merged theano.gradient & grad documentation, added a link

doc/library/gradient.txt

@@ -9,11 +9,11 @@
    :synopsis: low-level automatic differentiation
 .. moduleauthor:: LISA
 
-Symbolic gradient is usually computed from :func:`tensor.grad`, which offers a
+Symbolic gradient is usually computed from :func:`gradient.grad`, which offers a
 more convenient syntax for the common case of wanting the gradient in some
 expressions with respect to a scalar cost.  The :func:`grad_sources_inputs`
 function does the underlying work, and is more flexible, but is also more
-awkward to use when :func:`tensor.grad` can do the job.
+awkward to use when :func:`gradient.grad` can do the job.
 
 
 .. automodule:: theano.gradient

doc/library/tensor/basic.txt

@@ -1632,31 +1632,11 @@ Linear Algebra
 Gradient / Differentiation
 ==========================
 
-.. function:: grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False)
-
-    Return symbolic gradients for one or more variables with respect to some
-    cost.
-
-    For more information about how automatic differentiation works in Theano,
-    see :mod:`gradient`. For information on how to implement the gradient of
-    a certain Op, see :func:`grad`.
-
-    :type cost: 0-d tensor variable
-    :type wrt: tensor variable or list of tensor variables
-    :type g_cost: same as type of `cost`
-    :type consider_constant: list of variables
-    :type warn_type: bool
-
-    :param cost: a scalar with respect to which we are differentiating
-    :param wrt: term[s] for which we want gradients
-    :param g_cost: the gradient on the cost
-    :param consider_constant: variables whose gradients will be held at 0.
-    :param warn_type: True will trigger warnings via the logging module when
-       the gradient on an expression has a different type than the original
-       expression
-
-    :rtype: variable or list of variables (matching `wrt`)
-    :returns: gradients of the cost with respect to each of the `wrt` terms
+.. automodule:: theano.gradient
+    :members: grad
+
+See the :ref:`gradient <libdoc_gradient>` page for complete documentation
+of the gradient module.
 
 .. _R_op_list:
 

theano/gradient.py

@@ -356,9 +356,21 @@ def grad(cost, wrt, consider_constant=None,
          disconnected_inputs='raise', add_names=True,
          known_grads=None, return_disconnected='zero'):
     """
-    :type cost: Scalar (0-dimensional) Variable.
+    Return symbolic gradients for one or more variables with respect to some
+    cost.
+
+    For more information about how automatic differentiation works in Theano,
+    see :mod:`gradient`. For information on how to implement the gradient of
+    a certain Op, see :func:`grad`.
+
+    :type cost: Scalar (0-dimensional) tensor variable.
         May optionally be None if known_grads is provided.
-    :type wrt: Variable or list of Variables.
+    :param cost: a scalar with respect to which we are differentiating
+
+    :type wrt: Tensor variable or list of variables.
+    :param wrt: term[s] for which we want gradients
+
+    :type consider_constant: list of variables
     :param consider_constant: a list of expressions not to backpropagate
         through
 
@@ -389,9 +401,10 @@ def grad(cost, wrt, consider_constant=None,
                    None
         - 'Disconnected' : returns variables of type DisconnectedType
 
-    :rtype: Variable or list/tuple of Variables (depending upon `wrt`)
+    :rtype: variable or list/tuple of Variables (matching `wrt`)
 
-    :return: symbolic expression of gradient of `cost` with respect to `wrt`.
+    :return: symbolic expression of gradient of `cost` with respect to each
+             of the `wrt` terms.
              If an element of `wrt` is not differentiable with respect
              to the output, then a zero variable is returned.
              It returns an object of same type as `wrt`: a list/tuple