Reuse pre-created object

theano/gradient.py

@@ -146,6 +146,7 @@ class DisconnectedType(theano.gof.type.Type):
 
     def __str__(self):
         return 'DisconnectedType'
+disconnected_type = DisconnectedType()
 
 
 ########################
@@ -524,7 +525,7 @@ def grad(cost, wrt, consider_constant=None,
         if elem not in var_to_app_to_idx and elem is not cost \
                 and elem not in grad_dict:
             handle_disconnected(elem)
-            grad_dict[elem] = DisconnectedType()()
+            grad_dict[elem] = disconnected_type()
 
     cost_name = None
     if add_names and cost is not None:
@@ -978,7 +979,7 @@ def _populate_grad_dict(var_to_app_to_idx,
                 # are disconnected
                 # (The op's grad method could do this too, but this saves the
                 # implementer the trouble of worrying about this case)
-                input_grads = [DisconnectedType()() for ipt in inputs]
+                input_grads = [disconnected_type() for ipt in inputs]
             elif False not in only_connected_to_nan:
                 # All inputs are only connected to nan gradients, so we don't
                 # need to bother calling the grad method. We know the gradient
@@ -988,7 +989,7 @@ def _populate_grad_dict(var_to_app_to_idx,
                     if connected:
                         input_grads.append(NullType()())
                     else:
-                        input_grads.append(DisconnectedType()())
+                        input_grads.append(disconnected_type())
             else:
                 # At least one input of this op is connected to the cost so and
                 # not all output gradients are undefined so we must
@@ -1124,7 +1125,7 @@ def _populate_grad_dict(var_to_app_to_idx,
                     raise TypeError(('%s.grad returned None for' +
                              ' a gradient term, '
                             'this is prohibited. Instead of None,'
-                            'return zeros_like(input), DisconnectedType()(),'
+                            'return zeros_like(input), disconnected_type(),'
                             ' or a NullType variable such as those made with '
                             'the grad_undefined or grad_unimplemented helper '
                             'functions.') % node.op)
@@ -1258,14 +1259,14 @@ def _populate_grad_dict(var_to_app_to_idx,
                     # extraneous TensorConstant(0)
                     grad_dict[var] = reduce(lambda x, y: x + y, terms)
                 else:
-                    grad_dict[var] = DisconnectedType()()
+                    grad_dict[var] = disconnected_type()
 
                 if cost_name is not None and var.name is not None:
                     grad_dict[var].name = '(d%s/d%s)' % (cost_name, var.name)
             else:
                 # this variable isn't connected to the cost in the
                 # computational graph
-                grad_dict[var] = DisconnectedType()()
+                grad_dict[var] = disconnected_type()
         # end if cache miss
         return grad_dict[var]