more to scalar dtype fix

theano/gradient.py

@@ -449,7 +449,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
         # 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
         # to cast it make sure it even has a dtype
-        if hasattr(g_cost.type, 'dtype') and cost.dtype not in tensor.discrete_dtypes:
+        if hasattr(g_cost.type, 'dtype') and cost.type.dtype not in tensor.discrete_dtypes:
             # Here we enforce the constraint that floating point variables have
             # the same dtype as their gradient.
             g_cost = g_cost.astype(cost.type.dtype)