero, and print a warning.
         - 'raise': raise an exception.
 
-    :return: either a instance of `Variable` or list/tuple of `Variable`s
+    :return: either a instance of Variable or list/tuple of Variables
             (depending upon `wrt`) repressenting the Hessian of the `cost`
             with respect to (elements of) `wrt`. If an element of `wrt` is not
             differentiable with respect to the output, then a zero