diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 78aa0778f8d1dca9fae82f0411be5a00e636cbc9..f6e8819e0f86d8f6c36a72de2b3759dce28dd0f5 100755
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -3529,12 +3529,7 @@ def SubsequenceInput(input):
 
 
 @wrap_name_default("recurrent_group")
-def recurrent_group(step,
-                    input,
-                    reverse=False,
-                    name=None,
-                    targetInlink=None,
-                    is_generating=False):
+def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
     """
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
@@ -3600,21 +3595,12 @@ def recurrent_group(step,
 
     :type targetInlink: LayerOutput|SubsequenceInput
 
-    :param is_generating: If is generating, none of input type should be LayerOutput;
-                          else, for training or testing, one of the input type must
-                          be LayerOutput.
-
-    :type is_generating: bool
-
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
     model_type('recurrent_nn')
 
-    def is_single_input(x):
-        return isinstance(x, LayerOutput) or isinstance(x, StaticInput)
-
-    if is_single_input(input):
+    if isinstance(input, LayerOutput) or isinstance(input, StaticInput):
         input = [input]
     assert isinstance(input, collections.Sequence)
 
@@ -3628,13 +3614,8 @@ def recurrent_group(step,
         in_links=map(lambda x: x.name, in_links),
         seq_reversed=reverse)
     in_args = []
-    has_LayerOutput = False
     for each_input in input:
-        assert is_single_input(each_input)
-        if isinstance(each_input, LayerOutput):
-            in_args.append(each_input)
-            has_LayerOutput = True
-        else:  # StaticInput
+        if isinstance(each_input, StaticInput):  # StaticInput
             mem_name = "__%s_memory__" % each_input.input.name
             mem = memory(
                 name=None,
@@ -3642,8 +3623,8 @@ def recurrent_group(step,
                 boot_layer=each_input.input)
             mem.set_input(mem)
             in_args.append(mem)
-
-    assert (is_generating != has_LayerOutput)
+        else:
+            in_args.append(each_input)
 
     layer_outs = step(*in_args)
 
@@ -3869,6 +3850,7 @@ def beam_search(step,
     :type step: callable
     :param input: Input data for the recurrent unit, which should include the
                   previously generated words as a GeneratedInput object.
+                  In beam_search, none of the input's type should be LayerOutput.
     :type input: list
     :param bos_id: Index of the start symbol in the dictionary. The start symbol
                    is a special token for NLP task, which indicates the
@@ -3910,15 +3892,18 @@ def beam_search(step,
 
     real_input = []
     for i, each_input in enumerate(input):
-        assert isinstance(each_input, StaticInput) or isinstance(
-            each_input, BaseGeneratedInput)
+        assert not isinstance(each_input, LayerOutput), (
+            "in beam_search, "
+            "none of the input should has a type of LayerOutput.")
         if isinstance(each_input, BaseGeneratedInput):
-            assert generated_input_index == -1
+            assert generated_input_index == -1, ("recurrent_group accepts "
+                                                 "only one GeneratedInput.")
             generated_input_index = i
+
         else:
             real_input.append(each_input)
 
-    assert generated_input_index != -1
+    assert generated_input_index != -1, "No GeneratedInput is given."
 
     gipt = input[generated_input_index]
 
@@ -3942,14 +3927,8 @@ def beam_search(step,
         eos_layer(input=predict, eos_id=eos_id, name=eos_name)
         return predict
 
-    tmp = recurrent_group(
-        step=__real_step__,
-        input=real_input,
-        reverse=False,
-        name=name,
-        is_generating=True)
-
-    return tmp
+    return recurrent_group(
+        step=__real_step__, input=real_input, reverse=False, name=name)
 
 
 def __cost_input__(input, label, weight=None):
diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py
index 810bea913ec79b2df0eb63ed5a4fd411549ff2e9..396073236c347865be95a1a5a6641d7047c3b183 100755
--- a/python/paddle/trainer_config_helpers/networks.py
+++ b/python/paddle/trainer_config_helpers/networks.py
@@ -15,6 +15,7 @@
 
 """
 # from activations import *
+import pdb
 from activations import LinearActivation, ReluActivation, SoftmaxActivation, \
     IdentityActivation, TanhActivation, SequenceSoftmaxActivation
 from attrs import ExtraAttr
@@ -614,6 +615,7 @@ def simple_lstm(input,
 
 @wrap_name_default('lstm_unit')
 def lstmemory_unit(input,
+                   out_memory=None,
                    memory_boot=None,
                    name=None,
                    size=None,
@@ -694,7 +696,11 @@ def lstmemory_unit(input,
     if size is None:
         assert input.size % 4 == 0
         size = input.size / 4
-    out_mem = memory(name=name, size=size)
+    if out_memory is None:
+        out_mem = memory(name=name, size=size)
+    else:
+        out_mem = out_memory
+
     state_mem = memory(
         name="%s_state" % name, size=size, boot_layer=memory_boot)