Fixed the ticket about the grad method silently using 0 as the gradients wrt

theano/tensor/basic.py

@@ -4717,13 +4717,29 @@ def grad(cost, wrt, g_cost=None, consider_constant=[], warn_type=False):
             list(inputs) + list(consider_constant),
             warn_type=warn_type)
 
-    # Note that it is important to use `zeros_like` when there is no gradient,
-    # instead of returning a scalar constant equal to zero. Otherwise we lose
-    # the guarantee that the gradient has same shape as `wrt`.
-    if isinstance(wrt, (list, tuple)):
-        return [gmap.get(p, zeros_like(p)) for p in wrt]
+
+    # Note : If p is not in gmap there can be several reasons, among which
+    # is the fact that p might not be part of the computational graph. A
+    # simple example is that for a+b for e.g. a[0] is not part of the graph,
+    # so Theano does not know how to compute TT.grad(TT.sum(a+b), a[0])
+    # such subtle cases can be fixed by a more careful implementation of the
+    # gradient, but for now Theano needs to throw an exception, and make the
+    # user aware that it does not know how to compute that gradient
+    if not isinstance(wrt, (list, tuple)):
+        wrt = [wrt]
+    ret = []
+    for p in wrt:
+        if p not in gmap:
+            raise ValueError(("grad method was asked to compute the graident "
+                             "with respect to a variable that is not part of "
+                             "the computational graph of the cost"),p)
+        else:
+            ret.append(gmap[p])
+
+    if len(ret) == 1:
+        return ret[0]
     else:
-        return gmap.get(wrt, zeros_like(wrt))
+        return ret
 
 class numeric_grad:
     """WRITEME"""