From 8c59437612280738f59df859d9f512b318153d05 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 15 Nov 2016 12:35:36 +0800
Subject: [PATCH] change is_train to is_generating

---
 python/paddle/trainer_config_helpers/layers.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index f0757b9ce21..7cd290023ab 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -494,8 +494,7 @@ def scaling_projection(input, param_attr=None):
     :return: A ScalingProjection object
     :rtype: ScalingProjection
     """
-    proj = ScalingProjection(input_layer_name=input.name,
-                             **param_attr.attr)
+    proj = ScalingProjection(input_layer_name=input.name, **param_attr.attr)
     proj.origin = input
     return proj
 
@@ -2788,7 +2787,7 @@ def recurrent_group(step,
                     reverse=False,
                     name=None,
                     targetInlink=None,
-                    is_train=True):
+                    is_generating=False):
     """
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
@@ -2853,11 +2852,11 @@ def recurrent_group(step,
 
     :type targetInlink: LayerOutput|SubsequenceInput
 
-    :param is_train: recurrent_group is used for training (True) or generating (False).
-                     If is training, one of the input type must be LayerOutput; else, 
-                     none of input type should be LayerOutput.
+    :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_train: bool                 
+    : type is_generating: bool
     
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -2928,7 +2927,7 @@ def recurrent_group(step,
                 mix += identity_projection(mem)
             in_args.append(mem)
 
-    assert (is_train == has_LayerOutput)
+    assert (is_generating != has_LayerOutput)
 
     layer_outs = step(*in_args)
 
@@ -3225,7 +3224,7 @@ def beam_search(step,
         input=real_input,
         reverse=False,
         name=name,
-        is_train=False)
+        is_generating=True)
 
     return tmp
 
-- 
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