cleanup of new features

theano/gradient.py

@@ -13,6 +13,7 @@ import warnings
 _logger = logging.getLogger('theano.gradient')
 
 import numpy  # for numeric_grad
+np = numpy
 
 import theano
 
@@ -551,7 +552,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
             grad_dict[elem] = DisconnectedType()()
 
     cost_name = None
-    if add_names:
+    if add_names and cost is not None:
         cost_name = cost.name
 
     # Make sure we didn't initialize the grad_dict with any ints
@@ -930,7 +931,7 @@ def _populate_grad_dict(var_to_node_to_idx,
                         # The only other valid thing it can be is 0
 
                         is_zero = _is_zero(term)
-                        assert is_zero in ['yes', 'no', 'maybe']:
+                        assert is_zero in ['yes', 'no', 'maybe']
                         if is_zero == 'maybe':
                             msg = "%s.grad returned %s of type %s for input"
                             msg += " %d. This input's only connections to "
@@ -1585,14 +1586,14 @@ def _is_zero(x):
     """
     if not hasattr(x, 'type'):
         return np.all(x == 0.)
-    if isinstance(term.type, NullType):
+    if isinstance(x.type, NullType):
         return 'no'
-    if isinstance(term.type, DisconnectedType):
+    if isinstance(x.type, DisconnectedType):
         return 'yes'
 
     no_constant_value = True
     try:
-        constant_value = theano.get_constant_value(term)
+        constant_value = theano.get_constant_value(x)
         no_constant_value = False
     except TypeError:
         pass
@@ -1601,4 +1602,6 @@ def _is_zero(x):
         return 'maybe'
 
     if constant_value != 0.:
-        return no
+        return 'no'
+
+    return 'yes'