diff --git a/python/paddle/v2/fluid/backward.py b/python/paddle/v2/fluid/backward.py
index 88fe19da5e2c2df7f7eed7b26261ec155f0013f7..66a7f737574c438a2b945bd4a49d8317bd460c80 100644
--- a/python/paddle/v2/fluid/backward.py
+++ b/python/paddle/v2/fluid/backward.py
@@ -7,7 +7,7 @@ __all__ = ['append_backward']
 
 def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
     """
-    Traverse all ops in op_descs[begin_idx : end_idx], 
+    Traverse all ops in op_descs[begin_idx : end_idx],
     if any op has inputs/outputs named "old_name", rename it as 'new_name'
     """
     if begin_idx is None:
@@ -162,7 +162,7 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
             if core.grad_var_suffix() in arg and arg in no_grad_set:
                 to_insert.append((_create_op_desc_("fill_zeros_like", {
                     "X": [_strip_grad_suffix_(arg)]
-                }, {"Y": [arg]}, {}), idx))
+                }, {"Out": [arg]}, {}), idx))
 
     map(lambda p: op_descs.insert(p[1], p[0]), reversed(to_insert))
 
@@ -182,7 +182,7 @@ def _append_backward_ops_(target,
         target(Variable): the target variable of forward pass
         block(Block): the block where forward ops are
         target_block(Block): the block which is going to hold new generated grad ops
-        no_grad_dict(dict): 
+        no_grad_dict(dict):
             key(int)  block index
             val(set) a set of varibale names. These varibales have no gradient
         grad_to_var(dict)(output argument):
@@ -276,8 +276,8 @@ def append_backward(loss, parameter_list=None, no_grad_set=None):
         loss(Variable): The variable generated by cost function.
         parameter_list(list): Parameters that need to be updated by optimizer.
             If None, it means all parameters need to be updated.
-        no_grad_set(set): Variables that have no gradients in Block 0. 
-            If None, the set will be generated inside the function and 
+        no_grad_set(set): Variables that have no gradients in Block 0.
+            If None, the set will be generated inside the function and
             contains all variables with `step_gradient=True` from all blocks.
 
     Return: