diff --git a/README.md b/README.md
index 92751ce924f49579222c3d155e680607f1397480..3d8b9e600745f8f5529fe85f109a019c80d08b83 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,7 @@ To avoid the trouble of environment setup, [running in Docker container](#runnin
 
 ### Prerequisites
 - Python 2.7 only supported
-- PaddlePaddle 1.6.0 or later (please refer to the [Installation Guide](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html))
+- PaddlePaddle 1.8.0 or later (please refer to the [Installation Guide](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html))
 
 ### Setup
 - Make sure these libraries or tools installed: `pkg-config`, `flac`, `ogg`, `vorbis`, `boost` and `swig`, e.g. installing them via `apt-get`:
diff --git a/README_cn.md b/README_cn.md
index ad04b1659d0a15c26ba34e6c29c908c6fc77dd61..d408afe9219199bd8531de243e5383a2707555ce 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -25,7 +25,7 @@
 
 ### 前提
 - 只支持Python 2.7
-- PaddlePaddle 1.6.0 版本及以上（请参考[安装指南](https://www.paddlepaddle.org.cn/install/quick)）
+- PaddlePaddle 1.8.0 版本及以上（请参考[安装指南](https://www.paddlepaddle.org.cn/install/quick)）
 
 ### 安装
 - 请确保以下库或工具已安装完毕：`pkg-config`, `flac`, `ogg`, `vorbis`, `boost` 和 `swig`, 如可以通过`apt-get`安装：
diff --git a/model_utils/model.py b/model_utils/model.py
index fe1ae43d0cc4fc178e2c53b934505fcdf9eec9de..7cb26f39f888fa54a6ee38cf44079b9870d1f803 100644
--- a/model_utils/model.py
+++ b/model_utils/model.py
@@ -285,9 +285,8 @@ class DeepSpeech2Model(object):
                         learning_rate=learning_rate,
                         decay_steps=num_samples / batch_size / dev_count,
                         decay_rate=0.83,
-                        staircase=True))
-                fluid.clip.set_gradient_clip(
-                    clip=fluid.clip.GradientClipByGlobalNorm(
+                        staircase=True),
+                    grad_clip=fluid.clip.GradientClipByGlobalNorm(
                         clip_norm=gradient_clipping))
                 optimizer.minimize(loss=ctc_loss)