diff --git a/ClassificationText/bert/keras_bert_embedding.py b/ClassificationText/bert/keras_bert_embedding.py
deleted file mode 100644
index a3f5230..0000000
--- a/ClassificationText/bert/keras_bert_embedding.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# -*- coding: UTF-8 -*-
-# !/usr/bin/python
-# @time     :2019/5/8 20:04
-# @author   :Mo
-# @function :embedding of bert keras
-
-from ClassificationText.bert.args import gpu_memory_fraction, max_seq_len, layer_indexes
-from conf.feature_config import config_name, ckpt_name, vocab_file
-from FeatureProject.bert.layers_keras import NonMaskingLayer
-from keras_bert import load_trained_model_from_checkpoint
-import keras.backend.tensorflow_backend as ktf_keras
-import keras.backend as k_keras
-from keras.models import Model
-from keras.layers import Add
-import tensorflow as tf
-import os
-
-import logging as logger
-# 全局使用，使其可以django、flask、tornado等调用
-graph = None
-model = None
-
-# gpu配置与使用率设置
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-config = tf.ConfigProto()
-config.gpu_options.per_process_gpu_memory_fraction = gpu_memory_fraction
-sess = tf.Session(config=config)
-ktf_keras.set_session(sess)
-
-class KerasBertEmbedding():
-    def __init__(self):
-        self.config_path, self.checkpoint_path, self.dict_path, self.max_seq_len = config_name, ckpt_name, vocab_file, max_seq_len
-
-    def bert_encode(self, layer_indexes=[12]):
-        # 全局使用，使其可以django、flask、tornado等调用
-        global graph
-        graph = tf.get_default_graph()
-        global model
-        model = load_trained_model_from_checkpoint(self.config_path, self.checkpoint_path,
-                                                   seq_len=self.max_seq_len)
-        print(model.output)
-        print(len(model.layers))
-        # lay = model.layers
-        #一共104个layer，其中前八层包括token,pos,embed等，
-        # 每8层（MultiHeadAttention,Dropout,Add,LayerNormalization）
-        # 一共12层
-        layer_dict = []
-        layer_0 = 7
-        for i in range(12):
-            layer_0 = layer_0 + 8
-            layer_dict.append(layer_0)
-        # 输出它本身
-        if len(layer_indexes) == 0:
-            encoder_layer = model.output
-        # 分类如果只有一层，就只取最后那一层的weight；取得不正确，就默认取最后一层
-        elif len(layer_indexes) == 1:
-            if layer_indexes[0] in [i+1 for i in range(23)]:
-                encoder_layer = model.get_layer(index=layer_dict[layer_indexes[0]]).output
-            else:
-                encoder_layer = model.get_layer(index=layer_dict[-1]).output
-        # 否则遍历需要取的层，把所有层的weight取出来并拼接起来shape:768*层数
-        else:
-            # layer_indexes must be [1,2,3,......12]
-            # all_layers = [model.get_layer(index=lay).output if lay is not 1 else model.get_layer(index=lay).output[0] for lay in layer_indexes]
-            all_layers = [model.get_layer(index=layer_dict[lay-1]).output if lay in [i+1 for i in range(23)]
-                          else model.get_layer(index=layer_dict[-1]).output  #如果给出不正确，就默认输出最后一层
-                          for lay in layer_indexes]
-            print(layer_indexes)
-            print(all_layers)
-            all_layers_select = []
-            for all_layers_one in all_layers:
-                all_layers_select.append(all_layers_one)
-            encoder_layer = Add()(all_layers_select)
-            print(encoder_layer.shape)
-        print("KerasBertEmbedding:")
-        print(encoder_layer.shape)
-        output_layer = NonMaskingLayer()(encoder_layer)
-        model = Model(model.inputs, output_layer)
-        # model.summary(120)
-        return model.inputs, model.output
-
-
-if __name__ == "__main__":
-    bert_vector = KerasBertEmbedding()
-    pooled = bert_vector.bert_encode()
-