diff --git a/x2paddle/decoder/tf_decoder.py b/x2paddle/decoder/tf_decoder.py
index e645ac1d2e57142f14eca649f02e86debe04ab67..6e1486ffc30230c055fa2cd428bd2eb6eb8a9e7b 100644
--- a/x2paddle/decoder/tf_decoder.py
+++ b/x2paddle/decoder/tf_decoder.py
@@ -488,96 +488,4 @@ class TFDecoder(object):
             return results[0].tolist()
         else:
             raise Exception("Couldn't infer a stable shape shape tensor value")
-            
-#     def infer_tensor(self, graph_node):
-#         if hasattr(graph_node, "index"):
-#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
-#         else:
-#             tensor_name = graph_node.layer.name + ":0"
-#         feed = dict()
-#         for input_name, info in self.inputs_info.items():
-#             (shape, dtype) = cp.deepcopy(info)
-#             input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0")
-#             if shape.count(-1) > 0:
-#                 shape[shape.index(-1)] = 2
-#             feed[input_tensor] = numpy.random.random_sample(shape)
-#         output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-#         return self.sess.run([output_tensor], feed)[0]
-
-#     def infer_shape_tensor(self, graph_node, out_shape=None):
-#         if hasattr(graph_node, "index"):
-#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
-#         else:
-#             tensor_name = graph_node.layer.name + ":0"
-#         feed = dict()
-#         batch_size = [2, 3, 5]
-#         results = list()
-#         for b in batch_size:
-#             for input_name, info in self.inputs_info.items():
-#                 (shape, dtype) = cp.deepcopy(info)
-#                 input_tensor = self.sess.graph.get_tensor_by_name(input_name +
-#                                                                   ":0")
-#                 if shape.count(-1) > 0:
-#                     shape[shape.index(-1)] = b
-#                 feed[input_tensor] = numpy.random.random_sample(shape)
-#             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-#             results.append(self.sess.run([output_tensor], feed)[0].flatten())
-
-#         compare01 = (results[0] == results[1])
-#         compare12 = (results[1] == results[2])
-
-#         if compare01.all() and compare12.all():
-#             return results[0].tolist()
-
-#         if (compare01 == compare12).all():
-#             index = numpy.argwhere(compare01 == False).flatten()
-#             if index.shape[0] != 1:
-#                 raise Exception("There's not only one unstable dimension")
-#             results[0][index[0]] = -1
-
-#             index = numpy.argwhere(results[0] < 0).flatten()
-#             if index.shape[0] > 2:
-#                 print("Warning: More than two dimension less than zero")
-#             if index.shape[0] == 2 and out_shape is not None:
-#                 if out_shape[index[1]] > 0:
-#                     results[0][index[1]] = out_shape[index[1]]
-#                 else:
-#                     results[0][index[0]] = out_shape[index[0]]
-#             return results[0].tolist()
-#         else:
-#             raise Exception("Couldn't infer a stable shape shape tensor value")
-
-#     def infer_tensor_shape(self, graph_node):
-#         if hasattr(graph_node, "index"):
-#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
-#         else:
-#             tensor_name = graph_node.layer.name + ":0"
-#         feed = dict()
-#         batch_size = [2, 3, 5]
-#         shapes = list()
-#         for b in batch_size:
-#             for input_name, info in self.inputs_info.items():
-#                 (shape, dtype) = cp.deepcopy(info)
-#                 input_tensor = self.sess.graph.get_tensor_by_name(input_name +
-#                                                                   ":0")
-#                 if shape.count(-1) > 0:
-#                     shape[shape.index(-1)] = b
-#                 feed[input_tensor] = numpy.random.random_sample(shape)
-#             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
-#             shape = self.sess.run([output_tensor], feed)[0].shape
-#             shapes.append(numpy.array(shape))
-
-#         compare01 = (shapes[0] == shapes[1])
-#         compare12 = (shapes[1] == shapes[2])
-
-#         if compare01.all() and compare12.all():
-#             return shape[0].tolist()
-
-#         if (compare01 == compare12).all():
-#             index = numpy.argwhere(compare01 == False).flatten()
-#             if index.shape[0] != 1:
-#                 raise Exception("There's not only one unstable dimension")
-#             if index[0] != 0:
-#                 raise Exception("Batch size not in the first dimension")
-#             shapes[0][0] = -1
-#             return shapes[0].tolist()
+            
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