remove g_cost

theano/gradient.py

@@ -349,16 +349,13 @@ def Lop(f, wrt, eval_points, consider_constant=None,
 # Gradient
 #########################
 
-def grad(cost, wrt, g_cost=None, consider_constant=None,
+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.
         May optionally be None if known_grads is provided.
     :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
         through
 
@@ -441,11 +438,11 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
     # build a dict mapping var to the gradient of cost with respect to var
     grad_dict = {}
 
-    # The gradient of the cost should default to 1 if the cost is of a
-    # continuous dtype (float, for the moment, as complex are unsupported),
-    # and should always be 0 if the cost is of discrete (integer) dtype.
+    # The gradient of the cost is 1 unless specified otherwise by known_grads.
     if cost is not None:
-        if g_cost is None:
+        if cost in known_grads:
+            g_cost = known_grads[cost]
+        else:
             g_cost = _float_ones_like(cost)
         # g_cost may be Disconnected or NullType. A creative use of the function,
         # sure, but nonetheless one we can and should support. So before we try
@@ -459,10 +456,6 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
         assert g_cost not in tensor.discrete_dtypes
 
         grad_dict[cost] = g_cost
-    else:
-        if g_cost is not None:
-            raise ValueError("No cost node was specified, but a gradient"
-                    " on it was.")
 
     if known_grads is not None:
         for var in known_grads:
@@ -737,14 +730,12 @@ def _populate_grad_dict(var_to_node_to_idx,
                 this variable to the variable's index in the apply
                 node's input list
 
-        grad_dict: a dictionary mapping variables to their gradients
-                   should be populated by grad function.
-
-                        grad should set gradients to DisconnectedType()() for
-                        variables to be considered constant, set the
-                        gradient for the cost variable to g_cost, etc.
-
-                        both should set the gradient for disconnected
+        grad_dict: A dictionary mapping variables to their gradients.
+                   Should be populated by grad function, which should:
+                       -Set the gradient with respect to the cost to 1
+                       -Load all gradients from known_grads, possibly overriding
+                        the cost
+                       -Set the gradient for disconnected
                         inputs to a variable with type DisconnectedType()
 
         wrt: the minimal set of variables that must be included in grad_dict