diff --git a/demo/ssd/README.md b/demo/ssd/README.md
index 0a6aafb2534b60c0910755e374f098fb03a0fbd4..2b3f5cbca98577fe3486d02bfbac68b7f7306bd5 100644
--- a/demo/ssd/README.md
+++ b/demo/ssd/README.md
@@ -27,18 +27,18 @@ $ pip install --upgrade paddlepaddle
 
 ## 命令行方式预测
 
-`infer.sh`给出了使用命令行调用Module预测的示例脚本
+`cli_demo.sh`给出了使用命令行调用Module预测的示例脚本
 通过以下命令试验下效果
 
 ```shell
-$ sh infer.sh
+$ sh cli_demo.sh
 ```
 
 ## 通过python API预测
 
-`infer_by_code.py`给出了使用python API调用Module预测的示例代码
+`ssd_demo.py`给出了使用python API调用Module预测的示例代码
 通过以下命令试验下效果
 
 ```shell
-python infer_by_code.py
+python ssd_demo.py
 ```
diff --git a/demo/ssd/infer.sh b/demo/ssd/cli_demo.sh
similarity index 100%
rename from demo/ssd/infer.sh
rename to demo/ssd/cli_demo.sh
diff --git a/demo/ssd/infer_by_code.py b/demo/ssd/infer_by_code.py
deleted file mode 100644
index 10e65bb22df899be67e65581ab0cefef0ddeb6e4..0000000000000000000000000000000000000000
--- a/demo/ssd/infer_by_code.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-import paddlehub as hub
-
-
-def infer_with_input_path():
-    # get ssd module
-    ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
-
-    test_img_path = os.path.join("test", "test_img_bird.jpg")
-
-    # get the input keys for signature 'object_detection'
-    data_format = ssd.processor.data_format(sign_name='object_detection')
-    key = list(data_format.keys())[0]
-
-    # set input dict
-    input_dict = {key: [test_img_path]}
-
-    # execute predict and print the result
-    results = ssd.object_detection(data=input_dict)
-    for result in results:
-        hub.logger.info(result)
-
-
-def infer_with_input_file():
-    # get ssd module
-    ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
-
-    # get the input keys for signature 'object_detection'
-    data_format = ssd.processor.data_format(sign_name='object_detection')
-    key = list(data_format.keys())[0]
-
-    # parse input file
-    test_file = os.path.join("test", "test.txt")
-    test_images = hub.io.parser.txt_parser.parse(test_file)
-
-    # set input dict
-    input_dict = {key: test_images}
-    results = ssd.object_detection(data=input_dict)
-    for result in results:
-        hub.logger.info(result)
-
-
-if __name__ == "__main__":
-    infer_with_input_file()
diff --git a/demo/ssd/ssd_demo.py b/demo/ssd/ssd_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a24751f9829d285c8242f4e4606144183eedfb2
--- /dev/null
+++ b/demo/ssd/ssd_demo.py
@@ -0,0 +1,19 @@
+import os
+import paddlehub as hub
+
+if __name__ == "__main__":
+    ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
+
+    test_img_path = os.path.join("test", "test_img_bird.jpg")
+
+    # get the input keys for signature 'object_detection'
+    data_format = ssd.processor.data_format(sign_name='object_detection')
+    key = list(data_format.keys())[0]
+
+    # set input dict
+    input_dict = {key: [test_img_path]}
+
+    # execute predict and print the result
+    results = ssd.object_detection(data=input_dict)
+    for result in results:
+        hub.logger.info(result)
diff --git a/demo/text-classification/run_classifier.sh b/demo/text-classification/run_classifier.sh
index 0b53a268c31929fd810138bf0fa36ab41469fc60..cb80a788c9d47caed011bca576512ecc9c4e8b2f 100644
--- a/demo/text-classification/run_classifier.sh
+++ b/demo/text-classification/run_classifier.sh
@@ -10,7 +10,7 @@ CKPT_DIR="./ckpt_${DATASET}"
 
 python -u text_classifier.py \
                    --batch_size=24 \
-                   --use_gpu=True \
+                   --use_gpu=False \
                    --dataset=${DATASET} \
                    --checkpoint_dir=${CKPT_DIR} \
                    --learning_rate=5e-5 \