started work on fixing gradient issues

theano/gradient.py

@@ -36,7 +36,14 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
     them)
 
     :rtype: dictionary whose keys and values are of type `Variable`
-    :return: mapping from each Variable encountered in the backward traversal to its gradient.
+    :return: mapping from each Variable encountered in the backward traversal to the gradient with respect to that Variable.
+
+    It is assumed that there is some objective J shared between all members of
+    sources, so that for each v, gradient-on-v is the gradient of J with respect to v
+
+
+
+
     """
     gmap = {}
     for (r, g_r) in sources:
@@ -78,7 +85,7 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
             else:
                 new_input_arg.append(input)
         input_arg = new_input_arg
-        
+
         #note that this function is not in a try-except block
         # the rationale:
         #  If the op implements grad, then any exception should be passed to the
@@ -93,8 +100,8 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
         g_inputs = op_grad
         assert isinstance(g_inputs, (list, tuple))
         if len(g_inputs) != len(node.inputs):
-            raise ValueError(_msg_badlen, 
-                    node.op, 
+            raise ValueError(_msg_badlen,
+                    node.op,
                     len(g_inputs),
                     len(node.inputs))
         for ii, (r, g_r) in enumerate(zip(node.inputs, g_inputs)):
@@ -106,7 +113,7 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
                         node.op, g_r_type, ii, r_type))
             if g_r and len(sources) == 1 and sources[0][0].name and r.name:
                 g_r.name = "(d%s/d%s)" % (sources[0][0].name, r.name)
-            if g_r is not None: 
+            if g_r is not None:
                 assert r is not None
                 if r in gmap:
                     gmap[r] = gmap[r] + g_r
@@ -125,3 +132,14 @@ def unimplemented_grad(op, x_pos, x):
     """
     msg = '%s.grad not implemented for input %i'%(op, x_pos)
     return Raise(msg=msg)(x)
+
+
+class GradientUndefined(Exception): pass
+
+def undefined_grad(op, x_pos, x):
+    msg = "Undefined gradient - do not use in computations"
+    exc = RuntimeError
+    return Raise(msg=msg, exc=exc)(x)
+
+def grad(self, inputs, out_storage):
+    return [g_x0, undefined_grad(self, 1, inputs[1])]