diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index 8437b2b21942ead544dab8636db1b355b7cf7bd5..02ca8a45a851d262eed6962a9a227b5009ef03a5 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -8,27 +8,6 @@ cc_library(paddle_fluid_api
 # Merge all modules into a simgle static library
 cc_library(paddle_fluid DEPS paddle_fluid_api ${FLUID_CORE_MODULES})
 
-# ptools
-# just for testing, we may need to change the storing format for inference_model
-# and move the dependent of pickle.
-# download from http://www.picklingtools.com/
-# build in the C++ sub-directory, using command
-#     make -f Makefile.Linux libptools.so
-set(PTOOLS_LIB)
-set(PTOOLS_ROOT $ENV{PTOOLS_ROOT} CACHE PATH "Folder contains PicklingTools")
-find_path(PTOOLS_INC_DIR chooseser.h PATHS ${PTOOLS_ROOT}/C++)
-find_library(PTOOLS_SHARED_LIB NAMES ptools PATHS ${PTOOLS_ROOT}/C++)
-if(PTOOLS_INC_DIR AND PTOOLS_SHARED_LIB)
-  add_definitions(-DPADDLE_USE_PTOOLS)
-  set(PTOOLS_LIB ptools)
-  message(STATUS "Found PicklingTools: ${PTOOLS_SHARED_LIB}")
-  add_library(${PTOOLS_LIB} SHARED IMPORTED GLOBAL)
-  set_property(TARGET ${PTOOLS_LIB} PROPERTY IMPORTED_LOCATION ${PTOOLS_SHARED_LIB})
-  include_directories(${PTOOLS_ROOT}/C++)
-  include_directories(${PTOOLS_ROOT}/C++/opencontainers_1_8_5/include)
-  add_definitions(-DOC_NEW_STYLE_INCLUDES) # used in ptools
-endif()
-
 add_executable(example example.cc)
 if(APPLE)
   set(OPTIONAL_LINK_FLAGS)
diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 4a8bc21b1cf0694dd27e7343432d37b94721fe1b..49001778808173b82865a4b6632a6b175ef96242 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -56,15 +56,6 @@ void InferenceEngine::LoadInferenceModel(
     const std::string& dirname,
     const std::vector<std::string>& feed_var_names,
     const std::vector<std::string>& fetch_var_names) {
-#ifdef PADDLE_USE_PTOOLS
-  std::string model_filename = dirname + "/__model__";
-  LOG(INFO) << "Using PicklingTools, loading model from " << model_filename;
-  Val v;
-  LoadValFromFile(model_filename.c_str(), v, SERIALIZE_P0);
-  std::string program_desc_str = v["program_desc_str"];
-  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
-// PicklingTools cannot parse the vector of strings correctly.
-#else
   std::string model_filename = dirname + "/__model__.dat";
   LOG(INFO) << "loading model from " << model_filename;
   std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
@@ -75,7 +66,7 @@ void InferenceEngine::LoadInferenceModel(
   LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
   inputfs.read(&program_desc_str[0], program_desc_str.size());
   inputfs.close();
-#endif
+
   program_ = new framework::ProgramDesc(program_desc_str);
   GenerateLoadProgram(dirname);
 
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 1b17afbe3fd305717097171024bf3c08c73662e3..57f051985ca0b91a071ad2ef27c48a00b0b615ef 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -198,11 +198,10 @@ def prepend_feed_ops(inference_program, feeded_var_names):
         name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
 
     for i, name in enumerate(feeded_var_names):
-        out = global_block.var(name)
         global_block.prepend_op(
             type='feed',
             inputs={'X': [feed_var]},
-            outputs={'Out': [out]},
+            outputs={'Out': [name]},
             attrs={'col': i})
 
 
@@ -269,11 +268,11 @@ def save_inference_model(dirname,
             "fetch_var_names": fetch_var_names
         }, f, -1)
 
-    # Save only programDesc of inference_program in binary format
-    # in another file: __model__.dat
     prepend_feed_ops(inference_program, feeded_var_names)
     append_fetch_ops(inference_program, fetch_var_names)
 
+    # Save only programDesc of inference_program in binary format
+    # in another file: __model__.dat
     with open(model_file_name + ".dat", "wb") as fp:
         fp.write(inference_program.desc.serialize_to_string())