diff --git a/.circleci/config.yml b/.circleci/config.yml
index 52fd04bf2..b396ce9b3 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -4,6 +4,7 @@ jobs:
 
   ###################################################################################
   #  TEST BUILDS with TensorLayer installed from Source - NOT PUSHED TO DOCKER HUB  #
+  #  tensorlayer@gmail.com                                                          #
   ###################################################################################
 
   test_sources_py2_cpu:
@@ -481,13 +482,11 @@ jobs:
           command: |
             echo "start tag workflow"
 
-###################################################################################
 ###################################################################################
 ###################################################################################
 #                               CircleCI WORKFLOWS                                #
 ###################################################################################
 ###################################################################################
-###################################################################################
 
 workflows:
   version: 2
diff --git a/.codacy.yaml b/.codacy.yaml
index 14735f068..6e3f3459a 100644
--- a/.codacy.yaml
+++ b/.codacy.yaml
@@ -2,7 +2,7 @@
 ---
 engines:
   bandit:
-    enabled: false # FIXME: make it work
+    enabled: false
 exclude_paths:
 - scripts/*
 - setup.py
diff --git a/.dockerignore b/.dockerignore
index 6b8710a71..0e8637283 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1 +1,2 @@
+# dockerignore
 .git
diff --git a/.github/changelog.yml b/.github/changelog.yml
index 8c22d177b..32d77a009 100644
--- a/.github/changelog.yml
+++ b/.github/changelog.yml
@@ -18,6 +18,8 @@ include:
   - requirements_dev.txt
   - requirements_tf_cpu.txt
   - requirements_tf_gpu.txt
+  - requirements_doc.txt
+  - requirements_test.txt
 
   # Configuration Files
   - .travis.yml
diff --git a/.gitignore b/.gitignore
index 4a6f60ff3..1d1e01d95 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,7 +26,6 @@ wheels/
 MANIFEST
 *~
 
-# PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
diff --git a/.pyup.yml b/.pyup.yml
index 6c969209a..35ded98b4 100644
--- a/.pyup.yml
+++ b/.pyup.yml
@@ -57,11 +57,6 @@ requirements:
     # Requirements for running unittests
     - requirements/requirements_test.txt
 
-# add a label to pull requests, default is not set
-# requires private repo permissions, even on public repos
-# default: empty
-#label_prs: update
-
 # configure the branch prefix the bot is using
 # default: pyup-
 branch_prefix: pyup-
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 075267529..966bc13c5 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -1,7 +1,7 @@
 # https://docs.readthedocs.io/en/latest/yaml-config.html
 
 build:
-  image: latest  # For python 3.6
+  image: latest
 
 formats:
     - epub
diff --git a/.travis.yml b/.travis.yml
index 2bf6a1e54..79445e638 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,7 +17,7 @@ branches:
     - /^\d+\.\d+(\.\d+)?(\S*)?$/
 
 python:
-  - "3.6"
+#  - "3.7"
   - "3.5"
 #  - "2.7"  # TensorLayer 2.0 does not support python2 now
 
@@ -26,7 +26,7 @@ env:
   # Backward Compatibility in insured for release less than 1 year old.
   # https://pypi.org/project/tensorflow/#history
   matrix:
-    - _TF_VERSION=2.0.0b1
+    - _TF_VERSION=2.0.0-rc1
 #     - _TF_VERSION=1.12.0 # Remove on Oct 22, 2019
 #     - _TF_VERSION=1.11.0 # Remove on Sep 28, 2019
 #     - _TF_VERSION=1.10.1 # Remove on Aug 24, 2019
@@ -63,7 +63,8 @@ matrix:
 install:
   - |
     if [[ -v _DOC_AND_YAPF_TEST ]]; then
-        pip install tensorflow==2.0.0b1
+        pip install tensorflow==2.0.0-rc1
+        pip install opencv-python
         pip install yapf
         pip install -e .[doc]
     else
@@ -101,7 +102,7 @@ deploy:
   on:
     tags: true
     python: '3.6'
-    condition: '$_TF_VERSION = 2.0.0b1'
+    condition: '$_TF_VERSION = 2.0.0-rc1'
 #     condition: '$_TF_VERSION = 1.11.0'
 
 # Documentation: https://docs.travis-ci.com/user/deployment/releases/
@@ -115,5 +116,5 @@ deploy:
   on:
     tags: true
     python: '3.6'
-    condition: '$_TF_VERSION = 2.0.0b1'
+    condition: '$_TF_VERSION = 2.0.0-rc1'
 #     condition: '$_TF_VERSION = 1.11.0'
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 19ac5ab5f..1d82838b8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -67,7 +67,6 @@ To release a new version, please update the changelog as followed:
 
 <!-- YOU CAN EDIT FROM HERE -->
 
-
 ## [Unreleased]
 
 ### Added
@@ -86,21 +85,149 @@ To release a new version, please update the changelog as followed:
 
 ### Contributors
 
+## [2.2.4] - 2020-12-10
+
+TensorLayer 2.2.4 is a maintenance release.
+
+### Added
+
+### Changed
+
+### Dependencies Update
+
+### Deprecated
+
+### Fixed
+
+- Fix batchnorm(#1104)
+- Fix recurrent(#1106)
+
+### Removed
+
+### Security
+
+### Contributors
+- @zsdonghao
+- @Laicheng0830(#1104)
+- @Thinkre(#1106)
+
+## [2.2.3] - 2020-06-18
 
-## [2.2.1]
+TensorLayer 2.2.3 is a maintenance release.
+It contains numerous bug fixes.
+
+### Added
+
+### Changed
+
+### Dependencies Update
+
+### Deprecated
+
+### Fixed
+
+- Fix VGG. (#1078, 1079, 1089)
+- Fix norm layer. (#1080)
+- Fix DeCov2d layer. (#1081)
+- Fix ModelLayer and LayerList doc. (#1083)
+- Fix bug in SAC. (#1085)
+- Fix refactoring: Deduplication. (#1086)
+- Fix maxpool, batchnorm Data format fixed, vgg forward. (#1089)
+- Fix package info. (#1090)
+
+### Removed
+
+### Security
+
+### Contributors
+- @zsdonghao
+- @tiancheng2000 (#1078 #1079 #1080 #1081)
+- @ChrisWu1997 (#1083)
+- @quantumiracle (#1085)
+- @marload (#1086)
+- @Gyx-One (#1089)
+- @Laicheng0830 (#1090)
+
+## [2.2.2] - 2020-04-26
+
+TensorLayer 2.2.2 is a maintenance release.
+
+### Added
+
+- Reinforcement learning(#1065)
+- Mish activation(#1068)
+
+### Changed
+
+### Dependencies Update
+
+### Deprecated
+
+### Fixed
+
+- Fix README.
+- Fix package info.
+
+### Removed
+
+### Security
+
+### Contributors
+
+- @zsdonghao
+- @quantumiracle(1065)
+- @Laicheng0830(#1068)
+
+## [2.2.1] - 2020-01-14
+
+TensorLayer 2.2.1 is a maintenance release.
+It contains numerous bug fixes.
+
+### Added
+
+### Changed
+
+### Dependencies Update
+
+### Deprecated
+
+### Fixed
+
+- Fix README. (#1044)
+- Fix package info. (#1046)
+- Fix build test (Using YAPF 0.29) (#1057)
+
+### Removed
+
+### Security
+
+### Contributors
+
+- @luomai (#1044, #1046, #1057)
+
+## [2.2.0] - 2019-09-13
+
+TensorLayer 2.2.0 is a maintenance release.
+It contains numerous API improvement and bug fixes.
+This release is compatible with TensorFlow 2 RC1.
 
 ### Added
 - Support nested layer customization (#PR 1015)
 - Support string dtype in InputLayer (#PR 1017)
 - Support Dynamic RNN in RNN (#PR 1023)
 - Add ResNet50 static model (#PR 1030)
+- Add performance test code in static model (#PR 1041)
 
 ### Changed
 
 - `SpatialTransform2dAffine` auto `in_channels`
-- support TensorFlow 2.0.0-beta1
+- support TensorFlow 2.0.0-rc1
 - Update model weights property, now returns its copy (#PR 1010)
 
+### Fixed
+- RNN updates: remove warnings, fix if seq_len=0, unitest (#PR 1033)
+- BN updates: fix BatchNorm1d for 2D data, refactored (#PR 1040)
+
 ### Dependencies Update
 
 ### Deprecated
@@ -112,7 +239,9 @@ To release a new version, please update the changelog as followed:
 - Remove `private_method` decorator (#PR 1025)
 - Copy original model's `trainable_weights` and `nontrainable_weights` when initializing `ModelLayer` (#PR 1026)
 - Copy original model's `trainable_weights` and `nontrainable_weights` when initializing `LayerList` (#PR 1029)
-- remove redundant parts in `model.all_layers` (#PR 1029)
+- Remove redundant parts in `model.all_layers` (#PR 1029)
+- Replace `tf.image.resize_image_with_crop_or_pad` with `tf.image.resize_with_crop_or_pad` (#PR 1032)
+- Fix a bug in `ResNet50` static model (#PR 1041)
 
 ### Removed
 
@@ -121,8 +250,9 @@ To release a new version, please update the changelog as followed:
 ### Contributors
 
 - @zsdonghao
-- @ChrisWu1997: #1010 #1015 #1025 #1030
-- @warshallrho: #1017 #1021 #1026 #1029
+- @luomai
+- @ChrisWu1997: #1010 #1015 #1025 #1030 #1040
+- @warshallrho: #1017 #1021 #1026 #1029 #1032 #1041
 - @ArnoldLIULJ: #1023
 - @JingqingZ: #1023
 
@@ -133,7 +263,7 @@ To release a new version, please update the changelog as followed:
 - Replace tf.nn.func with tf.nn.func.\_\_name\_\_ in model config. (PR #994)
 - Add Reinforcement learning tutorials. (PR #995)
 - Add RNN layers with simple rnn cell, GRU cell, LSTM cell. (PR #998)
-- Update Seq2seq (#998) 
+- Update Seq2seq (#998)
 - Add Seq2seqLuongAttention model (#998)
 
 ### Fixed
@@ -196,15 +326,12 @@ A maintain release.
 - @warshallrho: #PR966
 - @zsdonghao: #931
 - @yd-yin: #963
-<<<<<<< HEAD
 - @Tokarev-TT-33: # 995
 - @initial-h: # 995
 - @quantumiracle: #995
 - @Officium: #995
-=======
 - @1FengL: #958
 - @dvklopfenstein: #971
->>>>>>> 560dbb8a17963023a3b1d59a79e1c2752530114a
 
 
 ## [2.0.0] - 2019-05-04
@@ -557,12 +684,16 @@ To many PR for this update, please check [here](https://github.com/tensorlayer/t
 @zsdonghao @luomai @DEKHTIARJonathan
 
 [Unreleased]: https://github.com/tensorlayer/tensorlayer/compare/2.0....master
-[2.1.1]: https://github.com/tensorlayer/tensorlayer/compare/2.1.1...2.1.1
-[2.1.0]: https://github.com/tensorlayer/tensorlayer/compare/2.1.0...2.1.0
-[2.0.2]: https://github.com/tensorlayer/tensorlayer/compare/2.0.2...2.0.2
-[2.0.1]: https://github.com/tensorlayer/tensorlayer/compare/2.0.1...2.0.1
-[2.0.0]: https://github.com/tensorlayer/tensorlayer/compare/2.0.0...2.0.0
-[1.11.1]: https://github.com/tensorlayer/tensorlayer/compare/1.11.0...1.11.0
+[2.2.4]: https://github.com/tensorlayer/tensorlayer/compare/2.2.3...2.2.4
+[2.2.3]: https://github.com/tensorlayer/tensorlayer/compare/2.2.2...2.2.3
+[2.2.2]: https://github.com/tensorlayer/tensorlayer/compare/2.2.1...2.2.2
+[2.2.1]: https://github.com/tensorlayer/tensorlayer/compare/2.2.0...2.2.1
+[2.2.0]: https://github.com/tensorlayer/tensorlayer/compare/2.1.0...2.2.0
+[2.1.0]: https://github.com/tensorlayer/tensorlayer/compare/2.0.2...2.1.0
+[2.0.2]: https://github.com/tensorlayer/tensorlayer/compare/2.0.1...2.0.2
+[2.0.1]: https://github.com/tensorlayer/tensorlayer/compare/2.0.0...2.0.1
+[2.0.0]: https://github.com/tensorlayer/tensorlayer/compare/1.11.1...2.0.0
+[1.11.1]: https://github.com/tensorlayer/tensorlayer/compare/1.11.0...1.11.1
 [1.11.0]: https://github.com/tensorlayer/tensorlayer/compare/1.10.1...1.11.0
 [1.10.1]: https://github.com/tensorlayer/tensorlayer/compare/1.10.0...1.10.1
 [1.10.0]: https://github.com/tensorlayer/tensorlayer/compare/1.9.1...1.10.0
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 6a010a23a..6e17d39fb 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -140,8 +140,8 @@ make html
 # An example of a static model
 # A static model has inputs and outputs with fixed shape.
 inputs = tl.layers.Input([32, 784])
-dense1 = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=784, name='dense1')(inputs)
-dense2 = tl.layers.Dense(n_units=10,  act=tf.nn.relu, in_channels=800, name='dense2')(dense1)
+dense1 = tl.layers.Dense(n_units=800, act='relu', in_channels=784, name='dense1')(inputs)
+dense2 = tl.layers.Dense(n_units=10,  act='relu', in_channels=800, name='dense2')(dense1)
 model = tl.models.Model(inputs=inputs, outputs=dense2)
 
 # An example of a dynamic model
@@ -149,8 +149,8 @@ model = tl.models.Model(inputs=inputs, outputs=dense2)
 class CustomizeModel(tl.models.Model):
     def __init__(self):
         super(CustomizeModel, self).__init__()
-        self.dense1 = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=784, name='dense1')
-        self.dense2 = tl.layers.Dense(n_units=10,  act=tf.nn.relu, in_channels=800, name='dense2')
+        self.dense1 = tl.layers.Dense(n_units=800, act='relu', in_channels=784, name='dense1')
+        self.dense2 = tl.layers.Dense(n_units=10,  act='relu', in_channels=800, name='dense2')
 
     # a dynamic model allows more flexibility by customising forwarding.
     def forward(self, x, bar=None):
diff --git a/LICENSE.rst b/LICENSE.rst
index b662f1d30..b195ea11b 100644
--- a/LICENSE.rst
+++ b/LICENSE.rst
@@ -1,7 +1,7 @@
 License
 =======
 
-Copyright (c) 2016~2018 The TensorLayer contributors.  All rights reserved.
+Copyright (c) 2016~2020 The TensorLayer contributors.  All rights reserved.
 
                                  Apache License
                            Version 2.0, January 2004
@@ -208,4 +208,4 @@ Copyright (c) 2016~2018 The TensorLayer contributors.  All rights reserved.
 
 Contact
 =======
-Questions? Please contact hao.dong11@imperial.ac.uk
+Questions? Please contact hao.dong@pku.edu.cn
diff --git a/Makefile b/Makefile
index 4fbfd85c5..9ce4e51f6 100644
--- a/Makefile
+++ b/Makefile
@@ -14,16 +14,17 @@ test:
 	python3 tests/files/test_utils_saveload.py
 
 format:
-	autoflake -i examples/*.py
-	autoflake -i tensorlayer/*.py
-	autoflake -i tensorlayer/**/*.py
+	autoflake -ir examples
+	autoflake -ir tensorlayer
+	autoflake -ir tests
 
 	isort -rc examples
 	isort -rc tensorlayer
+	isort -rc tests
 
-	yapf -i examples/*.py
-	yapf -i tensorlayer/*.py
-	yapf -i tensorlayer/**/*.py
+	yapf -ir examples
+	yapf -ir tensorlayer
+	yapf -ir tests
 
 install3:
 	pip3 install -U . --user
diff --git a/README.md b/README.md
index b8d178cf5..033818f7c 100644
--- a/README.md
+++ b/README.md
@@ -4,77 +4,66 @@
     </div>
 </a>
 
-
-
-
 <!--- [![PyPI Version](https://badge.fury.io/py/tensorlayer.svg)](https://badge.fury.io/py/tensorlayer) --->
 <!--- ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tensorlayer.svg)) --->
+
 ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/tensorlayer/tensorlayer/master.svg)
 [![Supported TF Version](https://img.shields.io/badge/TensorFlow-2.0.0%2B-brightgreen.svg)](https://github.com/tensorflow/tensorflow/releases)
 [![Documentation Status](https://readthedocs.org/projects/tensorlayer/badge/)](https://tensorlayer.readthedocs.io/)
 [![Build Status](https://travis-ci.org/tensorlayer/tensorlayer.svg?branch=master)](https://travis-ci.org/tensorlayer/tensorlayer)
 [![Downloads](http://pepy.tech/badge/tensorlayer)](http://pepy.tech/project/tensorlayer)
+[![Downloads](https://pepy.tech/badge/tensorlayer/week)](https://pepy.tech/project/tensorlayer/week)
 [![Docker Pulls](https://img.shields.io/docker/pulls/tensorlayer/tensorlayer.svg)](https://hub.docker.com/r/tensorlayer/tensorlayer/)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d6b118784e25435498e7310745adb848)](https://www.codacy.com/app/tensorlayer/tensorlayer)
 
 <!---  [![CircleCI](https://circleci.com/gh/tensorlayer/tensorlayer/tree/master.svg?style=svg)](https://circleci.com/gh/tensorlayer/tensorlayer/tree/master) --->
 
-<!---  [![Documentation Status](https://readthedocs.org/projects/tensorlayercn/badge/)](https://tensorlayercn.readthedocs.io/) 
+<!---  [![Documentation Status](https://readthedocs.org/projects/tensorlayercn/badge/)](https://tensorlayercn.readthedocs.io/)
 <!---  [![PyUP Updates](https://pyup.io/repos/github/tensorlayer/tensorlayer/shield.svg)](https://pyup.io/repos/github/tensorlayer/tensorlayer/) --->
 
+# Please click [TensorLayerX](https://github.com/tensorlayer/tensorlayerx) 🔥🔥🔥
 
+[TensorLayer](https://tensorlayer.readthedocs.io) is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass [tutorials](https://github.com/tensorlayer/tensorlayer/blob/master/examples/reinforcement_learning/README.md) and [applications](https://github.com/tensorlayer). TensorLayer is awarded the 2017 Best Open Source Software by the [ACM Multimedia Society](https://twitter.com/ImperialDSI/status/923928895325442049). 
+This project can also be found at [OpenI](https://git.openi.org.cn/TensorLayer/tensorlayer3.0) and [Gitee](https://gitee.com/organizations/TensorLayer).
 
-<br/>
+# News
 
-<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc" target="\_blank">
-	<div align="center">
-		<img src="img/join_slack.png" width="40%"/>
-	</div>
-</a>
-
-<br/>
-
-TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the [ACM Multimedia Society](https://twitter.com/ImperialDSI/status/923928895325442049).
+- 🔥 [TensorLayerX](https://github.com/tensorlayer/tensorlayerx) is a Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS. The current version supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend.
+- TensorLayer is now in [OpenI](https://git.openi.org.cn/TensorLayer/tensorlayer3.0)
+- Reinforcement Learning Zoo: [Low-level APIs](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning) for professional usage, [High-level APIs](https://github.com/tensorlayer/RLzoo) for simple usage, and a corresponding [Springer textbook](http://springer.com/gp/book/9789811540943)
+- [Sipeed Maxi-EMC](https://github.com/sipeed/Maix-EMC): Run TensorLayer models on the **low-cost AI chip** (e.g., K210) (Alpha Version)
 
-🔥📰🔥 Reinforcement Learning Model Zoos: [Low-level APIs for Research](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning) and [High-level APIs for Production](https://github.com/tensorlayer/RLzoo)
+<!-- 🔥 [NNoM](https://github.com/majianjia/nnom): Run TensorLayer quantized models on the **MCU** (e.g., STM32) (Coming Soon) -->
 
-🔥📰🔥 [Sipeed Maxi-EMC](https://github.com/sipeed/Maix-EMC): Run TensorLayer models on the **low-cost AI chip** (e.g., K210) (Alpha Version)
+# Design Features
 
-🔥📰🔥 [NNoM](https://github.com/majianjia/nnom): Run TensorLayer quantized models on the **MCU** (e.g., STM32) (Coming Soon)
+TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
 
+- ***Simplicity*** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
+- ***Flexibility*** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
+- ***Zero-cost Abstraction*** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
 
-# Features
+TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
+hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
+making it easy to learn while being flexible enough to cope with complex AI tasks.
+TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
+Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
 
-As deep learning practitioners, we have been looking for a library that can address various development
- purposes. This library is easy to adopt by providing diverse examples, tutorials and pre-trained models.
-Also, it allow users to easily fine-tune TensorFlow; while being suitable for production deployment. TensorLayer aims to satisfy all these purposes. It has three key features:
-
-- ***Simplicity*** : TensorLayer lifts the low-level dataflow interface of TensorFlow to *high-level* layers / models. It is very easy to learn through the rich [example codes](https://github.com/tensorlayer/awesome-tensorlayer) contributed by a wide community.
-- ***Flexibility*** : TensorLayer APIs are transparent: it does not mask TensorFlow from users; but leaving massive hooks that help *low-level tuning* and *deep customization*.
-- ***Zero-cost Abstraction*** : TensorLayer can achieve the *full power* of TensorFlow. The following table shows the training speeds of [VGG16](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) using TensorLayer and native TensorFlow on a TITAN Xp.
-
-    |   Mode    |       Lib       |  Data Format  | Max GPU Memory Usage(MB)  |Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |
-    | :-------: | :-------------: | :-----------: | :-----------------: | :-----------------: | :-----------------: | :-----------: |
-    | AutoGraph | TensorFlow 2.0  | channel last  | 11833 |      2161         |        2136         |      74       |
-    |           | Tensorlayer 2.0 | channel last  | 11833 |      2187         |        2169         |      76       |
-    |   Graph   |      Keras      | channel last  | 8677 |      2580         |        2576         |      101       |
-    |   Eager   | TensorFlow 2.0  | channel last  | 8723 |      2052         |        2024         |      97       |
-    |           | TensorLayer 2.0 | channel last  | 8723 |      2010         |        2007         |      95       |
+# Multilingual Documents
 
+TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
+both English and Chinese.
 
-TensorLayer stands at a unique spot in the library landscape. Other wrapper libraries like Keras and TFLearn also provide high-level abstractions. They, however, often
-hide the underlying engine from users, which make them hard to customize
-and fine-tune. On the contrary, TensorLayer APIs are generally lightweight, flexible and transparent.
-Users often find it easy to start with the examples and tutorials, and then dive
-into TensorFlow seamlessly. In addition, TensorLayer does not create library lock-in through native supports for importing components from Keras.
+[![English Documentation](https://img.shields.io/badge/documentation-english-blue.svg)](https://tensorlayer.readthedocs.io/)
+[![Chinese Documentation](https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg)](https://tensorlayercn.readthedocs.io/)
+[![Chinese Book](https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg)](http://www.broadview.com.cn/book/5059/)
 
-TensorLayer has a fast growing usage among top researchers and engineers, from universities like Peking University,
-Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and
-University of Technology of Compiegne (UTC), and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
+If you want to try the experimental features on the the master branch, you can find the latest document
+[here](https://tensorlayer.readthedocs.io/en/latest/).
 
-# Tutorials and Real-World Applications
+# Extensive Examples
 
-You can find a large collection of tutorials, examples and real-world applications using TensorLayer within [examples](examples/) or through the following space:
+You can find a large collection of examples that use TensorLayer in [here](examples/) and the following space:
 
 <a href="https://github.com/tensorlayer/awesome-tensorlayer/blob/master/readme.md" target="\_blank">
 	<div align="center">
@@ -82,73 +71,42 @@ You can find a large collection of tutorials, examples and real-world applicatio
 	</div>
 </a>
 
-# Documentation
-
-TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
-both English and Chinese. Please click the following icons to find the documents you need:
-
-[![English Documentation](https://img.shields.io/badge/documentation-english-blue.svg)](https://tensorlayer.readthedocs.io/)
-[![Chinese Documentation](https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg)](https://tensorlayercn.readthedocs.io/)
-[![Chinese Book](https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg)](http://www.broadview.com.cn/book/5059/)
-
-If you want to try the experimental features on the the master branch, you can find the latest document
-[here](https://tensorlayer.readthedocs.io/en/latest/).
-
-# Install
+# Getting Start
 
-For latest code for TensorLayer 2.0, please build from the source. TensorLayer 2.0 has pre-requisites including TensorFlow 2, numpy, and others. For GPU support, CUDA and cuDNN are required.
+TensorLayer 2.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.
 
 Install TensorFlow:
 
 ```bash
-pip3 install tensorflow-gpu==2.0.0-beta1 # specific version  (YOU SHOULD INSTALL THIS ONE NOW)
-pip3 install tensorflow-gpu # GPU version
+pip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1)
 pip3 install tensorflow # CPU version
 ```
 
-Install the stable version of TensorLayer:
+Install the stable release of TensorLayer:
 
 ```bash
 pip3 install tensorlayer
 ```
 
-Install the latest version of TensorLayer:
+Install the unstable development version of TensorLayer:
 
 ```bash
 pip3 install git+https://github.com/tensorlayer/tensorlayer.git
-or
-pip3 install https://github.com/tensorlayer/tensorlayer/archive/master.zip
-```
-
-For developers, you should clone the folder to your local machine and put it along with your project scripts.
-
-```bash
-git clone https://github.com/tensorlayer/tensorlayer.git
 ```
 
-If you want install TensorLayer 1.X, the simplest way to install TensorLayer 1.X is to use the **Py**thon **P**ackage **I**ndex (PyPI):
-
+If you want to install the additional dependencies, you can also run
 ```bash
-# for last stable version of TensorLayer 1.X
-pip3 install --upgrade tensorlayer==1.X
-
-# for latest release candidate of TensorLayer 1.X
-pip3 install --upgrade --pre tensorlayer
-
-# if you want to install the additional dependencies, you can also run
 pip3 install --upgrade tensorlayer[all]              # all additional dependencies
 pip3 install --upgrade tensorlayer[extra]            # only the `extra` dependencies
 pip3 install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies
 ```
-<!---
-Alternatively, you can install the latest or development version by directly pulling from github:
+
+If you are TensorFlow 1.X users, you can use TensorLayer 1.11.0:
 
 ```bash
-pip3 install https://github.com/tensorlayer/tensorlayer/archive/master.zip
-# or
-# pip3 install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip
+# For last stable version of TensorLayer 1.X
+pip3 install --upgrade tensorlayer==1.11.0
 ```
---->
 
 <!---
 ## Using Docker
@@ -182,12 +140,37 @@ nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASS
 ```
 --->
 
-# Contribute
+# Performance Benchmark
+
+The following table shows the training speeds of [VGG16](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) using TensorLayer and native TensorFlow on a TITAN Xp.
+
+|   Mode    |       Lib       |  Data Format  | Max GPU Memory Usage(MB)  |Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |
+| :-------: | :-------------: | :-----------: | :-----------------: | :-----------------: | :-----------------: | :-----------: |
+| AutoGraph | TensorFlow 2.0  | channel last  | 11833 |      2161         |        2136         |      74       |
+|           | TensorLayer 2.0 | channel last  | 11833 |      2187         |        2169         |      76       |
+|   Graph   |      Keras      | channel last  | 8677 |      2580         |        2576         |      101       |
+|   Eager   | TensorFlow 2.0  | channel last  | 8723 |      2052         |        2024         |      97       |
+|           | TensorLayer 2.0 | channel last  | 8723 |      2010         |        2007         |      95       |
+
+# Getting Involved
 
 Please read the [Contributor Guideline](CONTRIBUTING.md) before submitting your PRs.
 
-# Cite
-If you use TensorLayer for any projects, please cite this paper：
+We suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayer in the following slack channel.
+
+<br/>
+
+<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY" target="\_blank">
+	<div align="center">
+		<img src="img/join_slack.png" width="40%"/>
+	</div>
+</a>
+
+<br/>
+
+# Citing TensorLayer
+
+If you find TensorLayer useful for your project, please cite the following papers：
 
 ```
 @article{tensorlayer2017,
@@ -197,8 +180,13 @@ If you use TensorLayer for any projects, please cite this paper：
     url     = {http://tensorlayer.org},
     year    = {2017}
 }
-```
 
-# License
-
-TensorLayer is released under the Apache 2.0 license.
+@inproceedings{tensorlayer2021,
+  title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
+  author={Lai, Cheng and Han, Jiarong and Dong, Hao},
+  booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
+  pages={1--3},
+  year={2021},
+  organization={IEEE}
+}
+```
diff --git a/README.rst b/README.rst
index 5f424cf63..e92c949a4 100644
--- a/README.rst
+++ b/README.rst
@@ -17,52 +17,20 @@ to build real-world AI applications. TensorLayer is awarded the 2017
 Best Open Source Software by the `ACM Multimedia
 Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
 
-Why another deep learning library: TensorLayer
-==============================================
-
-As deep learning practitioners, we have been looking for a library that
-can address various development purposes. This library is easy to adopt
-by providing diverse examples, tutorials and pre-trained models. Also,
-it allow users to easily fine-tune TensorFlow; while being suitable for
-production deployment. TensorLayer aims to satisfy all these purposes.
-It has three key features:
-
--  **Simplicity** : TensorLayer lifts the low-level dataflow interface
-   of TensorFlow to *high-level* layers / models. It is very easy to
-   learn through the rich `example
-   codes <https://github.com/tensorlayer/awesome-tensorlayer>`__
-   contributed by a wide community.
--  **Flexibility** : TensorLayer APIs are transparent: it does not
-   mask TensorFlow from users; but leaving massive hooks that help
-   *low-level tuning* and *deep customization*.
--  **Zero-cost Abstraction** : TensorLayer can achieve the *full
-   power* of TensorFlow. The following table shows the training speeds
-   of classic models using TensorLayer and native TensorFlow on a Titan
-   X Pascal GPU.
-
-   +---------------+-----------------+-----------------+-----------------+
-   |               | CIFAR-10        | PTB LSTM        | Word2Vec        |
-   +===============+=================+=================+=================+
-   | TensorLayer   | 2528 images/s   | 18063 words/s   | 58167 words/s   |
-   +---------------+-----------------+-----------------+-----------------+
-   | TensorFlow    | 2530 images/s   | 18075 words/s   | 58181 words/s   |
-   +---------------+-----------------+-----------------+-----------------+
-
-TensorLayer stands at a unique spot in the library landscape. Other
-wrapper libraries like Keras and TFLearn also provide high-level
-abstractions. They, however, often hide the underlying engine from
-users, which make them hard to customize and fine-tune. On the contrary,
-TensorLayer APIs are generally flexible and transparent. Users often
-find it easy to start with the examples and tutorials, and then dive
-into TensorFlow seamlessly. In addition, TensorLayer does not create
-library lock-in through native supports for importing components from
-Keras, TFSlim and TFLearn.
-
-TensorLayer has a fast growing usage among top researchers and
-engineers, from universities like Imperial College London, UC Berkeley,
-Carnegie Mellon University, Stanford University, and University of
-Technology of Compiegne (UTC), and companies like Google, Microsoft,
-Alibaba, Tencent, Xiaomi, and Bloomberg.
+Design Features
+=================
+
+TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
+
+- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
+- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
+- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
+
+TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
+hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
+making it easy to learn while being flexible enough to cope with complex AI tasks.
+TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
+Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
 
 Install
 =======
@@ -139,7 +107,7 @@ Cite
 ====
 
 If you find this project useful, we would be grateful if you cite the
-TensorLayer paper：
+TensorLayer papers.
 
 ::
 
@@ -151,6 +119,14 @@ TensorLayer paper：
         year    = {2017}
     }
 
+    @inproceedings{tensorlayer2021,
+      title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
+      author={Lai, Cheng and Han, Jiarong and Dong, Hao},
+      booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
+      pages={1--3},
+      year={2021},
+      organization={IEEE}
+
 License
 =======
 
diff --git a/docker/pypi_list.py b/docker/pypi_list.py
index 69a2bad39..05cf3f5e5 100644
--- a/docker/pypi_list.py
+++ b/docker/pypi_list.py
@@ -14,7 +14,6 @@
 
     args = parser.parse_args()
 
-    # create logger
     logger = logging.getLogger("PyPI_CLI")
 
     formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
@@ -32,11 +31,7 @@
     logger.debug("prerelease: %s" % args.prerelease)
     logger.debug("debug: %s" % args.debug)
 
-    finder = pip._internal.index.PackageFinder(
-        [],
-        ['https://pypi.python.org/simple'],
-        session=requests.Session()
-    )
+    finder = pip._internal.index.PackageFinder([], ['https://pypi.python.org/simple'], session=requests.Session())
     results = finder.find_all_candidates(args.package)
     tmp_versions = [str(p.version) for p in results]
 
diff --git a/docker/version_prefix.py b/docker/version_prefix.py
index 7ee648d61..f89252f23 100644
--- a/docker/version_prefix.py
+++ b/docker/version_prefix.py
@@ -6,17 +6,13 @@
     parser = argparse.ArgumentParser(description='Determine the version prefix to apply depending on the version name')
 
     parser.add_argument(
-        '--version',
-        type=str,
-        required=True,
-        help='The Package Version to be installed in the container'
+        '--version', type=str, required=True, help='The Package Version to be installed in the container'
     )
 
     parser.add_argument('--debug', help='Print debug information', action='store_true')
 
     args = parser.parse_args()
 
-    # create logger
     logger = logging.getLogger("VERSION_PREFIX_CLI")
 
     formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
diff --git a/docs/conf.py b/docs/conf.py
index 89f16018b..ea43e77d3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -19,7 +19,7 @@
 #
 import os, sys, datetime
 sys.path.insert(0, os.path.abspath("../"))  # Important
-sys.path.insert(0, os.path.abspath(os.path.join("..", "tensorlayer"))) # Important
+sys.path.insert(0, os.path.abspath(os.path.join("..", "tensorlayer")))  # Important
 
 from package_info import __shortversion__
 from package_info import __version__
@@ -34,12 +34,6 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 
-# extensions = [
-#    'sphinx.ext.coverage',
-#    'sphinx.ext.githubpages',
-#    'numpydoc',
-# ]
-
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
@@ -159,7 +153,6 @@
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = False
 
-
 # -- Options for HTML output ----------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
@@ -284,29 +277,28 @@
 # -- Options for LaTeX output ---------------------------------------------
 
 latex_elements = {
-     # The paper size ('letterpaper' or 'a4paper').
-     #
-     # 'papersize': 'letterpaper',
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
 
-     # The font size ('10pt', '11pt' or '12pt').
-     #
-     # 'pointsize': '10pt',
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
 
-     # Additional stuff for the LaTeX preamble.
-     #
-     # 'preamble': '',
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
 
-     # Latex figure (float) alignment
-     #
-     # 'figure_align': 'htbp',
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
 }
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'TensorLayer.tex', 'TensorLayer Documentation',
-     'TensorLayer contributors', 'manual'),
+    (master_doc, 'TensorLayer.tex', 'TensorLayer Documentation', 'TensorLayer contributors', 'manual'),
 ]
 
 # The name of an image file (relative to this directory) to place at the top of
@@ -335,30 +327,26 @@
 #
 # latex_domain_indices = True
 
-
 # -- Options for manual page output ---------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'tensorlayer', 'TensorLayer Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, 'tensorlayer', 'TensorLayer Documentation', [author], 1)]
 
 # If true, show URL addresses after external links.
 #
 # man_show_urls = False
 
-
 # -- Options for Texinfo output -------------------------------------------
 
 # Grouping the document tree into Texinfo files. List of tuples
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'TensorLayer', 'TensorLayer Documentation',
-     author, 'TensorLayer', 'Deep learning and Reinforcement learning library for Researchers and Engineers.',
-     'Miscellaneous'),
+    (
+        master_doc, 'TensorLayer', 'TensorLayer Documentation', author, 'TensorLayer',
+        'Deep learning and Reinforcement learning library for Researchers and Engineers.', 'Miscellaneous'
+    ),
 ]
 
 # Documents to append as an appendix to all manuals.
@@ -377,7 +365,6 @@
 #
 # texinfo_no_detailmenu = False
 
-
 # -- Options for Epub output ----------------------------------------------
 
 # Bibliographic Dublin Core info.
diff --git a/docs/images/3d_human_pose_result.jpg b/docs/images/3d_human_pose_result.jpg
new file mode 100644
index 000000000..0cb869333
Binary files /dev/null and b/docs/images/3d_human_pose_result.jpg differ
diff --git a/docs/images/human_pose_points.jpg b/docs/images/human_pose_points.jpg
new file mode 100644
index 000000000..c9dc68a59
Binary files /dev/null and b/docs/images/human_pose_points.jpg differ
diff --git a/docs/images/yolov4_image_result.png b/docs/images/yolov4_image_result.png
new file mode 100644
index 000000000..9683dc592
Binary files /dev/null and b/docs/images/yolov4_image_result.png differ
diff --git a/docs/images/yolov4_video_result.gif b/docs/images/yolov4_video_result.gif
new file mode 100644
index 000000000..91a23fb4d
Binary files /dev/null and b/docs/images/yolov4_video_result.gif differ
diff --git a/docs/index.rst b/docs/index.rst
index 977f3379d..b4b1fd2b6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -9,7 +9,7 @@ Welcome to TensorLayer
 
 **Documentation Version:** |release|
 
-**Jun 2019** `Deep Reinforcement Learning Model ZOO Release !! <https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning>`__.
+**Jun 2020** `Deep Reinforcement Learning Book Is Released <http://deepreinforcementlearningbook.org>`__.
 
 **Good News:** We won the **Best Open Source Software Award** `@ACM Multimedia (MM) 2017 <http://www.acmmm.org/2017/mm-2017-awardees/>`_.
 
diff --git a/docs/modules/activation.rst b/docs/modules/activation.rst
index 3965bd007..79bad9601 100644
--- a/docs/modules/activation.rst
+++ b/docs/modules/activation.rst
@@ -35,6 +35,7 @@ For more complex activation, TensorFlow API will be required.
    sign
    hard_tanh
    pixel_wise_softmax
+   mish
 
 Ramp
 ------
@@ -68,6 +69,10 @@ Pixel-wise softmax
 --------------------
 .. autofunction:: pixel_wise_softmax
 
+mish
+---------
+.. autofunction:: mish
+
 Parametric activation
 ------------------------------
 See ``tensorlayer.layers``.
diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
index f6c86a542..78e0eee9a 100644
--- a/docs/modules/layers.rst
+++ b/docs/modules/layers.rst
@@ -13,6 +13,9 @@ Layer list
 .. autosummary::
 
    Layer
+   
+   ModelLayer
+   LayerList
 
    Input
 
@@ -131,8 +134,18 @@ Layer list
 Base Layer
 -----------
 
+Base Layer
+^^^^^^^^^^^^^^^^
 .. autoclass:: Layer
 
+Model Layer
+^^^^^^^^^^^^^^^^
+.. autoclass:: ModelLayer
+
+Layer List
+^^^^^^^^^^^^^^^^
+.. autoclass:: LayerList
+
 .. -----------------------------------------------------------
 ..                        Input Layer
 .. -----------------------------------------------------------
diff --git a/docs/modules/rein.rst b/docs/modules/rein.rst
index 9ee16a6be..79f4e76ca 100644
--- a/docs/modules/rein.rst
+++ b/docs/modules/rein.rst
@@ -1,7 +1,10 @@
 API - Reinforcement Learning
 ==============================
 
-Reinforcement Learning.
+We provide two reinforcement learning libraries:
+
+- `RL-tutorial <https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning>`__ for professional users with low-level APIs.
+- `RLzoo <https://rlzoo.readthedocs.io/en/latest/>`__ for simple usage with high-level APIs.
 
 .. automodule:: tensorlayer.rein
 
diff --git a/docs/modules/visualize.rst b/docs/modules/visualize.rst
index 0bbe02861..0ef8f3b12 100644
--- a/docs/modules/visualize.rst
+++ b/docs/modules/visualize.rst
@@ -19,6 +19,7 @@ to visualize the model, activations etc. Here we provide more functions for data
    frame
    images2d
    tsne_embedding
+   draw_boxes_and_labels_to_image_with_json
 
 
 Save and read images
@@ -44,6 +45,9 @@ Save image for object detection
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: draw_boxes_and_labels_to_image
 
+Save image for object detection with json
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: draw_boxes_and_labels_to_image_with_json
 
 Save image for pose estimation (MPII)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/docs/user/contributing.rst b/docs/user/contributing.rst
index a83767a6c..9b1d98f88 100644
--- a/docs/user/contributing.rst
+++ b/docs/user/contributing.rst
@@ -40,8 +40,10 @@ For TensorLayer 1.x, it was actively developed and maintained by the following p
 - **Hao Dong** (`@zsdonghao <https://github.com/zsdonghao>`_) - `<https://zsdonghao.github.io>`_
 - **Jonathan Dekhtiar** (`@DEKHTIARJonathan <https://github.com/DEKHTIARJonathan>`_) - `<https://www.jonathandekhtiar.eu>`_
 - **Luo Mai** (`@luomai <https://github.com/luomai>`_) - `<http://www.doc.ic.ac.uk/~lm111/>`_
+- **Pan Wang** (`@FerociousPanda <http://github.com/FerociousPanda>`_) - `<http://github.com/FerociousPanda>`_  (UI)
 - **Simiao Yu** (`@nebulaV <https://github.com/nebulaV>`_) - `<https://nebulav.github.io>`_
 
+
 Numerous other contributors can be found in the `Github Contribution Graph <https://github.com/tensorlayer/tensorlayer/graphs/contributors>`_.
 
 
diff --git a/docs/user/get_involved.rst b/docs/user/get_involved.rst
index b7b5f292b..90d699aff 100644
--- a/docs/user/get_involved.rst
+++ b/docs/user/get_involved.rst
@@ -4,8 +4,21 @@
 Get Involved in Research
 =========================
 
+Ph.D. Postition @ PKU
+=============================================================
+
+
+Hi, I am `Hao Dong <https://zsdonghao.github.io/>`__, the founder of this project and a new faculty member in EECS, Peking University. I now have a few Ph.D. positions per year open for international students who would like to study AI. If you or your friends are interested in it, feel free to contact me.
+PKU is a top 30 university in the global ranking. The application is competitive, apply early is recommended. Please check the following links for more details.
+
+- `About the International Elite Ph.D. Program in Computer Science <https://cs.pku.edu.cn/info/1115/2233.htm>`__
+- `My homepage <https://zsdonghao.github.io/>`__
+
+Contact: hao.dong [AT] pku.edu.cn
+
+
 
-AP Opportunity, Peking University
+Faculty Postition @ PKU
 =============================================================
 
 The Center on Frontiers of Computing Studies (CFCS), Peking University (PKU), China, is a university new initiative co-founded by Professors John Hopcroft (Turing Awardee) and Wen Gao (CAE, ACM/IEEE Fellow). The center aims at developing the excellence on two fronts: research and education. On the research front, the center will provide a world-class research environment, where innovation and impactful research is the central aim, measured by professional reputation among world scholars, not by counting the number of publications and research funding. On the education front, the center deeply involves in the Turing Class, an elite undergraduate program that draws the cream of the crop from the PKU undergraduate talent pool. New curriculum and pedagogy are designed and practiced in this program, with the aim to cultivate a new generation of computer scientist/engineers that are solid in both theories and practices. 
@@ -27,7 +40,7 @@ Application for a postdoctoral position should include a curriculum vita, brief
 We conduct review of applications monthly, immediately upon the recipient of all application materials at the beginning of each month. However, it is highly recommended that applicants submit complete applications sooner than later, as the positions are to be filled quickly. 
  
 
-Postdoc Opportunity, Imperial College London
+Postdoc Postition @ ICL
 ==================================================
 
 Data science is therefore by nature at the core of all modern transdisciplinary scientific activities, as it involves the whole life cycle of data, from acquisition and exploration to analysis and communication of the results. Data science is not only concerned with the tools and methods to obtain, manage and analyse data: it is also about extracting value from data and translating it from asset to product.
@@ -48,3 +61,17 @@ and other ways to
 `get involved <https://www.imperial.ac.uk/data-science/get-involved/>`__
 , or feel free to
 `contact us <https://www.imperial.ac.uk/data-science/get-involved/contact-us/>`__.
+
+Software Engineer @ SurgicalAI.cn
+=============================================================
+SurgicalAI is a startup founded by the data scientists and surgical robot experts from Imperial College. Our objective is AI democratise Surgery. By combining 5G, AI and Cloud Computing, SurgicalAI is building a platform enable junor surgeons to perfom complex procedures. As one of the most impactful startup, SurgicalAI is supported by Nvidia, AWS and top surgeons around the world.
+
+Currently based in Hangzhou, China, we are building digital solution for cardiac surgery like TAVR, LAA and Orthopedidcs like TKA and UNA. A demo can be found at here <http://demo5g.surgicalai.cn>
+
+We are activly looking for experts in robotic navigation, computer graphics and  medical image analysis experts to join us, building digitalized surgical service platform for the aging world.
+
+Home Page: http://www.surgicalai.cn
+
+Demo Page: http://demo5g.surgicalai.cn
+
+Contact: liufangde@surgicalai.cn
diff --git a/docs/user/get_start_model.rst b/docs/user/get_start_model.rst
index 8c6b95a8d..2337a7d55 100644
--- a/docs/user/get_start_model.rst
+++ b/docs/user/get_start_model.rst
@@ -19,12 +19,12 @@ Static model
   def get_model(inputs_shape):
       ni = Input(inputs_shape)
       nn = Dropout(keep=0.8)(ni)
-      nn = Dense(n_units=800, act=tf.nn.relu, name="dense1")(nn)
+      nn = Dense(n_units=800, act=tf.nn.relu, name="dense1")(nn) # “name" is optional
       nn = Dropout(keep=0.8)(nn)
       nn = Dense(n_units=800, act=tf.nn.relu)(nn)
       nn = Dropout(keep=0.8)(nn)
-      nn = Dense(n_units=10, act=tf.nn.relu)(nn)
-      M = Model(inputs=ni, outputs=nn, name="mlp")
+      nn = Dense(n_units=10, act=None)(nn)
+      M = Model(inputs=ni, outputs=nn, name="mlp") # “name" is optional
       return M
 
   MLP = get_model([None, 784])
@@ -46,10 +46,10 @@ In this case, you need to manually input the output shape of the previous layer
 
           self.dropout1 = Dropout(keep=0.8)
           self.dense1 = Dense(n_units=800, act=tf.nn.relu, in_channels=784)
-          self.dropout2 = Dropout(keep=0.8)#(self.dense1)
+          self.dropout2 = Dropout(keep=0.8)
           self.dense2 = Dense(n_units=800, act=tf.nn.relu, in_channels=800)
-          self.dropout3 = Dropout(keep=0.8)#(self.dense2)
-          self.dense3 = Dense(n_units=10, act=tf.nn.relu, in_channels=800)
+          self.dropout3 = Dropout(keep=0.8)
+          self.dense3 = Dense(n_units=10, act=None, in_channels=800)
 
       def forward(self, x, foo=False):
           z = self.dropout1(x)
@@ -59,7 +59,7 @@ In this case, you need to manually input the output shape of the previous layer
           z = self.dropout3(z)
           out = self.dense3(z)
           if foo:
-              out = tf.nn.relu(out)
+              out = tf.nn.softmax(out)
           return out
 
   MLP = CustomModel()
@@ -156,7 +156,7 @@ Print model information
   #   (dropout_1): Dropout(keep=0.8, name='dropout_1')
   #   (dense_1): Dense(n_units=800, relu, in_channels='800', name='dense_1')
   #   (dropout_2): Dropout(keep=0.8, name='dropout_2')
-  #   (dense_2): Dense(n_units=10, relu, in_channels='800', name='dense_2')
+  #   (dense_2): Dense(n_units=10, None, in_channels='800', name='dense_2')
   # )
   
   import pprint
@@ -195,7 +195,7 @@ Print model information
   #                                   'name': 'dropout_3'},
   #                          'class': 'Dropout',
   #                          'prev_layer': ['dense_2_node_0']},
-  #                         {'args': {'act': 'relu',
+  #                         {'args': {'act': None,
   #                                   'layer_type': 'normal',
   #                                   'n_units': 10,
   #                                   'name': 'dense_3'},
diff --git a/docs/user/installation.rst b/docs/user/installation.rst
index bb8605412..3ba467f84 100644
--- a/docs/user/installation.rst
+++ b/docs/user/installation.rst
@@ -35,6 +35,8 @@ For stable version:
 .. code-block:: bash
 
   pip3 install tensorlayer
+  
+  pip install tensorlayer -i https://pypi.tuna.tsinghua.edu.cn/simple  (faster in China)
 
 For latest version, please install from Github.
 
@@ -60,7 +62,7 @@ Alternatively, you can build from the source.
   cd tensorlayer
 
   # Install virtualenv if necessary
-  pip install virtualenv
+  sudo pip3 install virtualenv
   # Then create a virtualenv called `venv`
   virtualenv venv
 
@@ -73,21 +75,21 @@ Alternatively, you can build from the source.
   venv\Scripts\activate.bat
 
   # basic installation
-  pip install .
+  pip3 install .
 
   # ============= IF TENSORFLOW IS NOT ALREADY INSTALLED ============= #
 
   # for a machine **without** an NVIDIA GPU
-  pip install -e ".[all_cpu_dev]"
+  pip3 install -e ".[all_cpu_dev]"
 
   # for a machine **with** an NVIDIA GPU
-  pip install -e ".[all_gpu_dev]"
+  pip3 install -e ".[all_gpu_dev]"
 
 If you want install TensorLayer 1.X, the simplest way to install TensorLayer 1.X is as follow. It will also install the numpy and matplotlib automatically.
 
 .. code-block:: bash
 
-  [stable version] pip install tensorlayer==1.x.x
+  [stable version] pip3 install tensorlayer==1.x.x
 
 However, if you want to modify or extend TensorLayer 1.X, you can download the repository from
 `Github`_ and install it as follow.
@@ -95,7 +97,7 @@ However, if you want to modify or extend TensorLayer 1.X, you can download the r
 .. code-block:: bash
 
   cd to the root of the git tree
-  pip install -e .
+  pip3 install -e .
 
 This command will run the ``setup.py`` to install TensorLayer. The ``-e`` reflects
 editable, then you can edit the source code in ``tensorlayer`` folder, and ``import`` the edited
@@ -194,9 +196,9 @@ For TensorLayer, please refer to the steps mentioned above.
 
 .. code-block:: bash
 
-  pip install tensorflow        #CPU version
-  pip install tensorflow-gpu    #GPU version (GPU version and CPU version just choose one)
-  pip install tensorlayer       #Install tensorlayer
+  pip3 install tensorflow        #CPU version
+  pip3 install tensorflow-gpu    #GPU version (GPU version and CPU version just choose one)
+  pip3 install tensorlayer       #Install tensorlayer
 
 
 
diff --git a/examples/app_tutorials/README.md b/examples/app_tutorials/README.md
new file mode 100644
index 000000000..5b8447e40
--- /dev/null
+++ b/examples/app_tutorials/README.md
@@ -0,0 +1,111 @@
+# Quick Start
+TensorLayer Implementation of [YOLOv4: Optimal Speed and Accuracy of Object Detection][1]
+
+TensorLayer Implementation of [Optimizing Network Structure for 3D Human Pose Estimation][2](ICCV2019)
+
+## YOLOv4
+
+Yolov4 was trained on COCO 2017 Dataset in this demo.
+
+### Data
+
+Download yolov4.weights file [yolov4_model.npz][3], Password: `idsz`, and put yolov4.weights under the folder `./examples/app_tutorials/model/`. Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── yolov4_model.npz
+                ├── coco.names
+                └── yolov4_weights_congfig.txt
+
+```
+
+
+You can put an image or a video under the folder `./examples/app_tutorials/data/`,like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                └── *.jpg/*.png/*.mp4/..  
+```
+### demo
+
+1. Image
+
+   Modify `image_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_image.py` according to your demand, then
+   
+```bash
+python tutorial_object_detection_yolov4_image.py
+```
+2. Video
+   
+   Modify `video_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_video.py` according to your demand, then
+
+```bash
+python tutorial_object_detection_yolov4_video.py
+```
+3. Output
+   
+   -Image
+   
+   <p align="center"><img src="../../docs/images/yolov4_image_result.png" width="640"\></p>
+  
+   -Video
+   
+   <p align="center"><img src="../../docs/images/yolov4_video_result.gif" width="640"\></p>
+
+
+
+## 3D Human Pose Estimation
+
+### Data
+
+Download 3D Human Pose Estimation model weights [lcn_model.npz][4], Password:`ec07`,and put it under the folder `./examples/app_tutorials/model/`, Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── lcn_model.npz
+                └── pose_weights_config.txt
+```
+Download finetuned Stacked Hourglass detections and preprocessed H3.6M data([H36M.rar][5],Password:`kw9i`), then uncompress and put them under the folder `./examples/app_tutorials/data/`, like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                ├── h36m_sh_dt_ft.pkl
+                ├── h36m_test.pkl
+                └── h36m_train.pkl
+```
+Each sample is a list with the length of 34 in three `.pkl` files. The list represents `[x,y]` of 17 human pose points:
+<p align="center"><img src="../../docs/images/human_pose_points.jpg" width="300"\></p>
+
+If you would like to know how to prepare the H3.6M data, please have a look at the [pose_lcn][6].
+
+### Demo
+
+For a quick demo, simply run
+
+```bash
+python tutorial_human_3dpose_estimation_LCN.py
+```
+This will produce a visualization similar to this:
+<p align="center"><img src="../../docs/images/3d_human_pose_result.jpg" width="1500"\></p>
+
+This demo maps 2D poses to 3D space. Each 3D space result list represents `[x,y,z]` of 17 human pose points.
+
+# Acknowledgement
+
+Yolov4 is bulit on https://github.com/AlexeyAB/darknet and https://github.com/hunglc007/tensorflow-yolov4-tflite.
+3D Human Pose Estimation is bulit on https://github.com/rujiewu/pose_lcn and https://github.com/una-dinosauria/3d-pose-baseline.
+We would like to thank the authors for publishing their code.
+
+
+[1]:https://arxiv.org/abs/2004.10934
+[2]:https://openaccess.thecvf.com/content_ICCV_2019/papers/Ci_Optimizing_Network_Structure_for_3D_Human_Pose_Estimation_ICCV_2019_paper.pdf
+[3]:https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ
+[4]:https://pan.baidu.com/s/1HBHWsAfyAlNaavw0iyUmUQ
+[5]:https://pan.baidu.com/s/1nA96AgMsvs1sFqkTs7Dfaw
+[6]:https://github.com/rujiewu/pose_lcn
diff --git a/examples/app_tutorials/model/coco.names b/examples/app_tutorials/model/coco.names
new file mode 100644
index 000000000..ec82f0ffd
--- /dev/null
+++ b/examples/app_tutorials/model/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+potted plant
+bed
+dining table
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
diff --git a/examples/app_tutorials/model/pose_weights_config.txt b/examples/app_tutorials/model/pose_weights_config.txt
new file mode 100644
index 000000000..393449071
--- /dev/null
+++ b/examples/app_tutorials/model/pose_weights_config.txt
@@ -0,0 +1,44 @@
+linear_model/w1
+linear_model/b1
+linear_model/batch_normalization/beta
+linear_model/batch_normalization/gamma
+linear_model/batch_normalization/moving_mean
+linear_model/batch_normalization/moving_variance
+linear_model/two_linear_0/w2_0
+linear_model/two_linear_0/b2_0
+linear_model/two_linear_0/batch_normalization10/beta
+linear_model/two_linear_0/batch_normalization10/gamma
+linear_model/two_linear_0/batch_normalization10/moving_mean
+linear_model/two_linear_0/batch_normalization10/moving_variance
+linear_model/two_linear_0/w3_0
+linear_model/two_linear_0/b3_0
+linear_model/two_linear_0/batch_normalization20/beta
+linear_model/two_linear_0/batch_normalization20/gamma
+linear_model/two_linear_0/batch_normalization20/moving_mean
+linear_model/two_linear_0/batch_normalization20/moving_variance
+linear_model/two_linear_1/w2_1
+linear_model/two_linear_1/b2_1
+linear_model/two_linear_1/batch_normalization11/beta
+linear_model/two_linear_1/batch_normalization11/gamma
+linear_model/two_linear_1/batch_normalization11/moving_mean
+linear_model/two_linear_1/batch_normalization11/moving_variance
+linear_model/two_linear_1/w3_1
+linear_model/two_linear_1/b3_1
+linear_model/two_linear_1/batch_normalization21/beta
+linear_model/two_linear_1/batch_normalization21/gamma
+linear_model/two_linear_1/batch_normalization21/moving_mean
+linear_model/two_linear_1/batch_normalization21/moving_variance
+linear_model/two_linear_2/w2_2
+linear_model/two_linear_2/b2_2
+linear_model/two_linear_2/batch_normalization12/beta
+linear_model/two_linear_2/batch_normalization12/gamma
+linear_model/two_linear_2/batch_normalization12/moving_mean
+linear_model/two_linear_2/batch_normalization12/moving_variance
+linear_model/two_linear_2/w3_2
+linear_model/two_linear_2/b3_2
+linear_model/two_linear_2/batch_normalization22/beta
+linear_model/two_linear_2/batch_normalization22/gamma
+linear_model/two_linear_2/batch_normalization22/moving_mean
+linear_model/two_linear_2/batch_normalization22/moving_variance
+linear_model/w4
+linear_model/b4
\ No newline at end of file
diff --git a/examples/app_tutorials/model/yolov4_weights_config.txt b/examples/app_tutorials/model/yolov4_weights_config.txt
new file mode 100644
index 000000000..2c28be036
--- /dev/null
+++ b/examples/app_tutorials/model/yolov4_weights_config.txt
@@ -0,0 +1,541 @@
+layer_with_weights-0/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-1/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-1/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-1/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-1/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-2/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-3/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-3/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-3/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-3/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-11/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-4/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-13/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-13/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-13/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-13/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-5/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-5/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-5/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-5/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-6/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-7/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-7/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-7/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-7/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-8/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-9/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-9/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-9/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-9/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-10/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-12/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-12/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-12/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-12/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-14/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-15/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-15/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-15/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-15/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-16/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-17/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-17/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-17/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-17/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-29/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-18/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-31/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-31/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-31/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-31/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-19/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-19/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-19/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-19/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-20/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-21/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-21/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-21/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-21/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-22/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-23/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-23/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-23/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-23/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-24/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-25/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-25/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-25/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-25/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-26/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-27/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-27/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-27/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-27/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-28/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-30/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-30/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-30/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
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+layer_with_weights-33/beta/.ATTRIBUTES/VARIABLE_VALUE
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+layer_with_weights-35/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-35/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-35/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-35/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-71/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-36/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-73/beta/.ATTRIBUTES/VARIABLE_VALUE
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+layer_with_weights-45/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-46/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-47/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-47/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-47/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-47/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-48/kernel/.ATTRIBUTES/VARIABLE_VALUE
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+layer_with_weights-190/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-191/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-191/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-191/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-191/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-192/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-193/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-193/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-193/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-193/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-194/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-195/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-195/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-195/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-195/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-209/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-212/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-212/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-212/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-212/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-196/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-215/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-215/bias/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-197/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-197/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-197/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-197/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-198/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-199/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-199/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-199/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-199/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-200/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-201/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-201/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-201/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-201/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-202/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-203/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-203/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-203/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-203/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-204/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-205/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-205/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-205/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-205/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-206/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-207/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-207/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-207/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-207/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-210/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-213/beta/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-213/gamma/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-213/moving_mean/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-213/moving_variance/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-216/kernel/.ATTRIBUTES/VARIABLE_VALUE
+layer_with_weights-216/bias/.ATTRIBUTES/VARIABLE_VALUE
\ No newline at end of file
diff --git a/examples/app_tutorials/tutorial_human_3dpose_estimation_LCN.py b/examples/app_tutorials/tutorial_human_3dpose_estimation_LCN.py
new file mode 100644
index 000000000..685092e51
--- /dev/null
+++ b/examples/app_tutorials/tutorial_human_3dpose_estimation_LCN.py
@@ -0,0 +1,19 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app.human_pose_estimation.common import DataReader, visualize_3D_pose, flip_data
+from tensorlayer.app import computer_vision
+import numpy as np
+
+datareader = DataReader()
+train_data, test_data = datareader.read_2d(which='scale', mode='gt', read_confidence=False)
+train_labels, test_labels = datareader.read_3d(which='scale', mode='gt')
+network = computer_vision.human_pose_estimation('3D-pose')
+test_data = flip_data(test_data)
+result = network(test_data)
+result = datareader.denormalize3D(np.asarray(result), which='scale')
+test_data = datareader.denormalize2D(test_data, which='scale')
+test_labels = datareader.denormalize3D(test_labels, which='scale')
+visualize_3D_pose(
+    test_data, test_labels, result
+)  # We plot 4 examples. You can modify this function according to your own needs.
diff --git a/examples/app_tutorials/tutorial_object_detection_yolov4_image.py b/examples/app_tutorials/tutorial_object_detection_yolov4_image.py
new file mode 100644
index 000000000..c3c152196
--- /dev/null
+++ b/examples/app_tutorials/tutorial_object_detection_yolov4_image.py
@@ -0,0 +1,20 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app import computer_vision
+from tensorlayer import visualize
+from tensorlayer.app.computer_vision_object_detection.common import read_class_names
+import numpy as np
+import cv2
+from PIL import Image
+INPUT_SIZE = 416
+image_path = './data/kite.jpg'
+
+class_names = read_class_names('./model/coco.names')
+original_image = cv2.imread(image_path)
+image = cv2.cvtColor(np.array(original_image), cv2.COLOR_BGR2RGB)
+net = computer_vision.object_detection('yolo4-mscoco')
+json_result = net(original_image)
+image = visualize.draw_boxes_and_labels_to_image_with_json(image, json_result, class_names)
+image = Image.fromarray(image.astype(np.uint8))
+image.show()
diff --git a/examples/app_tutorials/tutorial_object_detection_yolov4_video.py b/examples/app_tutorials/tutorial_object_detection_yolov4_video.py
new file mode 100644
index 000000000..9f1a3f4d6
--- /dev/null
+++ b/examples/app_tutorials/tutorial_object_detection_yolov4_video.py
@@ -0,0 +1,38 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app import computer_vision
+from tensorlayer import visualize
+from tensorlayer.app.computer_vision_object_detection.common import read_class_names
+import cv2
+INPUT_SIZE = 416
+video_path = './data/road.mp4'
+
+class_names = read_class_names('./model/coco.names')
+vid = cv2.VideoCapture(video_path)
+'''
+vid = cv2.VideoCapture(0) # the serial number of camera on you device
+'''
+
+if not vid.isOpened():
+    raise ValueError("Read Video Failed!")
+net = computer_vision.object_detection('yolo4-mscoco')
+frame_id = 0
+while True:
+    return_value, frame = vid.read()
+    if return_value:
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    else:
+        if frame_id == vid.get(cv2.CAP_PROP_FRAME_COUNT):
+            print("Video processing complete")
+            break
+        raise ValueError("No image! Try with another video format")
+
+    json_result = net(frame)
+    image = visualize.draw_boxes_and_labels_to_image_with_json(frame, json_result, class_names)
+    result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
+    cv2.imshow("result", result)
+    if cv2.waitKey(1) & 0xFF == ord('q'): break
+    frame_id += 1
diff --git a/examples/basic_tutorials/tutorial_cifar10_cnn_static.py b/examples/basic_tutorials/tutorial_cifar10_cnn_static.py
index ecb1117ce..9f7468497 100644
--- a/examples/basic_tutorials/tutorial_cifar10_cnn_static.py
+++ b/examples/basic_tutorials/tutorial_cifar10_cnn_static.py
@@ -5,8 +5,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Flatten, Input, LocalResponseNorm, MaxPool2d)
 from tensorlayer.models import Model
@@ -80,14 +80,11 @@ def get_model_batchnorm(inputs_shape):
 print_freq = 5
 n_step_epoch = int(len(y_train) / batch_size)
 n_step = n_epoch * n_step_epoch
-shuffle_buffer_size = 128  # 100
-# init_learning_rate = 0.1
-# learning_rate_decay_factor = 0.1
-# num_epoch_decay = 350
+shuffle_buffer_size = 128
 
 train_weights = net.trainable_weights
-# learning_rate = tf.Variable(init_learning_rate)
 optimizer = tf.optimizers.Adam(learning_rate)
+# looking for decay learning rate? see https://github.com/tensorlayer/srgan/blob/master/train.py
 
 
 def generator_train():
@@ -127,7 +124,7 @@ def _map_fn_train(img, target):
 
 def _map_fn_test(img, target):
     # 1. Crop the central [height, width] of the image.
-    img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
+    img = tf.image.resize_with_pad(img, 24, 24)
     # 2. Subtract off the mean and divide by the variance of the pixels.
     img = tf.image.per_image_standardization(img)
     img = tf.reshape(img, (24, 24, 3))
@@ -139,57 +136,45 @@ def _map_fn_test(img, target):
 train_ds = tf.data.Dataset.from_generator(
     generator_train, output_types=(tf.float32, tf.int32)
 )  # , output_shapes=((24, 24, 3), (1)))
-train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
 # train_ds = train_ds.repeat(n_epoch)
 train_ds = train_ds.shuffle(shuffle_buffer_size)
 train_ds = train_ds.prefetch(buffer_size=4096)
 train_ds = train_ds.batch(batch_size)
+train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
 # value = train_ds.make_one_shot_iterator().get_next()
 
 test_ds = tf.data.Dataset.from_generator(
     generator_test, output_types=(tf.float32, tf.int32)
 )  # , output_shapes=((24, 24, 3), (1)))
 # test_ds = test_ds.shuffle(shuffle_buffer_size)
-test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
 # test_ds = test_ds.repeat(n_epoch)
 test_ds = test_ds.prefetch(buffer_size=4096)
 test_ds = test_ds.batch(batch_size)
+test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
 # value_test = test_ds.make_one_shot_iterator().get_next()
 
 for epoch in range(n_epoch):
     start_time = time.time()
-
     train_loss, train_acc, n_iter = 0, 0, 0
     for X_batch, y_batch in train_ds:
         net.train()
-
         with tf.GradientTape() as tape:
             # compute outputs
             _logits = net(X_batch)
             # compute loss and update model
-            _loss_ce = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
-            _loss_L2 = 0
-            # for p in tl.layers.get_variables_with_name('relu/W', True, True):
-            #      _loss_L2 += tl.cost.lo_regularizer(1.0)(p)
-            _loss = _loss_ce + _loss_L2
-
+            _loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
         grad = tape.gradient(_loss, train_weights)
         optimizer.apply_gradients(zip(grad, train_weights))
-
         train_loss += _loss
         train_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
         n_iter += 1
 
     # use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-
         print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
-
         print("   train loss: {}".format(train_loss / n_iter))
         print("   train acc:  {}".format(train_acc / n_iter))
-
         net.eval()
-
         val_loss, val_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in test_ds:
             _logits = net(X_batch)  # is_train=False, disable dropout
@@ -199,10 +184,6 @@ def _map_fn_test(img, target):
         print("   val loss: {}".format(val_loss / n_iter))
         print("   val acc:  {}".format(val_acc / n_iter))
 
-    # FIXME : how to apply lr decay in eager mode?
-    # learning_rate.assign(tf.train.exponential_decay(init_learning_rate, epoch, num_epoch_decay,
-    #                                                 learning_rate_decay_factor))
-
 # use testing data to evaluate the model
 net.eval()
 test_loss, test_acc, n_iter = 0, 0, 0
diff --git a/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py b/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py
index f4ad787b7..d986b01a3 100644
--- a/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py
+++ b/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py
@@ -1,8 +1,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.models import Model
@@ -19,7 +19,6 @@ class CustomModel(Model):
 
     def __init__(self):
         super(CustomModel, self).__init__()
-
         self.dropout1 = Dropout(keep=0.8)  #(self.innet)
         self.dense1 = Dense(n_units=800, act=tf.nn.relu, in_channels=784)  #(self.dropout1)
         self.dropout2 = Dropout(keep=0.8)  #(self.dense1)
@@ -52,27 +51,20 @@ def forward(self, x, foo=None):
 for epoch in range(n_epoch):  ## iterate the dataset n_epoch times
     start_time = time.time()
     ## iterate over the entire training set once (shuffle the data via training)
-
     for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-
         MLP.train()  # enable dropout
-
         with tf.GradientTape() as tape:
             ## compute outputs
             _logits = MLP(X_batch, foo=1)
             ## compute loss and update model
             _loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
-
         grad = tape.gradient(_loss, train_weights)
         optimizer.apply_gradients(zip(grad, train_weights))
 
     ## use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-
         MLP.eval()  # disable dropout
-
         print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
-
         train_loss, train_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
             _logits = MLP(X_batch, foo=1)
@@ -81,7 +73,6 @@ def forward(self, x, foo=None):
             n_iter += 1
         print("   train foo=1 loss: {}".format(train_loss / n_iter))
         print("   train foo=1 acc:  {}".format(train_acc / n_iter))
-
         val_loss, val_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=False):
             _logits = MLP(X_batch, foo=1)  # is_train=False, disable dropout
@@ -90,7 +81,6 @@ def forward(self, x, foo=None):
             n_iter += 1
         print("   val foo=1 loss: {}".format(val_loss / n_iter))
         print("   val foo=1 acc:  {}".format(val_acc / n_iter))
-
         val_loss, val_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=False):
             _logits = MLP(X_batch)  # is_train=False, disable dropout
diff --git a/examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py b/examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py
index e2d45943d..58695c8ac 100644
--- a/examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py
+++ b/examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py
@@ -1,8 +1,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input, LayerList
 from tensorlayer.models import Model
@@ -19,9 +19,7 @@ class CustomModelHidden(Model):
 
     def __init__(self):
         super(CustomModelHidden, self).__init__()
-
         self.dropout1 = Dropout(keep=0.8)  #(self.innet)
-
         self.seq = LayerList(
             [
                 Dense(n_units=800, act=tf.nn.relu, in_channels=784),
@@ -29,7 +27,6 @@ def __init__(self):
                 Dense(n_units=800, act=tf.nn.relu, in_channels=800),
             ]
         )
-
         self.dropout3 = Dropout(keep=0.8)  #(self.seq)
 
     def forward(self, x):
@@ -43,7 +40,6 @@ class CustomModelOut(Model):
 
     def __init__(self):
         super(CustomModelOut, self).__init__()
-
         self.dense3 = Dense(n_units=10, act=tf.nn.relu, in_channels=800)
 
     def forward(self, x, foo=None):
@@ -74,30 +70,23 @@ def forward(self, x, foo=None):
 for epoch in range(n_epoch):  ## iterate the dataset n_epoch times
     start_time = time.time()
     ## iterate over the entire training set once (shuffle the data via training)
-
     for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-
         MLP1.train()  # enable dropout
         MLP2.train()
-
         with tf.GradientTape() as tape:
             ## compute outputs
             _hidden = MLP1(X_batch)
             _logits = MLP2(_hidden, foo=1)
             ## compute loss and update model
             _loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
-
         grad = tape.gradient(_loss, train_weights)
         optimizer.apply_gradients(zip(grad, train_weights))
 
     ## use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-
         MLP1.eval()  # disable dropout
         MLP2.eval()
-
         print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
-
         train_loss, train_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
             _hidden = MLP1(X_batch)
diff --git a/examples/basic_tutorials/tutorial_mnist_mlp_static.py b/examples/basic_tutorials/tutorial_mnist_mlp_static.py
index bd85a12a9..358a0e561 100644
--- a/examples/basic_tutorials/tutorial_mnist_mlp_static.py
+++ b/examples/basic_tutorials/tutorial_mnist_mlp_static.py
@@ -1,8 +1,9 @@
+import pprint
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.models import Model
@@ -21,7 +22,7 @@
 def get_model(inputs_shape):
     ni = Input(inputs_shape)
     nn = Dropout(keep=0.8)(ni)
-    nn = Dense(n_units=800, act=tf.nn.relu)(nn)  
+    nn = Dense(n_units=800, act=tf.nn.relu)(nn)
     nn = Dropout(keep=0.8)(nn)
     nn = Dense(n_units=800, act=tf.nn.relu)(nn)
     nn = Dropout(keep=0.8)(nn)
@@ -31,7 +32,6 @@ def get_model(inputs_shape):
 
 
 MLP = get_model([None, 784])
-import pprint
 pprint.pprint(MLP.config)
 
 ## start training
@@ -45,31 +45,23 @@ def get_model(inputs_shape):
 for epoch in range(n_epoch):  ## iterate the dataset n_epoch times
     start_time = time.time()
     ## iterate over the entire training set once (shuffle the data via training)
-
     for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-
         MLP.train()  # enable dropout
-
         with tf.GradientTape() as tape:
             ## compute outputs
-            _logits = MLP(X_batch)  # alternatively, you can use MLP(x, is_train=True) and remove MLP.train()
+            _logits = MLP(X_batch)
             ## compute loss and update model
             _loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
-
         grad = tape.gradient(_loss, train_weights)
         optimizer.apply_gradients(zip(grad, train_weights))
 
     ## use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-
         MLP.eval()  # disable dropout
-
         print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
-
         train_loss, train_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
-
-            _logits = MLP(X_batch)  # alternatively, you can use MLP(x, is_train=False) and remove MLP.eval()
+            _logits = MLP(X_batch)
             train_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
             train_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
             n_iter += 1
diff --git a/examples/basic_tutorials/tutorial_mnist_mlp_static_2.py b/examples/basic_tutorials/tutorial_mnist_mlp_static_2.py
index 67a519e4a..a4110eafb 100644
--- a/examples/basic_tutorials/tutorial_mnist_mlp_static_2.py
+++ b/examples/basic_tutorials/tutorial_mnist_mlp_static_2.py
@@ -1,8 +1,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.models import Model
@@ -55,27 +55,20 @@ def get_model(inputs_shape, hmodel):
 for epoch in range(n_epoch):  ## iterate the dataset n_epoch times
     start_time = time.time()
     ## iterate over the entire training set once (shuffle the data via training)
-
     for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-
         MLP.train()  # enable dropout
-
         with tf.GradientTape() as tape:
             ## compute outputs
             _logits = MLP(X_batch)  # alternatively, you can use MLP(x, is_train=True) and remove MLP.train()
             ## compute loss and update model
             _loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
-
         grad = tape.gradient(_loss, train_weights)
         optimizer.apply_gradients(zip(grad, train_weights))
 
     ## use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-
         MLP.eval()  # disable dropout
-
         print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
-
         train_loss, train_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
 
@@ -85,7 +78,6 @@ def get_model(inputs_shape, hmodel):
             n_iter += 1
         print("   train loss: {}".format(train_loss / n_iter))
         print("   train acc:  {}".format(train_acc / n_iter))
-
         val_loss, val_acc, n_iter = 0, 0, 0
         for X_batch, y_batch in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=False):
             _logits = MLP(X_batch)  # is_train=False, disable dropout
diff --git a/examples/basic_tutorials/tutorial_mnist_siamese.py b/examples/basic_tutorials/tutorial_mnist_siamese.py
index e8d50ef94..236a40542 100644
--- a/examples/basic_tutorials/tutorial_mnist_siamese.py
+++ b/examples/basic_tutorials/tutorial_mnist_siamese.py
@@ -14,8 +14,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Flatten, Input
 from tensorlayer.models import Model
diff --git a/examples/basic_tutorials/tutorial_mnist_simple.py b/examples/basic_tutorials/tutorial_mnist_simple.py
index ceaee0c48..b1ccd052b 100644
--- a/examples/basic_tutorials/tutorial_mnist_simple.py
+++ b/examples/basic_tutorials/tutorial_mnist_simple.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tl.logging.set_verbosity(tl.logging.DEBUG)
diff --git a/examples/data_process/tutorial_fast_affine_transform.py b/examples/data_process/tutorial_fast_affine_transform.py
index 71890f5bd..9163bb0a7 100644
--- a/examples/data_process/tutorial_fast_affine_transform.py
+++ b/examples/data_process/tutorial_fast_affine_transform.py
@@ -21,11 +21,18 @@
 
 def create_transformation_matrix():
     # 1. Create required affine transformation matrices
-    M_rotate = tl.prepro.affine_rotation_matrix(angle=20)
-    M_flip = tl.prepro.affine_horizontal_flip_matrix(prob=1)
-    M_shift = tl.prepro.affine_shift_matrix(wrg=0.1, hrg=0, h=h, w=w)
-    M_shear = tl.prepro.affine_shear_matrix(x_shear=0.2, y_shear=0)
-    M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=0.8)
+    ## fixed
+    # M_rotate = tl.prepro.affine_rotation_matrix(angle=20)
+    # M_flip = tl.prepro.affine_horizontal_flip_matrix(prob=1)
+    # M_shift = tl.prepro.affine_shift_matrix(wrg=0.1, hrg=0, h=h, w=w)
+    # M_shear = tl.prepro.affine_shear_matrix(x_shear=0.2, y_shear=0)
+    # M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=0.8)
+    ## random
+    M_rotate = tl.prepro.affine_rotation_matrix(angle=(-20, 20))
+    M_flip = tl.prepro.affine_horizontal_flip_matrix(prob=0.5)
+    M_shift = tl.prepro.affine_shift_matrix(wrg=(-0.1,0.1), hrg=(-0.1,0.1), h=h, w=w)
+    M_shear = tl.prepro.affine_shear_matrix(x_shear=(-0.2,0.2), y_shear=(-0.2,0.2))
+    M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=(0.8,1.2))
 
     # 2. Combine matrices
     # NOTE: operations are applied in a reversed order (i.e., rotation is performed first)
@@ -55,7 +62,8 @@ def example2():
     st = time.time()
     for _ in range(100):  # Repeat 100 times and compute the averaged speed
         transform_matrix = create_transformation_matrix()
-        result = tl.prepro.affine_transform_cv2(image, transform_matrix)  # Transform the image using a single operation
+        result = tl.prepro.affine_transform_cv2(image, transform_matrix, border_mode='replicate')  # Transform the image using a single operation
+        tl.vis.save_image(result, '_result_fast_{}.png'.format(_))
     print("apply all transforms once took %fs for each image" % ((time.time() - st) / 100))  # usually 50x faster
     tl.vis.save_image(result, '_result_fast.png')
 
@@ -90,8 +98,8 @@ def _map_fn(image_path, target):
     dataset = tf.data.Dataset.from_generator(generator, output_types=(tf.string, tf.int64))
     dataset = dataset.shuffle(buffer_size=4096)  # shuffle before loading images
     dataset = dataset.repeat(n_epoch)
-    dataset = dataset.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
     dataset = dataset.batch(batch_size)  # TODO: consider using tf.contrib.map_and_batch
+    dataset = dataset.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
     dataset = dataset.prefetch(1)  # prefetch 1 batch
 
     n_step = 0
diff --git a/examples/data_process/tutorial_tf_dataset_voc.py b/examples/data_process/tutorial_tf_dataset_voc.py
index c3ac07e06..2f8c3a513 100644
--- a/examples/data_process/tutorial_tf_dataset_voc.py
+++ b/examples/data_process/tutorial_tf_dataset_voc.py
@@ -13,8 +13,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 
 # tf.logging.set_verbosity(tf.logging.DEBUG)
@@ -89,10 +89,10 @@ def _map_fn(filename, annotation):
 
 ds = tf.data.Dataset.from_generator(generator, output_types=(tf.string, tf.string))
 ds = ds.shuffle(shuffle_buffer_size)
-ds = ds.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
 ds = ds.repeat(n_epoch)
 ds = ds.prefetch(buffer_size=2048)
 ds = ds.batch(batch_size)
+ds = ds.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
 
 st = time.time()
 im, annbyte = next(iter(ds))
diff --git a/examples/data_process/tutorial_tfrecord.py b/examples/data_process/tutorial_tfrecord.py
index 6c5c38162..f6d0c7754 100644
--- a/examples/data_process/tutorial_tfrecord.py
+++ b/examples/data_process/tutorial_tfrecord.py
@@ -22,9 +22,9 @@
 import os
 
 import numpy as np
+import tensorflow as tf
 from PIL import Image
 
-import tensorflow as tf
 import tensorlayer as tl
 
 ## Save data ==================================================================
@@ -77,13 +77,11 @@
 def read_and_decode(filename):
     # generate a queue with a given file name
     raw_dataset = tf.data.TFRecordDataset([filename]).shuffle(1000).batch(4)
+    features = {}
     for serialized_example in raw_dataset:
-        features = tf.io.parse_example(
-            serialized_example, features={
-                'label': tf.io.FixedLenFeature([], tf.int64),
-                'img_raw': tf.io.FixedLenFeature([], tf.string),
-            }
-        )
+        features['label'] = tf.io.FixedLenFeature([], tf.int64)
+        features['img_raw'] = tf.io.FixedLenFeature([], tf.string)
+        features = tf.io.parse_example(serialized_example, features)
         # You can do more image distortion here for training data
         img_batch = tf.io.decode_raw(features['img_raw'], tf.uint8)
         img_batch = tf.reshape(img_batch, [4, 224, 224, 3])
diff --git a/examples/data_process/tutorial_tfrecord2.py b/examples/data_process/tutorial_tfrecord2.py
index 6997be251..aba05198c 100755
--- a/examples/data_process/tutorial_tfrecord2.py
+++ b/examples/data_process/tutorial_tfrecord2.py
@@ -14,10 +14,10 @@
 import os
 
 import numpy as np
-
 # import matplotlib
 # matplotlib.use('GTK')
 import tensorflow as tf
+
 import tensorlayer as tl
 
 # Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
@@ -63,13 +63,11 @@
 def read_and_decode(filename):
     batchsize = 4
     raw_dataset = tf.data.TFRecordDataset([filename]).shuffle(1000).batch(batchsize)
+    features = {}
     for serialized_example in raw_dataset:
-        features = tf.io.parse_example(
-            serialized_example, features={
-                'label': tf.io.FixedLenFeature([], tf.int64),
-                'img_raw': tf.io.FixedLenFeature([], tf.string),
-            }
-        )
+        features['label'] = tf.io.FixedLenFeature([], tf.int64)
+        features['img_raw'] = tf.io.FixedLenFeature([], tf.string)
+        features = tf.io.parse_example(serialized_example, features)
         # You can do more image distortion here for training data
         img_batch = tf.io.decode_raw(features['img_raw'], tf.uint8)
         img_batch = tf.reshape(img_batch, [-1, 32, 32, 3])
diff --git a/examples/data_process/tutorial_tfrecord3.py b/examples/data_process/tutorial_tfrecord3.py
index bc8752f2a..9e5751a25 100644
--- a/examples/data_process/tutorial_tfrecord3.py
+++ b/examples/data_process/tutorial_tfrecord3.py
@@ -19,9 +19,9 @@
 import os
 
 import numpy as np
+import tensorflow as tf
 from PIL import Image
 
-import tensorflow as tf
 import tensorlayer as tl
 
 
diff --git a/examples/database/dispatch_tasks.py b/examples/database/dispatch_tasks.py
index ff7f752ed..4c8c02e44 100644
--- a/examples/database/dispatch_tasks.py
+++ b/examples/database/dispatch_tasks.py
@@ -6,6 +6,7 @@
 import time
 
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tl.logging.set_verbosity(tl.logging.DEBUG)
diff --git a/examples/database/task_script.py b/examples/database/task_script.py
index 3d77102b1..3f2f93ccd 100644
--- a/examples/database/task_script.py
+++ b/examples/database/task_script.py
@@ -1,6 +1,7 @@
 """Sample task script."""
 
 import tensorflow as tf
+
 import tensorlayer as tl
 
 # tf.logging.set_verbosity(tf.logging.DEBUG)
diff --git a/examples/deprecated_tutorials/tutorial_imagenet_inceptionV3_distributed.py b/examples/deprecated_tutorials/tutorial_imagenet_inceptionV3_distributed.py
index 1c2801306..6c208f354 100644
--- a/examples/deprecated_tutorials/tutorial_imagenet_inceptionV3_distributed.py
+++ b/examples/deprecated_tutorials/tutorial_imagenet_inceptionV3_distributed.py
@@ -19,9 +19,7 @@
 from xml.etree import ElementTree
 
 import numpy as np
-
 import tensorflow as tf
-import tensorlayer as tl
 from tensorflow.contrib import slim
 from tensorflow.contrib.slim.python.slim.nets.inception_v3 import (inception_v3, inception_v3_arg_scope)
 from tensorflow.python.framework.errors_impl import OutOfRangeError
@@ -30,6 +28,8 @@
 from tensorflow.python.training.monitored_session import \
     SingularMonitoredSession
 
+import tensorlayer as tl
+
 tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
diff --git a/examples/deprecated_tutorials/tutorial_mnist_distributed.py b/examples/deprecated_tutorials/tutorial_mnist_distributed.py
index 18f7cdb92..29d291ba4 100644
--- a/examples/deprecated_tutorials/tutorial_mnist_distributed.py
+++ b/examples/deprecated_tutorials/tutorial_mnist_distributed.py
@@ -13,6 +13,7 @@
 """
 
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tf.logging.set_verbosity(tf.logging.DEBUG)
diff --git a/examples/distributed_training/tutorial_cifar10_distributed_trainer.py b/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
index 340e37b2f..830bf879b 100644
--- a/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
+++ b/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
@@ -15,8 +15,8 @@
 import multiprocessing
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import (BatchNormLayer, Conv2d, DenseLayer, FlattenLayer, InputLayer, MaxPool2d)
 
diff --git a/examples/distributed_training/tutorial_mnist_distributed_trainer.py b/examples/distributed_training/tutorial_mnist_distributed_trainer.py
index 0cf916370..0f1b8b6dd 100755
--- a/examples/distributed_training/tutorial_mnist_distributed_trainer.py
+++ b/examples/distributed_training/tutorial_mnist_distributed_trainer.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tf.logging.set_verbosity(tf.logging.DEBUG)
diff --git a/examples/keras_tfslim/tutorial_keras.py b/examples/keras_tfslim/tutorial_keras.py
index 9b877738c..9d0606c5f 100644
--- a/examples/keras_tfslim/tutorial_keras.py
+++ b/examples/keras_tfslim/tutorial_keras.py
@@ -4,8 +4,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Input, Lambda
 
diff --git a/examples/pretrained_cnn/tutorial_load_ckpt_weights_to_tensorlayer.py b/examples/pretrained_cnn/tutorial_load_ckpt_weights_to_tensorlayer.py
new file mode 100644
index 000000000..a3837f9fc
--- /dev/null
+++ b/examples/pretrained_cnn/tutorial_load_ckpt_weights_to_tensorlayer.py
@@ -0,0 +1,70 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorlayer as tl
+from tensorlayer.layers import (Input, Conv2d, Flatten, Dense, MaxPool2d)
+from tensorlayer.models import Model
+from tensorlayer.files import maybe_download_and_extract
+import numpy as np
+import tensorflow as tf
+
+filename = 'ckpt_parameters.zip'
+url_score = 'https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/'
+
+# download weights
+down_file = tl.files.maybe_download_and_extract(
+    filename=filename, working_directory='model/', url_source=url_score, extract=True
+)
+
+model_file = 'model/ckpt_parameters'
+
+# ckpt to npz, rename_key used to match TL naming rule
+tl.files.ckpt_to_npz_dict(model_file, rename_key=True)
+weights = np.load('model.npz', allow_pickle=True)
+
+# View the parameters and weights shape
+for key in weights.keys():
+    print(key, weights[key].shape)
+
+
+# build model
+def create_model(inputs_shape):
+    W_init = tl.initializers.truncated_normal(stddev=5e-2)
+    W_init2 = tl.initializers.truncated_normal(stddev=0.04)
+    ni = Input(inputs_shape)
+    nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, name='conv1_1')(ni)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1_1')(nn)
+    nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv1_2')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1_2')(nn)
+
+    nn = Conv2d(128, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv2_1')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2_1')(nn)
+    nn = Conv2d(128, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv2_2')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2_2')(nn)
+
+    nn = Conv2d(256, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv3_1')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool3_1')(nn)
+    nn = Conv2d(256, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv3_2')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool3_2')(nn)
+
+    nn = Conv2d(512, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv4_1')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool4_1')(nn)
+    nn = Conv2d(512, (3, 3), (1, 1), padding='SAME', act=tf.nn.relu, W_init=W_init, b_init=None, name='conv4_2')(nn)
+    nn = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool4_2')(nn)
+
+    nn = Flatten(name='flatten')(nn)
+    nn = Dense(1000, act=None, W_init=W_init2, name='output')(nn)
+
+    M = Model(inputs=ni, outputs=nn, name='cnn')
+    return M
+
+
+net = create_model([None, 224, 224, 3])
+# loaded weights whose name is not found in network's weights will be skipped.
+# If ckpt has the same naming rule as TL, We can restore the model with tl.files.load_and_assign_ckpt(model_dir=, network=, skip=True)
+tl.files.load_and_assign_npz_dict(network=net, skip=True)
+
+# you can use the following code to view the restore the model parameters.
+net_weights_name = [w.name for w in net.all_weights]
+for i in range(len(net_weights_name)):
+    print(net_weights_name[i], net.all_weights[net_weights_name.index(net_weights_name[i])])
diff --git a/examples/pretrained_cnn/tutorial_models_mobilenetv1.py b/examples/pretrained_cnn/tutorial_models_mobilenetv1.py
index 6b797a075..8d7b35a6b 100644
--- a/examples/pretrained_cnn/tutorial_models_mobilenetv1.py
+++ b/examples/pretrained_cnn/tutorial_models_mobilenetv1.py
@@ -10,8 +10,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/pretrained_cnn/tutorial_models_resnet50.py b/examples/pretrained_cnn/tutorial_models_resnet50.py
index b5055cee3..b8f8b1c28 100644
--- a/examples/pretrained_cnn/tutorial_models_resnet50.py
+++ b/examples/pretrained_cnn/tutorial_models_resnet50.py
@@ -8,8 +8,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/pretrained_cnn/tutorial_models_squeezenetv1.py b/examples/pretrained_cnn/tutorial_models_squeezenetv1.py
index 755d6c28b..9b6ee4e7f 100644
--- a/examples/pretrained_cnn/tutorial_models_squeezenetv1.py
+++ b/examples/pretrained_cnn/tutorial_models_squeezenetv1.py
@@ -5,8 +5,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/pretrained_cnn/tutorial_models_vgg16.py b/examples/pretrained_cnn/tutorial_models_vgg16.py
index 7749d5391..7d224c235 100644
--- a/examples/pretrained_cnn/tutorial_models_vgg16.py
+++ b/examples/pretrained_cnn/tutorial_models_vgg16.py
@@ -5,8 +5,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/pretrained_cnn/tutorial_models_vgg19.py b/examples/pretrained_cnn/tutorial_models_vgg19.py
index 09f2afa22..3f04fe9b3 100644
--- a/examples/pretrained_cnn/tutorial_models_vgg19.py
+++ b/examples/pretrained_cnn/tutorial_models_vgg19.py
@@ -5,8 +5,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/pretrained_cnn/tutorial_models_vgg_static.py b/examples/pretrained_cnn/tutorial_models_vgg_static.py
index 0e73b82ef..e5644395f 100644
--- a/examples/pretrained_cnn/tutorial_models_vgg_static.py
+++ b/examples/pretrained_cnn/tutorial_models_vgg_static.py
@@ -5,8 +5,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names
 
diff --git a/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py b/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py
index d3205045a..5fdd332ae 100644
--- a/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py
+++ b/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py
@@ -39,235 +39,180 @@
 
 """
 
-import os
+import multiprocessing
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (
+    BinaryConv2d, BinaryDense, Conv2d, Dense, Flatten, Input, LocalResponseNorm, MaxPool2d, Sign
+)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-model_file_name = "./model_cifar10_tfrecord.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
 # Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
+# prepare cifar10 data
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
 
-print('X_train.shape', X_train.shape)  # (50000, 32, 32, 3)
-print('y_train.shape', y_train.shape)  # (50000,)
-print('X_test.shape', X_test.shape)  # (10000, 32, 32, 3)
-print('y_test.shape', y_test.shape)  # (10000,)
-print('X %s   y %s' % (X_test.dtype, y_test.dtype))
-
-
-def data_to_tfrecord(images, labels, filename):
-    """Save data into TFRecord."""
-    if os.path.isfile(filename):
-        print("%s exists" % filename)
-        return
-    print("Converting data into %s ..." % filename)
-    # cwd = os.getcwd()
-    writer = tf.python_io.TFRecordWriter(filename)
-    for index, img in enumerate(images):
-        img_raw = img.tobytes()
-        # Visualize a image
-        # tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        label = int(labels[index])
-        # print(label)
-        # Convert the bytes back to image as follow:
-        # image = Image.frombytes('RGB', (32, 32), img_raw)
-        # image = np.fromstring(img_raw, np.float32)
-        # image = image.reshape([32, 32, 3])
-        # tl.visualize.frame(np.asarray(image, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        example = tf.train.Example(
-            features=tf.train.Features(
-                feature={
-                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
-                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
-                }
-            )
-        )
-        writer.write(example.SerializeToString())  # Serialize To String
-    writer.close()
-
-
-def read_and_decode(filename, is_train=None):
-    """Return tensor to read from TFRecord."""
-    filename_queue = tf.train.string_input_producer([filename])
-    reader = tf.TFRecordReader()
-    _, serialized_example = reader.read(filename_queue)
-    features = tf.parse_single_example(
-        serialized_example, features={
-            'label': tf.FixedLenFeature([], tf.int64),
-            'img_raw': tf.FixedLenFeature([], tf.string),
-        }
-    )
-    # You can do more image distortion here for training data
-    img = tf.decode_raw(features['img_raw'], tf.float32)
-    img = tf.reshape(img, [32, 32, 3])
-    # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
-        # 1. Randomly crop a [height, width] section of the image.
-        img = tf.random_crop(img, [24, 24, 3])
-
-        # 2. Randomly flip the image horizontally.
-        img = tf.image.random_flip_left_right(img)
-
-        # 3. Randomly change brightness.
-        img = tf.image.random_brightness(img, max_delta=63)
-
-        # 4. Randomly change contrast.
-        img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
-
-        # 5. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == False:
-        # 1. Crop the central [height, width] of the image.
-        img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
-
-        # 2. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == None:
-        img = img
-
-    label = tf.cast(features['label'], tf.int32)
-    return img, label
-
-
-# Save data into TFRecord files
-data_to_tfrecord(images=X_train, labels=y_train, filename="train.cifar10")
-data_to_tfrecord(images=X_test, labels=y_test, filename="test.cifar10")
 
+def binary_model(input_shape, n_classes):
+    in_net = Input(shape=input_shape, name='input')
+
+    net = Conv2d(64, (5, 5), (1, 1), act='relu', padding='SAME', name='conv1')(in_net)
+    net = Sign(name='sign1')(net)
+
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm1')(net)
+    net = BinaryConv2d(64, (5, 5), (1, 1), act='relu', padding='SAME', name='bconv1')(net)
+
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm2')(net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
+    net = Flatten(name='flatten')(net)
+    net = Sign(name='sign2')(net)
+    net = BinaryDense(384, act='relu', name='d1relu')(net)
+    net = Sign(name='sign3')(net)
+    net = BinaryDense(192, act='relu', name='d2relu')(net)
+    net = Dense(n_classes, act=None, name='output')(net)
+    net = Model(inputs=in_net, outputs=net, name='binarynet')
+    return net
+
+
+# training settings
+net = binary_model([None, 24, 24, 3], n_classes=10)
 batch_size = 128
-model_file_name = "./model_cifar10_advanced.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
-with tf.device('/cpu:0'):
-    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
-    # prepare data in cpu
-    x_train_, y_train_ = read_and_decode("train.cifar10", True)
-    x_test_, y_test_ = read_and_decode("test.cifar10", False)
-    # set the number of threads here
-    x_train_batch, y_train_batch = tf.train.shuffle_batch(
-        [x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32
-    )
-    # for testing, uses batch instead of shuffle_batch
-    x_test_batch, y_test_batch = tf.train.batch(
-        [x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32
-    )
-
-    def model(x_crop, y_, reuse):
-        """For more simplified CNN APIs, check tensorlayer.org."""
-        with tf.variable_scope("model", reuse=reuse):
-            net = tl.layers.InputLayer(x_crop, name='input')
-            net = tl.layers.Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn1')
-            net = tl.layers.SignLayer(net)
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm1')
-            net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn2')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm2')
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-            net = tl.layers.FlattenLayer(net, name='flatten')
-            net = tl.layers.SignLayer(net)
-            net = tl.layers.BinaryDenseLayer(net, 384, act=tf.nn.relu, name='d1relu')
-            net = tl.layers.SignLayer(net)
-            net = tl.layers.BinaryDenseLayer(net, 192, act=tf.nn.relu, name='d2relu')
-            net = tl.layers.DenseLayer(net, 10, act=None, name='output')
-
-            y = net.outputs
-
-            ce = tl.cost.cross_entropy(y, y_, name='cost')
-            # L2 for the MLP, without this, the accuracy will be reduced by 15%.
-            L2 = 0
-            for p in tl.layers.get_variables_with_name('relu/W', True, True):
-                L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
-            cost = ce + L2
-
-            # correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(y), 1), y_)
-            correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), tf.int32), y_)
-            acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-
-            return net, cost, acc
-
-    # You can also use placeholder to feed_dict in data after using
-    # val, l = sess.run([x_train_batch, y_train_batch]) to get the data
-    # x_crop = tf.placeholder(tf.float32, shape=[batch_size, 24, 24, 3])
-    # y_ = tf.placeholder(tf.int32, shape=[batch_size,])
-    # cost, acc, network = model(x_crop, y_, None)
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        network, cost, acc, = model(x_train_batch, y_train_batch, False)
-        _, cost_test, acc_test = model(x_test_batch, y_test_batch, True)
-
-    # train
-    n_epoch = 50000
-    learning_rate = 0.0001
-    print_freq = 1
-    n_step_epoch = int(len(y_train) / batch_size)
-    n_step = n_epoch * n_step_epoch
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
-
-    sess.run(tf.global_variables_initializer())
-    if resume:
-        print("Load existing model " + "!" * 10)
-        saver = tf.train.Saver()
-        saver.restore(sess, model_file_name)
-
-    network.print_params(False)
-    network.print_layers()
-
-    print('   learning_rate: %f' % learning_rate)
-    print('   batch_size: %d' % batch_size)
-    print('   n_epoch: %d, step in an epoch: %d, total n_step: %d' % (n_epoch, n_step_epoch, n_step))
-
-    coord = tf.train.Coordinator()
-    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-    step = 0
-    for epoch in range(n_epoch):
-        start_time = time.time()
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for s in range(n_step_epoch):
-            # You can also use placeholder to feed_dict in data after using
-            # val, l = sess.run([x_train_batch, y_train_batch])
-            # tl.visualize.images2d(val, second=3, saveable=False, name='batch', dtype=np.uint8, fig_idx=2020121)
-            # err, ac, _ = sess.run([cost, acc, train_op], feed_dict={x_crop: val, y_: l})
-            err, ac, _ = sess.run([cost, acc, train_op])
-            step += 1
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
-
-        if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-            print(
-                "Epoch %d : Step %d-%d of %d took %fs" %
-                (epoch, step, step + n_step_epoch, n_step, time.time() - start_time)
-            )
-            print("   train loss: %f" % (train_loss / n_batch))
-            print("   train acc: %f" % (train_acc / n_batch))
-
-            test_loss, test_acc, n_batch = 0, 0, 0
-            for _ in range(int(len(y_test) / batch_size)):
-                err, ac = sess.run([cost_test, acc_test])
-                test_loss += err
-                test_acc += ac
-                n_batch += 1
-            print("   test loss: %f" % (test_loss / n_batch))
-            print("   test acc: %f" % (test_acc / n_batch))
-
-        if (epoch + 1) % (print_freq * 50) == 0:
-            print("Save model " + "!" * 10)
-            saver = tf.train.Saver()
-            save_path = saver.save(sess, model_file_name)
-            # you can also save model into npz
-            tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
-            # and restore it as follow:
-            # tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
-
-    coord.request_stop()
-    coord.join(threads)
-    sess.close()
+n_epoch = 50000
+learning_rate = 0.0001
+print_freq = 5
+n_step_epoch = int(len(y_train) / batch_size)
+n_step = n_epoch * n_step_epoch
+shuffle_buffer_size = 128
+
+train_weights = net.trainable_weights
+optimizer = tf.optimizers.Adam(learning_rate)
+cost = tl.cost.cross_entropy
+
+
+def generator_train():
+    inputs = X_train
+    targets = y_train
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def generator_test():
+    inputs = X_test
+    targets = y_test
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def _map_fn_train(img, target):
+    # 1. Randomly crop a [height, width] section of the image.
+    img = tf.image.random_crop(img, [24, 24, 3])
+    # 2. Randomly flip the image horizontally.
+    img = tf.image.random_flip_left_right(img)
+    # 3. Randomly change brightness.
+    img = tf.image.random_brightness(img, max_delta=63)
+    # 4. Randomly change contrast.
+    img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
+    # 5. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _map_fn_test(img, target):
+    # 1. Crop the central [height, width] of the image.
+    img = tf.image.resize_with_pad(img, 24, 24)
+    # 2. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    img = tf.reshape(img, (24, 24, 3))
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
+
+
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+
+
+# dataset API and augmentation
+train_ds = tf.data.Dataset.from_generator(
+    generator_train, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# train_ds = train_ds.repeat(n_epoch)
+train_ds = train_ds.shuffle(shuffle_buffer_size)
+train_ds = train_ds.prefetch(buffer_size=4096)
+train_ds = train_ds.batch(batch_size)
+train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
+# value = train_ds.make_one_shot_iterator().get_next()
+
+test_ds = tf.data.Dataset.from_generator(
+    generator_test, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# test_ds = test_ds.shuffle(shuffle_buffer_size)
+# test_ds = test_ds.repeat(n_epoch)
+test_ds = test_ds.prefetch(buffer_size=4096)
+test_ds = test_ds.batch(batch_size)
+test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
+# value_test = test_ds.make_one_shot_iterator().get_next()
+
+for epoch in range(n_epoch):
+    start_time = time.time()
+
+    train_loss, train_acc, n_iter = 0, 0, 0
+    for X_batch, y_batch in train_ds:
+        net.train()
+        _loss, acc = _train_step(net, X_batch, y_batch, cost=cost, train_op=optimizer, acc=accuracy)
+
+        train_loss += _loss
+        train_acc += acc
+        n_iter += 1
+
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+    # use training and evaluation sets to evaluate the model every print_freq epoch
+    if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
+        net.eval()
+        val_loss, val_acc, n_val_iter = 0, 0, 0
+        for X_batch, y_batch in test_ds:
+            _logits = net(X_batch)  # is_train=False, disable dropout
+            val_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+            n_val_iter += 1
+        print("   val loss: {}".format(val_loss / n_val_iter))
+        print("   val acc:  {}".format(val_acc / n_val_iter))
+
+# use testing data to evaluate the model
+net.eval()
+test_loss, test_acc, n_iter = 0, 0, 0
+for X_batch, y_batch in test_ds:
+    _logits = net(X_batch)
+    test_loss += tl.cost.cross_entropy(_logits, y_batch, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+    n_iter += 1
+print("   test loss: {}".format(test_loss / n_iter))
+print("   test acc:  {}".format(test_acc / n_iter))
diff --git a/examples/quantized_net/tutorial_binarynet_mnist_cnn.py b/examples/quantized_net/tutorial_binarynet_mnist_cnn.py
index 84fbf7fc9..4eccd5c2e 100644
--- a/examples/quantized_net/tutorial_binarynet_mnist_cnn.py
+++ b/examples/quantized_net/tutorial_binarynet_mnist_cnn.py
@@ -3,109 +3,104 @@
 
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (BatchNorm, BinaryConv2d, BinaryDense, Flatten, Input, MaxPool2d, Sign)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
 X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
-# X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
-
-sess = tf.InteractiveSession()
 
 batch_size = 128
 
-x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
-y_ = tf.placeholder(tf.int64, shape=[batch_size])
 
-
-def model(x, is_train=True, reuse=False):
+def model(inputs_shape, n_class=10):
     # In BNN, all the layers inputs are binary, with the exception of the first layer.
     # ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
-    with tf.variable_scope("binarynet", reuse=reuse):
-        net = tl.layers.InputLayer(x, name='input')
-        net = tl.layers.BinaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
-
-        net = tl.layers.SignLayer(net)
-        net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
-
-        net = tl.layers.FlattenLayer(net)
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
-        net = tl.layers.SignLayer(net)
-        net = tl.layers.BinaryDenseLayer(net, 256, b_init=None, name='dense')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn3')
-
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
-        net = tl.layers.SignLayer(net)
-        net = tl.layers.BinaryDenseLayer(net, 10, b_init=None, name='bout')
-        net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bno')
+    net_in = Input(inputs_shape, name='input')
+    net = BinaryConv2d(32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')(net_in)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn1')(net)
+
+    net = Sign("sign1")(net)
+    net = BinaryConv2d(64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')(net)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn2')(net)
+
+    net = Flatten('ft')(net)
+    net = Sign("sign2")(net)
+    net = BinaryDense(256, b_init=None, name='dense')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn3')(net)
+
+    net = Sign("sign3")(net)
+    net = BinaryDense(10, b_init=None, name='bout')(net)
+    net = BatchNorm(name='bno')(net)
+    net = Model(inputs=net_in, outputs=net, name='binarynet')
     return net
 
 
-# define inferences
-net_train = model(x, is_train=True, reuse=False)
-net_test = model(x, is_train=False, reuse=True)
-
-# cost for training
-y = net_train.outputs
-cost = tl.cost.cross_entropy(y, y_, name='xentropy')
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
 
-# cost and accuracy for evalution
-y2 = net_test.outputs
-cost_test = tl.cost.cross_entropy(y2, y_, name='xentropy2')
-correct_prediction = tf.equal(tf.argmax(y2, 1), y_)
-acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
-# define the optimizer
-train_params = tl.layers.get_variables_with_name('binarynet', True, True)
-train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
 
-# initialize all variables in the session
-sess.run(tf.global_variables_initializer())
-
-net_train.print_params()
-net_train.print_layers()
 
 n_epoch = 200
 print_freq = 5
 
-# print(sess.run(net_test.all_params)) # print real values of parameters
+net = model([None, 28, 28, 1])
+train_op = tf.optimizers.Adam(learning_rate=0.0001)
+cost = tl.cost.cross_entropy
 
 for epoch in range(n_epoch):
     start_time = time.time()
+    train_loss, train_acc, n_batch = 0, 0, 0
+    net.train()
+
     for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-        sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
+        _loss, acc = _train_step(net, X_train_a, y_train_a, cost=cost, train_op=train_op, acc=accuracy)
+        train_loss += _loss
+        train_acc += acc
+        n_batch += 1
+
+        # print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
+        # print("   train loss: %f" % (train_loss / n_batch))
+        # print("   train acc: %f" % (train_acc / n_batch))
 
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
         print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
         print("   train loss: %f" % (train_loss / n_batch))
         print("   train acc: %f" % (train_acc / n_batch))
-        val_loss, val_acc, n_batch = 0, 0, 0
+        val_loss, val_acc, val_batch = 0, 0, 0
+        net.eval()
         for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_val_a, y_: y_val_a})
-            val_loss += err
-            val_acc += ac
-            n_batch += 1
-        print("   val loss: %f" % (val_loss / n_batch))
-        print("   val acc: %f" % (val_acc / n_batch))
-
-print('Evaluation')
-test_loss, test_acc, n_batch = 0, 0, 0
+            _logits = net(X_val_a)
+            val_loss += tl.cost.cross_entropy(_logits, y_val_a, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_val_a))
+            val_batch += 1
+        print("   val loss: {}".format(val_loss / val_batch))
+        print("   val acc:  {}".format(val_acc / val_batch))
+
+net.test()
+test_loss, test_acc, n_test_batch = 0, 0, 0
 for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True):
-    err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
-    test_loss += err
-    test_acc += ac
-    n_batch += 1
-print("   test loss: %f" % (test_loss / n_batch))
-print("   test acc: %f" % (test_acc / n_batch))
+    _logits = net(X_test_a)
+    test_loss += tl.cost.cross_entropy(_logits, y_test_a, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_test_a))
+    n_test_batch += 1
+print("   test loss: %f" % (test_loss / n_test_batch))
+print("   test acc: %f" % (test_acc / n_test_batch))
diff --git a/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py b/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py
index fe7666bab..10c624c24 100644
--- a/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py
+++ b/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py
@@ -39,231 +39,173 @@
 
 """
 
-import os
+import multiprocessing
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (Conv2d, Dense, DorefaConv2d, DorefaDense, Flatten, Input, LocalResponseNorm, MaxPool2d)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-model_file_name = "./model_cifar10_tfrecord.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
 # Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
+# prepare cifar10 data
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
 
-print('X_train.shape', X_train.shape)  # (50000, 32, 32, 3)
-print('y_train.shape', y_train.shape)  # (50000,)
-print('X_test.shape', X_test.shape)  # (10000, 32, 32, 3)
-print('y_test.shape', y_test.shape)  # (10000,)
-print('X %s   y %s' % (X_test.dtype, y_test.dtype))
-
-
-def data_to_tfrecord(images, labels, filename):
-    """Save data into TFRecord."""
-    if os.path.isfile(filename):
-        print("%s exists" % filename)
-        return
-    print("Converting data into %s ..." % filename)
-    # cwd = os.getcwd()
-    writer = tf.python_io.TFRecordWriter(filename)
-    for index, img in enumerate(images):
-        img_raw = img.tobytes()
-        # Visualize a image
-        # tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        label = int(labels[index])
-        # print(label)
-        # Convert the bytes back to image as follow:
-        # image = Image.frombytes('RGB', (32, 32), img_raw)
-        # image = np.fromstring(img_raw, np.float32)
-        # image = image.reshape([32, 32, 3])
-        # tl.visualize.frame(np.asarray(image, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        example = tf.train.Example(
-            features=tf.train.Features(
-                feature={
-                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
-                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
-                }
-            )
-        )
-        writer.write(example.SerializeToString())  # Serialize To String
-    writer.close()
-
-
-def read_and_decode(filename, is_train=None):
-    """Return tensor to read from TFRecord."""
-    filename_queue = tf.train.string_input_producer([filename])
-    reader = tf.TFRecordReader()
-    _, serialized_example = reader.read(filename_queue)
-    features = tf.parse_single_example(
-        serialized_example, features={
-            'label': tf.FixedLenFeature([], tf.int64),
-            'img_raw': tf.FixedLenFeature([], tf.string),
-        }
-    )
-    # You can do more image distortion here for training data
-    img = tf.decode_raw(features['img_raw'], tf.float32)
-    img = tf.reshape(img, [32, 32, 3])
-    # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
-        # 1. Randomly crop a [height, width] section of the image.
-        img = tf.random_crop(img, [24, 24, 3])
-
-        # 2. Randomly flip the image horizontally.
-        img = tf.image.random_flip_left_right(img)
-
-        # 3. Randomly change brightness.
-        img = tf.image.random_brightness(img, max_delta=63)
-
-        # 4. Randomly change contrast.
-        img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
-
-        # 5. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == False:
-        # 1. Crop the central [height, width] of the image.
-        img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
-
-        # 2. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == None:
-        img = img
-
-    label = tf.cast(features['label'], tf.int32)
-    return img, label
-
-
-# Save data into TFRecord files
-data_to_tfrecord(images=X_train, labels=y_train, filename="train.cifar10")
-data_to_tfrecord(images=X_test, labels=y_test, filename="test.cifar10")
 
+def dorefanet_model(input_shape, n_classes):
+    in_net = Input(shape=input_shape, name='input')
+    net = Conv2d(32, (5, 5), (1, 1), act='relu', padding='SAME', name='conv1')(in_net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm1')(net)
+    net = tl.layers.Sign("sign")(net)
+    net = DorefaConv2d(8, 32, 64, (5, 5), (1, 1), act='relu', padding='SAME', name='DorefaConv1')(net)
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm2')(net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
+    net = Flatten(name='flatten')(net)
+    net = DorefaDense(8, 16, 384, act='relu', name='DorefaDense1')(net)
+    net = DorefaDense(8, 16, 192, act='relu', name='DorefaDense2')(net)
+    net = Dense(n_classes, act=None, name='output')(net)
+    net = Model(inputs=in_net, outputs=net, name='dorefanet')
+    return net
+
+
+# training settings
+net = dorefanet_model([None, 24, 24, 3], n_classes=10)
 batch_size = 128
-model_file_name = "./model_cifar10_advanced.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
-with tf.device('/cpu:0'):
-    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
-    # prepare data in cpu
-    x_train_, y_train_ = read_and_decode("train.cifar10", True)
-    x_test_, y_test_ = read_and_decode("test.cifar10", False)
-    # set the number of threads here
-    x_train_batch, y_train_batch = tf.train.shuffle_batch(
-        [x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32
-    )
-    # for testing, uses batch instead of shuffle_batch
-    x_test_batch, y_test_batch = tf.train.batch(
-        [x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32
-    )
-
-    def model(x_crop, y_, reuse):
-        """For more simplified CNN APIs, check tensorlayer.org."""
-        with tf.variable_scope("model", reuse=reuse):
-            net = tl.layers.InputLayer(x_crop, name='input')
-            net = tl.layers.Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn1')
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm1')
-            net = tl.layers.DorefaConv2d(net, 1, 3, 64, (5, 5), (1, 1), tf.nn.relu, padding='SAME', name='cnn2')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm2')
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-            net = tl.layers.FlattenLayer(net, name='flatten')
-            net = tl.layers.DorefaDenseLayer(net, 1, 3, 384, act=tf.nn.relu, name='d1relu')
-            net = tl.layers.DorefaDenseLayer(net, 1, 3, 192, act=tf.nn.relu, name='d2relu')
-            net = tl.layers.DenseLayer(net, 10, act=None, name='output')
-            y = net.outputs
-
-            ce = tl.cost.cross_entropy(y, y_, name='cost')
-            # L2 for the MLP, without this, the accuracy will be reduced by 15%.
-            L2 = 0
-            for p in tl.layers.get_variables_with_name('relu/W', True, True):
-                L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
-            cost = ce + L2
-
-            # correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(y), 1), y_)
-            correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), tf.int32), y_)
-            acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-
-            return net, cost, acc
-
-    # You can also use placeholder to feed_dict in data after using
-    # val, l = sess.run([x_train_batch, y_train_batch]) to get the data
-    # x_crop = tf.placeholder(tf.float32, shape=[batch_size, 24, 24, 3])
-    # y_ = tf.placeholder(tf.int32, shape=[batch_size,])
-    # cost, acc, network = model(x_crop, y_, None)
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        network, cost, acc, = model(x_train_batch, y_train_batch, False)
-        _, cost_test, acc_test = model(x_test_batch, y_test_batch, True)
-
-    # train
-    n_epoch = 50000
-    learning_rate = 0.0001
-    print_freq = 1
-    n_step_epoch = int(len(y_train) / batch_size)
-    n_step = n_epoch * n_step_epoch
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
-
-    sess.run(tf.global_variables_initializer())
-    if resume:
-        print("Load existing model " + "!" * 10)
-        saver = tf.train.Saver()
-        saver.restore(sess, model_file_name)
-
-    network.print_params(False)
-    network.print_layers()
-
-    print('   learning_rate: %f' % learning_rate)
-    print('   batch_size: %d' % batch_size)
-    print('   n_epoch: %d, step in an epoch: %d, total n_step: %d' % (n_epoch, n_step_epoch, n_step))
-
-    coord = tf.train.Coordinator()
-    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-    step = 0
-    for epoch in range(n_epoch):
-        start_time = time.time()
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for s in range(n_step_epoch):
-            # You can also use placeholder to feed_dict in data after using
-            # val, l = sess.run([x_train_batch, y_train_batch])
-            # tl.visualize.images2d(val, second=3, saveable=False, name='batch', dtype=np.uint8, fig_idx=2020121)
-            # err, ac, _ = sess.run([cost, acc, train_op], feed_dict={x_crop: val, y_: l})
-            err, ac, _ = sess.run([cost, acc, train_op])
-            step += 1
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
-
-        if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-            print(
-                "Epoch %d : Step %d-%d of %d took %fs" %
-                (epoch, step, step + n_step_epoch, n_step, time.time() - start_time)
-            )
-            print("   train loss: %f" % (train_loss / n_batch))
-            print("   train acc: %f" % (train_acc / n_batch))
-
-            test_loss, test_acc, n_batch = 0, 0, 0
-            for _ in range(int(len(y_test) / batch_size)):
-                err, ac = sess.run([cost_test, acc_test])
-                test_loss += err
-                test_acc += ac
-                n_batch += 1
-            print("   test loss: %f" % (test_loss / n_batch))
-            print("   test acc: %f" % (test_acc / n_batch))
-
-        if (epoch + 1) % (print_freq * 50) == 0:
-            print("Save model " + "!" * 10)
-            saver = tf.train.Saver()
-            save_path = saver.save(sess, model_file_name)
-            # you can also save model into npz
-            tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
-            # and restore it as follow:
-            # tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
-
-    coord.request_stop()
-    coord.join(threads)
-    sess.close()
+n_epoch = 50000
+learning_rate = 0.0001
+print_freq = 5
+n_step_epoch = int(len(y_train) / batch_size)
+n_step = n_epoch * n_step_epoch
+shuffle_buffer_size = 128
+
+optimizer = tf.optimizers.Adam(learning_rate)
+# optimizer = tf.optimizers.SGD(learning_rate)
+cost = tl.cost.cross_entropy
+
+
+def generator_train():
+    inputs = X_train
+    targets = y_train
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def generator_test():
+    inputs = X_test
+    targets = y_test
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def _map_fn_train(img, target):
+    # 1. Randomly crop a [height, width] section of the image.
+    img = tf.image.random_crop(img, [24, 24, 3])
+    # 2. Randomly flip the image horizontally.
+    img = tf.image.random_flip_left_right(img)
+    # 3. Randomly change brightness.
+    img = tf.image.random_brightness(img, max_delta=63)
+    # 4. Randomly change contrast.
+    img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
+    # 5. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _map_fn_test(img, target):
+    # 1. Crop the central [height, width] of the image.
+    img = tf.image.resize_with_pad(img, 24, 24)
+    # 2. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    img = tf.reshape(img, (24, 24, 3))
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
+
+
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+
+
+# dataset API and augmentation
+train_ds = tf.data.Dataset.from_generator(
+    generator_train, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# train_ds = train_ds.repeat(n_epoch)
+train_ds = train_ds.shuffle(shuffle_buffer_size)
+train_ds = train_ds.prefetch(buffer_size=4096)
+train_ds = train_ds.batch(batch_size)
+train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
+# value = train_ds.make_one_shot_iterator().get_next()
+
+test_ds = tf.data.Dataset.from_generator(
+    generator_test, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# test_ds = test_ds.shuffle(shuffle_buffer_size)
+# test_ds = test_ds.repeat(n_epoch)
+test_ds = test_ds.prefetch(buffer_size=4096)
+test_ds = test_ds.batch(batch_size)
+test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
+# value_test = test_ds.make_one_shot_iterator().get_next()
+
+for epoch in range(n_epoch):
+    start_time = time.time()
+
+    train_loss, train_acc, n_iter = 0, 0, 0
+    net.train()
+    for X_batch, y_batch in train_ds:
+        _loss, acc = _train_step(net, X_batch, y_batch, cost=cost, train_op=optimizer, acc=accuracy)
+
+        train_loss += _loss
+        train_acc += acc
+        n_iter += 1
+
+    # use training and evaluation sets to evaluate the model every print_freq epoch
+    if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+        net.eval()
+        val_loss, val_acc, n_val_iter = 0, 0, 0
+        for X_batch, y_batch in test_ds:
+            _logits = net(X_batch)  # is_train=False, disable dropout
+            val_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+            n_val_iter += 1
+        print("   val loss: {}".format(val_loss / n_val_iter))
+        print("   val acc:  {}".format(val_acc / n_val_iter))
+
+# use testing data to evaluate the model
+net.eval()
+test_loss, test_acc, n_iter = 0, 0, 0
+for X_batch, y_batch in test_ds:
+    _logits = net(X_batch)
+    test_loss += tl.cost.cross_entropy(_logits, y_batch, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+    n_iter += 1
+print("   test loss: {}".format(test_loss / n_iter))
+print("   test acc:  {}".format(test_acc / n_iter))
diff --git a/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py b/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py
index d8cab9bc8..1cfd68124 100644
--- a/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py
+++ b/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py
@@ -3,109 +3,99 @@
 
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (BatchNorm, Dense, DorefaConv2d, DorefaDense, Flatten, Input, MaxPool2d)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
 X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
-# X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
-
-sess = tf.InteractiveSession()
 
 batch_size = 128
 
-x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
-y_ = tf.placeholder(tf.int64, shape=[batch_size])
-
-
-def model(x, is_train=True, reuse=False):
-    # In BNN, all the layers inputs are binary, with the exception of the first layer.
-    # ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
-    with tf.variable_scope("binarynet", reuse=reuse):
-        net = tl.layers.InputLayer(x, name='input')
-        net = tl.layers.DorefaConv2d(net, 1, 3, 32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')  #pylint: disable=bare-except
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
-
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.DorefaConv2d(net, 1, 3, 64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')  #pylint: disable=bare-except
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
-
-        net = tl.layers.FlattenLayer(net)
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.DorefaDenseLayer(net, 1, 3, 256, b_init=None, name='dense')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn3')
-
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.DenseLayer(net, 10, b_init=None, name='bout')
-        net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bno')
-    return net
 
+def model(inputs_shape, n_class=10):
+    in_net = Input(inputs_shape, name='input')
+    net = DorefaConv2d(1, 3, 32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')(in_net)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn1')(net)
+
+    net = DorefaConv2d(1, 3, 64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')(net)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn2')(net)
 
-# define inferences
-net_train = model(x, is_train=True, reuse=False)
-net_test = model(x, is_train=False, reuse=True)
+    net = Flatten('flatten')(net)
+    net = DorefaDense(1, 3, 256, b_init=None, name='dense')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn3')(net)
+
+    net = Dense(n_class, b_init=None, name='bout')(net)
+    net = BatchNorm(name='bno')(net)
+    net = Model(inputs=in_net, outputs=net, name='dorefanet')
+    return net
 
-# cost for training
-y = net_train.outputs
-cost = tl.cost.cross_entropy(y, y_, name='xentropy')
 
-# cost and accuracy for evalution
-y2 = net_test.outputs
-cost_test = tl.cost.cross_entropy(y2, y_, name='xentropy2')
-correct_prediction = tf.equal(tf.argmax(y2, 1), y_)
-acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
 
-# define the optimizer
-train_params = tl.layers.get_variables_with_name('binarynet', True, True)
-train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
 
-# initialize all variables in the session
-sess.run(tf.global_variables_initializer())
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
 
-net_train.print_params()
-net_train.print_layers()
 
 n_epoch = 200
 print_freq = 5
 
-# print(sess.run(net_test.all_params)) # print real values of parameters
+net = model([None, 28, 28, 1])
+train_op = tf.optimizers.Adam(learning_rate=0.0001)
+cost = tl.cost.cross_entropy
 
 for epoch in range(n_epoch):
     start_time = time.time()
+    train_loss, train_acc, n_batch = 0, 0, 0
+    net.train()
+
     for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-        sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
+        _loss, acc = _train_step(net, X_train_a, y_train_a, cost=cost, train_op=train_op, acc=accuracy)
+        train_loss += _loss
+        train_acc += acc
+        n_batch += 1
+
+        # print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
+        # print("   train loss: %f" % (train_loss / n_batch))
+        # print("   train acc: %f" % (train_acc / n_batch))
 
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
         print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
         print("   train loss: %f" % (train_loss / n_batch))
         print("   train acc: %f" % (train_acc / n_batch))
-        val_loss, val_acc, n_batch = 0, 0, 0
+        val_loss, val_acc, val_batch = 0, 0, 0
+        net.eval()
         for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_val_a, y_: y_val_a})
-            val_loss += err
-            val_acc += ac
-            n_batch += 1
-        print("   val loss: %f" % (val_loss / n_batch))
-        print("   val acc: %f" % (val_acc / n_batch))
-
-print('Evaluation')
-test_loss, test_acc, n_batch = 0, 0, 0
+            _logits = net(X_val_a)
+            val_loss += tl.cost.cross_entropy(_logits, y_val_a, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_val_a))
+            val_batch += 1
+        print("   val loss: {}".format(val_loss / val_batch))
+        print("   val acc:  {}".format(val_acc / val_batch))
+
+net.test()
+test_loss, test_acc, n_test_batch = 0, 0, 0
 for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True):
-    err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
-    test_loss += err
-    test_acc += ac
-    n_batch += 1
-print("   test loss: %f" % (test_loss / n_batch))
-print("   test acc: %f" % (test_acc / n_batch))
+    _logits = net(X_test_a)
+    test_loss += tl.cost.cross_entropy(_logits, y_test_a, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_test_a))
+    n_test_batch += 1
+print("   test loss: %f" % (test_loss / n_test_batch))
+print("   test acc: %f" % (test_acc / n_test_batch))
diff --git a/examples/quantized_net/tutorial_quanconv_cifar10.py b/examples/quantized_net/tutorial_quanconv_cifar10.py
index f93368467..1da9b9037 100644
--- a/examples/quantized_net/tutorial_quanconv_cifar10.py
+++ b/examples/quantized_net/tutorial_quanconv_cifar10.py
@@ -38,105 +38,171 @@
 we run them inside 16 separate threads which continuously fill a TensorFlow queue.
 
 """
+import multiprocessing
 import time
 
 import numpy as np
-
 import tensorflow as tf
-import tensorlayer as tl
 
-bitW = 8
-bitA = 8
+import tensorlayer as tl
+from tensorlayer.layers import (Dense, Flatten, Input, MaxPool2d, QuanConv2dWithBN, QuanDense)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-sess = tf.InteractiveSession()
-
+# Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
+# prepare cifar10 data
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
 
 
-def model(x, y_, reuse, is_train, bitW, bitA):
-    with tf.variable_scope("model", reuse=reuse):
-        net = tl.layers.InputLayer(x, name='input')
-        net = tl.layers.QuanConv2dWithBN(
-            net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', is_train=is_train, bitW=bitW, bitA=bitA,
-            name='qcnnbn1'
-        )
-        net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-        # net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
-        net = tl.layers.QuanConv2dWithBN(
-            net, 64, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, is_train=is_train, bitW=bitW, bitA=bitA,
-            name='qcnnbn2'
-        )
-        # net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
-        net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-        net = tl.layers.FlattenLayer(net, name='flatten')
-        net = tl.layers.QuanDenseLayer(net, 384, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd1relu')
-        net = tl.layers.QuanDenseLayer(net, 192, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd2relu')
-        net = tl.layers.DenseLayer(net, 10, act=None, name='output')
-        y = net.outputs
-
-        ce = tl.cost.cross_entropy(y, y_, name='cost')
-        L2 = 0
-        for p in tl.layers.get_variables_with_name('relu/W', True, True):
-            L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
-        cost = ce + L2
-
-        # correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(y), 1), y_)
-        correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), tf.int64), y_)
-        acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-
-        return net, cost, acc
-
-
-def distort_fn(x, is_train=False):
-    x = tl.prepro.crop(x, 24, 24, is_random=is_train)
-    if is_train:
-        x = tl.prepro.flip_axis(x, axis=1, is_random=True)
-        x = tl.prepro.brightness(x, gamma=0.1, gain=1, is_random=True)
-    x = (x - np.mean(x)) / max(np.std(x), 1e-5)  # avoid values divided by 0
-    return x
-
-
-x = tf.placeholder(dtype=tf.float32, shape=[None, 24, 24, 3], name='x')
-y_ = tf.placeholder(dtype=tf.int64, shape=[None], name='y_')
-
-network, cost, _ = model(x, y_, False, True, bitW=bitW, bitA=bitA)
-_, cost_test, acc = model(x, y_, True, False, bitW=bitW, bitA=bitA)
-
-# train
-n_epoch = 50000
-learning_rate = 0.0001
-print_freq = 1
-batch_size = 128
-
-train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08,
-                                  use_locking=False).minimize(cost)
-
-sess.run(tf.global_variables_initializer())
+def model(input_shape, n_classes, bitW, bitA):
+    in_net = Input(shape=input_shape, name='input')
+    net = QuanConv2dWithBN(64, (5, 5), (1, 1), act='relu', padding='SAME', bitW=bitW, bitA=bitA, name='qcnnbn1')(in_net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
+    net = QuanConv2dWithBN(64, (5, 5), (1, 1), padding='SAME', act='relu', bitW=bitW, bitA=bitA, name='qcnnbn2')(net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
+    net = Flatten(name='flatten')(net)
+    net = QuanDense(384, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd1relu')(net)
+    net = QuanDense(192, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd2relu')(net)
+    net = Dense(n_classes, act=None, name='output')(net)
+    net = Model(inputs=in_net, outputs=net, name='dorefanet')
+    return net
 
-network.print_params(False)
-network.print_layers()
 
-print('   learning_rate: %f' % learning_rate)
-print('   batch_size: %d' % batch_size)
-print('   bitW: %d,   bitA: %d' % (bitW, bitA))
+# training settings
+bitW = 8
+bitA = 8
+net = model([None, 24, 24, 3], n_classes=10, bitW=bitW, bitA=bitA)
+batch_size = 128
+n_epoch = 50000
+learning_rate = 0.0001
+print_freq = 5
+n_step_epoch = int(len(y_train) / batch_size)
+n_step = n_epoch * n_step_epoch
+shuffle_buffer_size = 128
+
+optimizer = tf.optimizers.Adam(learning_rate)
+cost = tl.cost.cross_entropy
+
+
+def generator_train():
+    inputs = X_train
+    targets = y_train
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def generator_test():
+    inputs = X_test
+    targets = y_test
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def _map_fn_train(img, target):
+    # 1. Randomly crop a [height, width] section of the image.
+    img = tf.image.random_crop(img, [24, 24, 3])
+    # 2. Randomly flip the image horizontally.
+    img = tf.image.random_flip_left_right(img)
+    # 3. Randomly change brightness.
+    img = tf.image.random_brightness(img, max_delta=63)
+    # 4. Randomly change contrast.
+    img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
+    # 5. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _map_fn_test(img, target):
+    # 1. Crop the central [height, width] of the image.
+    img = tf.image.resize_with_pad(img, 24, 24)
+    # 2. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    img = tf.reshape(img, (24, 24, 3))
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
+
+
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+
+
+# dataset API and augmentation
+train_ds = tf.data.Dataset.from_generator(
+    generator_train, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# train_ds = train_ds.repeat(n_epoch)
+train_ds = train_ds.shuffle(shuffle_buffer_size)
+train_ds = train_ds.prefetch(buffer_size=4096)
+train_ds = train_ds.batch(batch_size)
+train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
+# value = train_ds.make_one_shot_iterator().get_next()
+
+test_ds = tf.data.Dataset.from_generator(
+    generator_test, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# test_ds = test_ds.shuffle(shuffle_buffer_size)
+# test_ds = test_ds.repeat(n_epoch)
+test_ds = test_ds.prefetch(buffer_size=4096)
+test_ds = test_ds.batch(batch_size)
+test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
+# value_test = test_ds.make_one_shot_iterator().get_next()
 
 for epoch in range(n_epoch):
     start_time = time.time()
-    for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-        X_train_a = tl.prepro.threading_data(X_train_a, fn=distort_fn, is_train=True)  # data augmentation for training
-        sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
 
+    train_loss, train_acc, n_iter = 0, 0, 0
+    net.train()
+    for X_batch, y_batch in train_ds:
+        _loss, acc = _train_step(net, X_batch, y_batch, cost=cost, train_op=optimizer, acc=accuracy)
+
+        train_loss += _loss
+        train_acc += acc
+        n_iter += 1
+
+    # use training and evaluation sets to evaluate the model every print_freq epoch
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-        print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
-        test_loss, test_acc, n_batch = 0, 0, 0
-        for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=False):
-            X_test_a = tl.prepro.threading_data(X_test_a, fn=distort_fn, is_train=False)  # central crop
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
-            test_loss += err
-            test_acc += ac
-            n_batch += 1
-        print("   test loss: %f" % (test_loss / n_batch))
-        print("   test acc: %f" % (test_acc / n_batch))
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+        net.eval()
+        val_loss, val_acc, n_val_iter = 0, 0, 0
+        for X_batch, y_batch in test_ds:
+            _logits = net(X_batch)  # is_train=False, disable dropout
+            val_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+            n_val_iter += 1
+        print("   val loss: {}".format(val_loss / n_val_iter))
+        print("   val acc:  {}".format(val_acc / n_val_iter))
+
+# use testing data to evaluate the model
+net.eval()
+test_loss, test_acc, n_iter = 0, 0, 0
+for X_batch, y_batch in test_ds:
+    _logits = net(X_batch)
+    test_loss += tl.cost.cross_entropy(_logits, y_batch, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+    n_iter += 1
+print("   test loss: {}".format(test_loss / n_iter))
+print("   test acc:  {}".format(test_acc / n_iter))
diff --git a/examples/quantized_net/tutorial_quanconv_mnist.py b/examples/quantized_net/tutorial_quanconv_mnist.py
index 66d52d13c..1dbfe8d4d 100644
--- a/examples/quantized_net/tutorial_quanconv_mnist.py
+++ b/examples/quantized_net/tutorial_quanconv_mnist.py
@@ -1,106 +1,116 @@
-#! /usr/bin/python
+#!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (
+    Dense, Dropout, Flatten, Input, MaxPool2d, QuanConv2d, QuanConv2dWithBN, QuanDense, QuanDenseLayerWithBN
+)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
 X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
 # X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
 
-sess = tf.InteractiveSession()
-
 batch_size = 128
 
-x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
-y_ = tf.placeholder(tf.int64, shape=[batch_size])
 
+def model(inputs_shape, n_class=10):
+    net_in = Input(inputs_shape, name="input")
 
-def model(x, is_train=True, reuse=False):
-    with tf.variable_scope("quan_cnn", reuse=reuse):
-        net = tl.layers.InputLayer(x, name='input')
-        net = tl.layers.QuanConv2dWithBN(
-            net, 32, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, is_train=is_train, name='qcbnb1'
-        )
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
+    net = QuanConv2dWithBN(
+        n_filter=32, filter_size=(5, 5), strides=(1, 1), padding='SAME', act=tl.nn.relu, name='qconvbn1'
+    )(net_in)
+    net = MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')(net)
 
-        net = tl.layers.QuanConv2dWithBN(
-            net, 64, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, is_train=is_train, name='qcbn2'
-        )
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
+    net = QuanConv2dWithBN(
+        n_filter=64, filter_size=(5, 5), strides=(1, 1), padding='SAME', act=tl.nn.relu, name='qconvbn2'
+    )(net)
+    net = MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')(net)
 
-        net = tl.layers.FlattenLayer(net)
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
-        net = tl.layers.QuanDenseLayerWithBN(net, 256, is_train=is_train, act=tf.nn.relu, name='qdbn')
+    net = Flatten(name='ft')(net)
 
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
-        net = tl.layers.QuanDenseLayer(net, 10, name='qdbn_out')
-    return net
+    # net = QuanDense(256, act="relu", name='qdbn')(net)
+    # net = QuanDense(n_class, name='qdbn_out')(net)
 
+    net = QuanDenseLayerWithBN(256, act="relu", name='qdbn')(net)
+    net = QuanDenseLayerWithBN(n_class, name='qdbn_out')(net)
 
-# define inferences
-net_train = model(x, is_train=True, reuse=False)
-net_test = model(x, is_train=False, reuse=True)
+    # net = Dense(256, act='relu', name='Dense1')(net)
+    # net = Dense(n_class, name='Dense2')(net)
+
+    net = Model(inputs=net_in, outputs=net, name='quan')
+    return net
 
-# cost for training
-y = net_train.outputs
-cost = tl.cost.cross_entropy(y, y_, name='xentropy')
 
-# cost and accuracy for evalution
-y2 = net_test.outputs
-cost_test = tl.cost.cross_entropy(y2, y_, name='xentropy2')
-correct_prediction = tf.equal(tf.argmax(y2, 1), y_)
-acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
 
-# define the optimizer
-train_params = tl.layers.get_variables_with_name('quan_cnn', True, True)
-train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
 
-# initialize all variables in the session
-sess.run(tf.global_variables_initializer())
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
 
-net_train.print_params(False)
-net_train.print_layers()
 
 n_epoch = 200
-print_freq = 5
+print_freq = 1
 
 # print(sess.run(net_test.all_params)) # print real values of parameters
+net = model([None, 28, 28, 1])
+train_op = tf.optimizers.Adam(learning_rate=0.0001)
+cost = tl.cost.cross_entropy
 
 for epoch in range(n_epoch):
     start_time = time.time()
+    train_loss, train_acc, n_iter = 0, 0, 0
+
     for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-        sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
+        net.train()
+        _loss, acc = _train_step(net, X_train_a, y_train_a, cost=cost, train_op=train_op, acc=accuracy)
+
+        train_loss += _loss
+        train_acc += acc
+        n_iter += 1
+
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
 
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-        print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
-        print("   train loss: %f" % (train_loss / n_batch))
-        print("   train acc: %f" % (train_acc / n_batch))
-        val_loss, val_acc, n_batch = 0, 0, 0
+
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+        # net.eval()
+        val_loss, val_acc, n_eval = 0, 0, 0
         for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_val_a, y_: y_val_a})
-            val_loss += err
-            val_acc += ac
-            n_batch += 1
-        print("   val loss: %f" % (val_loss / n_batch))
-        print("   val acc: %f" % (val_acc / n_batch))
-
-print('Evaluation')
-test_loss, test_acc, n_batch = 0, 0, 0
+            _logits = net(X_val_a)  # is_train=False, disable dropout
+            val_loss += tl.cost.cross_entropy(_logits, y_val_a, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_val_a))
+            n_eval += 1
+        print("   val loss: {}".format(val_loss / n_eval))
+        print("   val acc:  {}".format(val_acc / n_eval))
+
+# net.eval()
+test_loss, test_acc, n_test_batch = 0, 0, 0
 for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True):
-    err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
-    test_loss += err
-    test_acc += ac
-    n_batch += 1
-print("   test loss: %f" % (test_loss / n_batch))
-print("   test acc: %f" % (test_acc / n_batch))
+    _logits = net(X_test_a)
+    test_loss += tl.cost.cross_entropy(_logits, y_test_a, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_test_a))
+    n_test_batch += 1
+print("   test loss: %f" % (test_loss / n_test_batch))
+print("   test acc: %f" % (test_acc / n_test_batch))
diff --git a/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py b/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py
index b695fa88a..93748d89b 100644
--- a/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py
+++ b/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py
@@ -38,231 +38,184 @@
 we run them inside 16 separate threads which continuously fill a TensorFlow queue.
 
 """
-import os
+import multiprocessing
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (
+    Conv2d, Dense, Flatten, Input, LocalResponseNorm, MaxPool2d, TernaryConv2d, TernaryDense
+)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-model_file_name = "./model_cifar10_tfrecord.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
 # Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
+# prepare cifar10 data
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
 
-print('X_train.shape', X_train.shape)  # (50000, 32, 32, 3)
-print('y_train.shape', y_train.shape)  # (50000,)
-print('X_test.shape', X_test.shape)  # (10000, 32, 32, 3)
-print('y_test.shape', y_test.shape)  # (10000,)
-print('X %s   y %s' % (X_test.dtype, y_test.dtype))
-
-
-def data_to_tfrecord(images, labels, filename):
-    """Save data into TFRecord."""
-    if os.path.isfile(filename):
-        print("%s exists" % filename)
-        return
-    print("Converting data into %s ..." % filename)
-    # cwd = os.getcwd()
-    writer = tf.python_io.TFRecordWriter(filename)
-    for index, img in enumerate(images):
-        img_raw = img.tobytes()
-        # Visualize a image
-        # tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        label = int(labels[index])
-        # print(label)
-        # Convert the bytes back to image as follow:
-        # image = Image.frombytes('RGB', (32, 32), img_raw)
-        # image = np.fromstring(img_raw, np.float32)
-        # image = image.reshape([32, 32, 3])
-        # tl.visualize.frame(np.asarray(image, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
-        example = tf.train.Example(
-            features=tf.train.Features(
-                feature={
-                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
-                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
-                }
-            )
-        )
-        writer.write(example.SerializeToString())  # Serialize To String
-    writer.close()
-
-
-def read_and_decode(filename, is_train=None):
-    """Return tensor to read from TFRecord."""
-    filename_queue = tf.train.string_input_producer([filename])
-    reader = tf.TFRecordReader()
-    _, serialized_example = reader.read(filename_queue)
-    features = tf.parse_single_example(
-        serialized_example, features={
-            'label': tf.FixedLenFeature([], tf.int64),
-            'img_raw': tf.FixedLenFeature([], tf.string),
-        }
-    )
-    # You can do more image distortion here for training data
-    img = tf.decode_raw(features['img_raw'], tf.float32)
-    img = tf.reshape(img, [32, 32, 3])
-    # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
-        # 1. Randomly crop a [height, width] section of the image.
-        img = tf.random_crop(img, [24, 24, 3])
-
-        # 2. Randomly flip the image horizontally.
-        img = tf.image.random_flip_left_right(img)
-
-        # 3. Randomly change brightness.
-        img = tf.image.random_brightness(img, max_delta=63)
-
-        # 4. Randomly change contrast.
-        img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
-
-        # 5. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == False:
-        # 1. Crop the central [height, width] of the image.
-        img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
-
-        # 2. Subtract off the mean and divide by the variance of the pixels.
-        img = tf.image.per_image_standardization(img)
-
-    elif is_train == None:
-        img = img
-
-    label = tf.cast(features['label'], tf.int32)
-    return img, label
-
-
-# Save data into TFRecord files
-data_to_tfrecord(images=X_train, labels=y_train, filename="train.cifar10")
-data_to_tfrecord(images=X_test, labels=y_test, filename="test.cifar10")
 
+def model(input_shape, n_classes):
+    in_net = Input(shape=input_shape, name='input')
+
+    net = Conv2d(64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn1')(in_net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm1')(net)
+
+    net = TernaryConv2d(64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn2')(net)
+    net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name='norm2')(net)
+    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
+
+    net = Flatten(name='flatten')(net)
+
+    net = TernaryDense(384, act=tf.nn.relu, name='d1relu')(net)
+    net = TernaryDense(192, act=tf.nn.relu, name='d2relu')(net)
+    net = Dense(n_classes, act=None, name='output')(net)
+
+    net = Model(inputs=in_net, outputs=net, name='dorefanet')
+    return net
+
+
+# training settings
+bitW = 8
+bitA = 8
+net = model([None, 24, 24, 3], n_classes=10)
 batch_size = 128
-model_file_name = "./model_cifar10_advanced.ckpt"
-resume = False  # load model, resume from previous checkpoint?
-
-with tf.device('/cpu:0'):
-    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
-    # prepare data in cpu
-    x_train_, y_train_ = read_and_decode("train.cifar10", True)
-    x_test_, y_test_ = read_and_decode("test.cifar10", False)
-    # set the number of threads here
-    x_train_batch, y_train_batch = tf.train.shuffle_batch(
-        [x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32
-    )
-    # for testing, uses batch instead of shuffle_batch
-    x_test_batch, y_test_batch = tf.train.batch(
-        [x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32
-    )
-
-    def model(x_crop, y_, reuse):
-        """For more simplified CNN APIs, check tensorlayer.org."""
-        with tf.variable_scope("model", reuse=reuse):
-            net = tl.layers.InputLayer(x_crop, name='input')
-            net = tl.layers.Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn1')
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm1')
-            net = tl.layers.TernaryConv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn2')
-            net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm2')
-            net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-            net = tl.layers.FlattenLayer(net, name='flatten')
-            net = tl.layers.TernaryDenseLayer(net, 384, act=tf.nn.relu, name='d1relu')
-            net = tl.layers.TernaryDenseLayer(net, 192, act=tf.nn.relu, name='d2relu')
-            net = tl.layers.DenseLayer(net, 10, act=None, name='output')
-            y = net.outputs
-
-            ce = tl.cost.cross_entropy(y, y_, name='cost')
-            # L2 for the MLP, without this, the accuracy will be reduced by 15%.
-            L2 = 0
-            for p in tl.layers.get_variables_with_name('relu/W', True, True):
-                L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
-            cost = ce + L2
-
-            # correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(y), 1), y_)
-            correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), tf.int32), y_)
-            acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-
-            return net, cost, acc
-
-    # You can also use placeholder to feed_dict in data after using
-    # val, l = sess.run([x_train_batch, y_train_batch]) to get the data
-    # x_crop = tf.placeholder(tf.float32, shape=[batch_size, 24, 24, 3])
-    # y_ = tf.placeholder(tf.int32, shape=[batch_size,])
-    # cost, acc, network = model(x_crop, y_, None)
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        network, cost, acc, = model(x_train_batch, y_train_batch, False)
-        _, cost_test, acc_test = model(x_test_batch, y_test_batch, True)
-
-    # train
-    n_epoch = 50000
-    learning_rate = 0.0001
-    print_freq = 1
-    n_step_epoch = int(len(y_train) / batch_size)
-    n_step = n_epoch * n_step_epoch
-
-    with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
-
-    sess.run(tf.global_variables_initializer())
-    if resume:
-        print("Load existing model " + "!" * 10)
-        saver = tf.train.Saver()
-        saver.restore(sess, model_file_name)
-
-    network.print_params(False)
-    network.print_layers()
-
-    print('   learning_rate: %f' % learning_rate)
-    print('   batch_size: %d' % batch_size)
-    print('   n_epoch: %d, step in an epoch: %d, total n_step: %d' % (n_epoch, n_step_epoch, n_step))
-
-    coord = tf.train.Coordinator()
-    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-    step = 0
-    for epoch in range(n_epoch):
-        start_time = time.time()
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for s in range(n_step_epoch):
-            # You can also use placeholder to feed_dict in data after using
-            # val, l = sess.run([x_train_batch, y_train_batch])
-            # tl.visualize.images2d(val, second=3, saveable=False, name='batch', dtype=np.uint8, fig_idx=2020121)
-            # err, ac, _ = sess.run([cost, acc, train_op], feed_dict={x_crop: val, y_: l})
-            err, ac, _ = sess.run([cost, acc, train_op])
-            step += 1
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
-
-        if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-            print(
-                "Epoch %d : Step %d-%d of %d took %fs" %
-                (epoch, step, step + n_step_epoch, n_step, time.time() - start_time)
-            )
-            print("   train loss: %f" % (train_loss / n_batch))
-            print("   train acc: %f" % (train_acc / n_batch))
-
-            test_loss, test_acc, n_batch = 0, 0, 0
-            for _ in range(int(len(y_test) / batch_size)):
-                err, ac = sess.run([cost_test, acc_test])
-                test_loss += err
-                test_acc += ac
-                n_batch += 1
-            print("   test loss: %f" % (test_loss / n_batch))
-            print("   test acc: %f" % (test_acc / n_batch))
-
-        if (epoch + 1) % (print_freq * 50) == 0:
-            print("Save model " + "!" * 10)
-            saver = tf.train.Saver()
-            save_path = saver.save(sess, model_file_name)
-            # you can also save model into npz
-            tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
-            # and restore it as follow:
-            # tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
-
-    coord.request_stop()
-    coord.join(threads)
-    sess.close()
+n_epoch = 50000
+learning_rate = 0.0001
+print_freq = 5
+n_step_epoch = int(len(y_train) / batch_size)
+n_step = n_epoch * n_step_epoch
+shuffle_buffer_size = 128
+
+optimizer = tf.optimizers.Adam(learning_rate)
+cost = tl.cost.cross_entropy
+
+
+def generator_train():
+    inputs = X_train
+    targets = y_train
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def generator_test():
+    inputs = X_test
+    targets = y_test
+    if len(inputs) != len(targets):
+        raise AssertionError("The length of inputs and targets should be equal")
+    for _input, _target in zip(inputs, targets):
+        # yield _input.encode('utf-8'), _target.encode('utf-8')
+        yield _input, _target
+
+
+def _map_fn_train(img, target):
+    # 1. Randomly crop a [height, width] section of the image.
+    img = tf.image.random_crop(img, [24, 24, 3])
+    # 2. Randomly flip the image horizontally.
+    img = tf.image.random_flip_left_right(img)
+    # 3. Randomly change brightness.
+    img = tf.image.random_brightness(img, max_delta=63)
+    # 4. Randomly change contrast.
+    img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
+    # 5. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _map_fn_test(img, target):
+    # 1. Crop the central [height, width] of the image.
+    img = tf.image.resize_with_pad(img, 24, 24)
+    # 2. Subtract off the mean and divide by the variance of the pixels.
+    img = tf.image.per_image_standardization(img)
+    img = tf.reshape(img, (24, 24, 3))
+    target = tf.reshape(target, ())
+    return img, target
+
+
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
+
+
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+
+
+# dataset API and augmentation
+train_ds = tf.data.Dataset.from_generator(
+    generator_train, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# train_ds = train_ds.repeat(n_epoch)
+train_ds = train_ds.shuffle(shuffle_buffer_size)
+train_ds = train_ds.prefetch(buffer_size=4096)
+train_ds = train_ds.batch(batch_size)
+train_ds = train_ds.map(_map_fn_train, num_parallel_calls=multiprocessing.cpu_count())
+# value = train_ds.make_one_shot_iterator().get_next()
+
+test_ds = tf.data.Dataset.from_generator(
+    generator_test, output_types=(tf.float32, tf.int32)
+)  # , output_shapes=((24, 24, 3), (1)))
+# test_ds = test_ds.shuffle(shuffle_buffer_size)
+# test_ds = test_ds.repeat(n_epoch)
+test_ds = test_ds.prefetch(buffer_size=4096)
+test_ds = test_ds.batch(batch_size)
+test_ds = test_ds.map(_map_fn_test, num_parallel_calls=multiprocessing.cpu_count())
+# value_test = test_ds.make_one_shot_iterator().get_next()
+
+for epoch in range(n_epoch):
+    start_time = time.time()
+
+    train_loss, train_acc, n_iter = 0, 0, 0
+    net.train()
+    for X_batch, y_batch in train_ds:
+        _loss, acc = _train_step(net, X_batch, y_batch, cost=cost, train_op=optimizer, acc=accuracy)
+
+        train_loss += _loss
+        train_acc += acc
+        n_iter += 1
+
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+    # use training and evaluation sets to evaluate the model every print_freq epoch
+    if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
+        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: {}".format(train_loss / n_iter))
+        print("   train acc:  {}".format(train_acc / n_iter))
+
+        net.eval()
+        val_loss, val_acc, n_val_iter = 0, 0, 0
+        for X_batch, y_batch in test_ds:
+            _logits = net(X_batch)  # is_train=False, disable dropout
+            val_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+            n_val_iter += 1
+        print("   val loss: {}".format(val_loss / n_val_iter))
+        print("   val acc:  {}".format(val_acc / n_val_iter))
+
+# use testing data to evaluate the model
+net.eval()
+test_loss, test_acc, n_iter = 0, 0, 0
+for X_batch, y_batch in test_ds:
+    _logits = net(X_batch)
+    test_loss += tl.cost.cross_entropy(_logits, y_batch, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
+    n_iter += 1
+print("   test loss: {}".format(test_loss / n_iter))
+print("   test acc:  {}".format(test_acc / n_iter))
diff --git a/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py b/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py
index 6850b9591..a708d1f0e 100644
--- a/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py
+++ b/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py
@@ -3,109 +3,100 @@
 
 import time
 
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
+from tensorlayer.layers import (BatchNorm, Dense, Flatten, Input, MaxPool2d, TernaryConv2d, TernaryDense)
+from tensorlayer.models import Model
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
 X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
-# X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
-
-sess = tf.InteractiveSession()
 
 batch_size = 128
 
-x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
-y_ = tf.placeholder(tf.int64, shape=[batch_size])
-
-
-def model(x, is_train=True, reuse=False):
-    # In BNN, all the layers inputs are binary, with the exception of the first layer.
-    # ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
-    with tf.variable_scope("binarynet", reuse=reuse):
-        net = tl.layers.InputLayer(x, name='input')
-        net = tl.layers.TernaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
-
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.TernaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')
-        net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
-
-        net = tl.layers.FlattenLayer(net)
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.TernaryDenseLayer(net, 256, b_init=None, name='dense')
-        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn3')
-
-        # net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
-        # net = tl.layers.SignLayer(net)
-        net = tl.layers.TernaryDenseLayer(net, 10, b_init=None, name='bout')
-        net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bno')
-    return net
 
+def model(inputs_shape, n_class=10):
+    in_net = Input(inputs_shape, name='input')
+    net = TernaryConv2d(32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')(in_net)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn1')(net)
+
+    net = TernaryConv2d(64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')(net)
+    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn2')(net)
+
+    net = Flatten('flatten')(net)
+    net = Dense(256, b_init=None, name='dense')(net)
+    net = BatchNorm(act=tl.act.htanh, name='bn3')(net)
+
+    net = TernaryDense(n_class, b_init=None, name='bout')(net)
+    net = BatchNorm(name='bno')(net)
 
-# define inferences
-net_train = model(x, is_train=True, reuse=False)
-net_test = model(x, is_train=False, reuse=True)
+    net = Model(inputs=in_net, outputs=net, name='dorefanet')
+    return net
 
-# cost for training
-y = net_train.outputs
-cost = tl.cost.cross_entropy(y, y_, name='xentropy')
 
-# cost and accuracy for evalution
-y2 = net_test.outputs
-cost_test = tl.cost.cross_entropy(y2, y_, name='xentropy2')
-correct_prediction = tf.equal(tf.argmax(y2, 1), y_)
-acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):
+    with tf.GradientTape() as tape:
+        y_pred = network(X_batch)
+        _loss = cost(y_pred, y_batch)
+    grad = tape.gradient(_loss, network.trainable_weights)
+    train_op.apply_gradients(zip(grad, network.trainable_weights))
+    if acc is not None:
+        _acc = acc(y_pred, y_batch)
+        return _loss, _acc
+    else:
+        return _loss, None
 
-# define the optimizer
-train_params = tl.layers.get_variables_with_name('binarynet', True, True)
-train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
 
-# initialize all variables in the session
-sess.run(tf.global_variables_initializer())
+def accuracy(_logits, y_batch):
+    return np.mean(np.equal(np.argmax(_logits, 1), y_batch))
 
-net_train.print_params()
-net_train.print_layers()
 
 n_epoch = 200
 print_freq = 5
 
-# print(sess.run(net_test.all_params)) # print real values of parameters
+net = model([None, 28, 28, 1])
+train_op = tf.optimizers.Adam(learning_rate=0.0001)
+cost = tl.cost.cross_entropy
 
 for epoch in range(n_epoch):
     start_time = time.time()
+    train_loss, train_acc, n_batch = 0, 0, 0
+    net.train()
+
     for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-        sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
+        _loss, acc = _train_step(net, X_train_a, y_train_a, cost=cost, train_op=train_op, acc=accuracy)
+        train_loss += _loss
+        train_acc += acc
+        n_batch += 1
+
+        print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
+        print("   train loss: %f" % (train_loss / n_batch))
+        print("   train acc: %f" % (train_acc / n_batch))
 
     if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
         print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
-        train_loss, train_acc, n_batch = 0, 0, 0
-        for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
-            train_loss += err
-            train_acc += ac
-            n_batch += 1
         print("   train loss: %f" % (train_loss / n_batch))
         print("   train acc: %f" % (train_acc / n_batch))
-        val_loss, val_acc, n_batch = 0, 0, 0
+        val_loss, val_acc, val_batch = 0, 0, 0
+        net.eval()
         for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
-            err, ac = sess.run([cost_test, acc], feed_dict={x: X_val_a, y_: y_val_a})
-            val_loss += err
-            val_acc += ac
-            n_batch += 1
-        print("   val loss: %f" % (val_loss / n_batch))
-        print("   val acc: %f" % (val_acc / n_batch))
-
-print('Evaluation')
-test_loss, test_acc, n_batch = 0, 0, 0
+            _logits = net(X_val_a)
+            val_loss += tl.cost.cross_entropy(_logits, y_val_a, name='eval_loss')
+            val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_val_a))
+            val_batch += 1
+        print("   val loss: {}".format(val_loss / val_batch))
+        print("   val acc:  {}".format(val_acc / val_batch))
+
+net.test()
+test_loss, test_acc, n_test_batch = 0, 0, 0
 for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True):
-    err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
-    test_loss += err
-    test_acc += ac
-    n_batch += 1
-print("   test loss: %f" % (test_loss / n_batch))
-print("   test acc: %f" % (test_acc / n_batch))
+    _logits = net(X_test_a)
+    test_loss += tl.cost.cross_entropy(_logits, y_test_a, name='test_loss')
+    test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_test_a))
+    n_test_batch += 1
+print("   test loss: %f" % (test_loss / n_test_batch))
+print("   test acc: %f" % (test_acc / n_test_batch))
diff --git a/examples/reinforcement_learning/.gitignore b/examples/reinforcement_learning/.gitignore
new file mode 100644
index 000000000..92fdef002
--- /dev/null
+++ b/examples/reinforcement_learning/.gitignore
@@ -0,0 +1,2 @@
+model/
+image/
diff --git a/examples/reinforcement_learning/README.md b/examples/reinforcement_learning/README.md
index 09a97d155..a17f9d577 100644
--- a/examples/reinforcement_learning/README.md
+++ b/examples/reinforcement_learning/README.md
@@ -1,11 +1,17 @@
-# Reinforcement Learning Tutorial with Tensorlayer
+# Comprehensive Reinforcement Learning Tutorial
+
+![GitHub last commit (branch)](https://img.shields.io/github/last-commit/tensorlayer/tensorlayer/master.svg)
+[![Supported TF Version](https://img.shields.io/badge/TensorFlow-2.0.0%2B-brightgreen.svg)](https://github.com/tensorflow/tensorflow/releases)
+[![Documentation Status](https://readthedocs.org/projects/tensorlayer/badge/)](https://tensorlayer.readthedocs.io/)
+[![Build Status](https://travis-ci.org/tensorlayer/tensorlayer.svg?branch=master)](https://travis-ci.org/tensorlayer/tensorlayer)
+[![Downloads](http://pepy.tech/badge/tensorlayer)](http://pepy.tech/project/tensorlayer)
 
 <br/>
-<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc" target="\_blank">
+<a href="https://deepreinforcementlearningbook.org" target="\_blank">
 	<div align="center">
-		<img src="../../img/join_slack.png" width="40%"/>
+		<img src="http://deep-reinforcement-learning-book.github.io/assets/images/cover_v1.png" width="22%"/>
 	</div>
-	<div align="center"><caption>Slack Invitation Link</caption></div>
+<!-- 	<div align="center"><caption>Slack Invitation Link</caption></div> -->
 </a>
 <br/>
 
@@ -20,7 +26,18 @@
 <br/>
 -->
 
-This repository contains implementation of most popular reinforcement learning algorithms with Tensorlayer 2.0, supporting [Tensorflow 2.0](https://www.tensorflow.org/alpha/guide/effective_tf2). We aim to make the reinforcement learning tutorial for each algorithm simple and straight-forward to use, as this would not only benefits new learners of reinforcement learning, but also provide convenience for senior researchers to testify their new ideas quickly.
+This repository contains implementations of the most popular reinforcement learning algorithms, powered by [Tensorflow 2.0](https://www.tensorflow.org/alpha/guide/effective_tf2) and Tensorlayer 2.0. We aim to make the reinforcement learning tutorial simple, transparent and straight-forward, as this would not only benefits new learners of reinforcement learning, but also provide convenience for senior researchers to testify their new ideas quickly. 
+
+A corresponding [Springer textbook](https://deepreinforcementlearningbook.org) is also provided, you can get the free PDF if your institute has Springer license. We also released an [RLzoo](https://github.com/tensorlayer/RLzoo) for simple usage.
+
+<br/>
+<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc" target="\_blank">
+	<div align="center">
+		<img src="../../img/join_slack.png" width="20%"/>
+	</div>
+<!-- 	<div align="center"><caption>Slack Invitation Link</caption></div> -->
+</a>
+<br/>
 
 ## Prerequisites:
 
@@ -28,12 +45,24 @@ This repository contains implementation of most popular reinforcement learning a
 * tensorflow >= 2.0.0 or tensorflow-gpu >= 2.0.0a0
 * tensorlayer >= 2.0.1
 * tensorflow-probability
-* tf-nightly-2.0-preview
 
 *** If you meet the error`AttributeError: module 'tensorflow' has no attribute 'contrib'` when running the code after installing tensorflow-probability, try:
 
 `pip install --upgrade tf-nightly-2.0-preview tfp-nightly`
 
+## Quick Start
+```
+conda create --name tl python=3.6.4  
+conda activate tl
+pip install tensorflow-gpu==2.0.0-rc1 # if no GPU, use pip install tensorflow==2.0.0
+pip install tensorlayer
+pip install tensorflow-probability==0.9.0
+pip install gym
+pip install gym[atari] # for others, use pip instal gym[all]
+
+python tutorial_DDPG.py --train
+```
+
 ## Status: Beta
 
 We are currently open to any suggestions or pull requests from you to make the reinforcement learning tutorial with TensorLayer2.0 a better code repository for both new learners and senior researchers. Some of the algorithms mentioned in the this markdown may be not yet available, since we are still trying to implement more RL algorithms and optimize their performances. However, those algorithms listed above will come out in a few weeks, and the repository will keep updating more advanced RL algorithms in the future.
@@ -46,6 +75,8 @@ For each tutorial, open a terminal and run:
 
 The tutorial algorithms follow the same basic structure, as shown in file: [`./tutorial_format.py`](https://github.com/tensorlayer/tensorlayer/blob/reinforcement-learning/examples/reinforcement_learning/tutorial_format.py)
 
+The pretrained models and learning curves for each algorithm are stored [here](https://github.com/tensorlayer/pretrained-models). You can download the models and load the weights in the policies for tests.
+
 ## Table of Contents:
 ### value-based
 | Algorithms      | Action Space | Tutorial Env   | Papers |
@@ -56,7 +87,6 @@ The tutorial algorithms follow the same basic structure, as shown in file: [`./t
 | Prioritized Experience Replay | Discrete     | Pong, CartPole | [Schaul et al. Prioritized experience replay. Schaul et al. 2015.](https://arxiv.org/abs/1511.05952) |
 |Dueling DQN|Discrete     | Pong, CartPole |[Dueling network architectures for deep reinforcement learning. Wang et al. 2015.](https://arxiv.org/abs/1511.06581)|
 |Double DQN| Discrete     | Pong, CartPole |[Deep reinforcement learning with double q-learning. Van et al. 2016.](https://arxiv.org/abs/1509.06461)|
-|Retrace|Discrete     | Pong, CartPole |[Safe and efficient off-policy reinforcement learning. Munos et al. 2016: ](https://arxiv.org/pdf/1606.02647.pdf)|
 |Noisy DQN|Discrete     | Pong, CartPole |[Noisy networks for exploration. Fortunato et al. 2017.](https://arxiv.org/pdf/1706.10295.pdf)|
 | Distributed DQN (C51)| Discrete     | Pong, CartPole | [A distributional perspective on reinforcement learning. Bellemare et al. 2017.](https://arxiv.org/pdf/1707.06887.pdf) |
 |**policy-based**||||
@@ -90,7 +120,6 @@ The tutorial algorithms follow the same basic structure, as shown in file: [`./t
   See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
   ```
 
-  ​    
 
 * **Deep Q-Network (DQN)**
 
@@ -124,25 +153,24 @@ The tutorial algorithms follow the same basic structure, as shown in file: [`./t
 
   ```
   We implement Double DQN, Dueling DQN and Noisy DQN here.
-
+  
   -The max operator in standard DQN uses the same values both to select and to evaluate an action by:
-
+  
      Q(s_t, a_t) = R\_{t+1\} + gamma \* max\_{a}Q\_\{target\}(s_{t+1}, a).
-
+  
   -Double DQN proposes to use following evaluation to address overestimation problem of max operator:
-
+  
      Q(s_t, a_t) = R\_{t+1\} + gamma \* Q\_{target}(s\_\{t+1\}, max{a}Q(s_{t+1}, a)).
-
+  
   -Dueling DQN uses dueling architecture where the value of state and the advantage of each action is estimated separately.
-
+  
   -Noisy DQN propose to explore by adding parameter noises.
-
-
   ```
 
 
 
 
+
 * **Prioritized Experience Replay**
 
   <u>Code</u>: `./tutorial_prioritized_replay.py`
@@ -170,23 +198,6 @@ The tutorial algorithms follow the same basic structure, as shown in file: [`./t
   ```
 
 
-
-
-* **Retrace(lambda) DQN**
-
-  <u>Code</u>: `./tutorial_Retrace.py`
-
-  <u>Paper</u>: [Safe and Efficient Off-Policy Reinforcement Learning](https://arxiv.org/abs/1606.02647)
-
-  <u>Description:</u>
-
-  ```
-  Retrace (lambda) is an off-policy algorithm that extend the idea of eligibility trace. It apply an importance sampling ratio truncated at 1 to several behaviour policies, which suffer from the variance explosion of standard IS and lead to safe and efficient learning.
-  ```
-
-
-
-
 * **Actor-Critic (AC)**
 
   <u>Code</u>:`./tutorial_AC.py`
@@ -355,5 +366,12 @@ Our env wrapper: `./tutorial_wrappers.py`
 - @zsdonghao Hao Dong: AC, A3C, Q-Learning, DQN, PG
 - @quantumiracle Zihan Ding: SAC, TD3.
 - @Tokarev-TT-33 Tianyang Yu @initial-h Hongming Zhang : PG, DDPG, PPO, DPPO, TRPO
-- @Officium Yanhua Huang: C51, Retrace, DQN_variants, prioritized_replay, wrappers.
+- @Officium Yanhua Huang: C51, DQN_variants, prioritized_replay, wrappers.
+
+## Recommended Materials
 
+- [李宏毅RL视频](https://www.bilibili.com/video/av58458003?from=search&seid=962941912089186406)
+- [CS885 Spring 2018 - Reinforcement Learning by Pascal Poupart](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/schedule.html)
+- [Youtube Video By David Silver, 2015 @ UCL](https://www.youtube.com/playlist?list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-)
+- [Teaching Materials By David Silver @ UCL](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html)
+- [Deep Reinforcement Learning: Fundamentals, Research and Applications By Hao Dong, Zihan Ding, Shanghang Zhang etc](http://deep-reinforcement-learning-book.github.io/)
diff --git a/examples/reinforcement_learning/baselines/SAC.py b/examples/reinforcement_learning/baselines/SAC.py
deleted file mode 100644
index 5760298d3..000000000
--- a/examples/reinforcement_learning/baselines/SAC.py
+++ /dev/null
@@ -1,404 +0,0 @@
-'''
-Soft Actor-Critic
-using target Q instead of V net: 2 Q net, 2 target Q net, 1 policy net
-adding alpha loss
-
-paper: https://arxiv.org/pdf/1812.05905.pdf
-Actor policy is stochastic.
-
-Env: Openai Gym Pendulum-v0, continuous action space
-
-tensorflow 2.0.0a0
-tensorflow-probability 0.6.0
-tensorlayer 2.0.0
-
-&&
-pip install box2d box2d-kengz --user
-
-To run:
-python tutorial_sac.py --train/test
-'''
-
-import argparse
-import math
-import random
-import time
-
-import matplotlib.pyplot as plt
-import numpy as np
-from IPython.display import clear_output
-
-import gym
-import tensorflow as tf
-import tensorflow_probability as tfp
-import tensorlayer as tl
-from tensorlayer.layers import Dense
-from tensorlayer.models import Model
-from utils import *
-from wrappers import NormalizedActions
-
-tfd = tfp.distributions
-Normal = tfd.Normal
-
-tl.logging.set_verbosity(tl.logging.DEBUG)
-
-np.random.seed(2)
-tf.random.set_seed(2)  # reproducible
-
-parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=False)
-parser.add_argument('--test', dest='test', action='store_true', default=True)
-args = parser.parse_args()
-
-
-class SoftQNetwork(Model):
-
-    def __init__(self, num_inputs, num_actions, hidden_dim, init_w=3e-3):
-        super(SoftQNetwork, self).__init__()
-        input_dim = num_inputs + num_actions
-        w_init = tf.keras.initializers.glorot_normal(
-            seed=None
-        )  # glorot initialization is better than uniform in practice
-        # w_init = tf.random_uniform_initializer(-init_w, init_w)
-
-        self.linear1 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=input_dim, name='q1')
-        self.linear2 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='q2')
-        self.linear3 = Dense(n_units=1, W_init=w_init, in_channels=hidden_dim, name='q3')
-
-    def forward(self, input):
-        x = self.linear1(input)
-        x = self.linear2(x)
-        x = self.linear3(x)
-        return x
-
-
-class PolicyNetwork(Model):
-
-    def __init__(
-            self, num_inputs, num_actions, hidden_dim, action_range=1., init_w=3e-3, log_std_min=-20, log_std_max=2
-    ):
-        super(PolicyNetwork, self).__init__()
-
-        self.log_std_min = log_std_min
-        self.log_std_max = log_std_max
-
-        w_init = tf.keras.initializers.glorot_normal(seed=None)
-        # w_init = tf.random_uniform_initializer(-init_w, init_w)
-
-        self.linear1 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=num_inputs, name='policy1')
-        self.linear2 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy2')
-        self.linear3 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy3')
-
-        self.mean_linear = Dense(n_units=num_actions, W_init=w_init, \
-        b_init=tf.random_uniform_initializer(-init_w, init_w), in_channels=hidden_dim, name='policy_mean')
-        self.log_std_linear = Dense(n_units=num_actions, W_init=w_init, \
-        b_init=tf.random_uniform_initializer(-init_w, init_w), in_channels=hidden_dim, name='policy_logstd')
-
-        self.action_range = action_range
-        self.num_actions = num_actions
-
-    def forward(self, state):
-        x = self.linear1(state)
-        x = self.linear2(x)
-        x = self.linear3(x)
-
-        mean = self.mean_linear(x)
-        log_std = self.log_std_linear(x)
-        log_std = tf.clip_by_value(log_std, self.log_std_min, self.log_std_max)
-
-        return mean, log_std
-
-    def evaluate(self, state, epsilon=1e-6):
-        ''' generate action with state for calculating gradients '''
-        state = state.astype(np.float32)
-        mean, log_std = self.forward(state)
-        std = tf.math.exp(log_std)  # no clip in evaluation, clip affects gradients flow
-
-        normal = Normal(0, 1)
-        z = normal.sample()
-        action_0 = tf.math.tanh(mean + std * z)  # TanhNormal distribution as actions; reparameterization trick
-        action = self.action_range * action_0
-        # according to original paper, with an extra last term for normalizing different action range
-        log_prob = Normal(mean, std).log_prob(mean + std * z) - tf.math.log(1. - action_0**2 +
-                                                                            epsilon) - np.log(self.action_range)
-        # both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
-        # the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
-        # needs sum up across the dim of actions to get 1 dim probability; or else use Multivariate Normal.
-        log_prob = tf.reduce_sum(log_prob, axis=1)[:, np.newaxis]  # expand dim as reduce_sum causes 1 dim reduced
-
-        return action, log_prob, z, mean, log_std
-
-    def get_action(self, state, deterministic):
-        ''' generate action with state for interaction with envronment '''
-        mean, log_std = self.forward([state])
-        std = tf.math.exp(log_std)
-
-        normal = Normal(0, 1)
-        z = normal.sample()
-        action = self.action_range * tf.math.tanh(
-            mean + std * z
-        )  # TanhNormal distribution as actions; reparameterization trick
-
-        action = self.action_range * mean if deterministic else action
-        return action.numpy()[0]
-
-    def sample_action(self, ):
-        ''' generate random actions for exploration '''
-        a = tf.random.uniform([self.num_actions], -1, 1)
-
-        return self.action_range * a.numpy()
-
-
-class SAC_Trainer():
-
-    def __init__(self, replay_buffer, hidden_dim, action_range, soft_q_lr=3e-4, policy_lr=3e-4, alpha_lr=3e-4):
-        self.replay_buffer = replay_buffer
-
-        # initialize all networks
-        self.soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim)
-        self.soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim)
-        self.target_soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim)
-        self.target_soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim)
-        self.policy_net = PolicyNetwork(state_dim, action_dim, hidden_dim, action_range)
-        self.log_alpha = tf.Variable(0, dtype=np.float32, name='log_alpha')
-        self.alpha = tf.math.exp(self.log_alpha)
-        print('Soft Q Network (1,2): ', self.soft_q_net1)
-        print('Policy Network: ', self.policy_net)
-
-        # initialize weights of target networks
-        self.target_soft_q_net1 = self.target_ini(self.soft_q_net1, self.target_soft_q_net1)
-        self.target_soft_q_net2 = self.target_ini(self.soft_q_net2, self.target_soft_q_net2)
-
-        self.soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
-        self.soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
-        self.policy_optimizer = tf.optimizers.Adam(policy_lr)
-        self.alpha_optimizer = tf.optimizers.Adam(alpha_lr)
-        # self.alpha_optimizer = optim.Adam([self.log_alpha], lr=alpha_lr)
-
-    def target_ini(self, net, target_net):
-        ''' hard-copy update for initializing target networks '''
-        for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
-            target_param.assign(param)
-        return target_net
-
-    def target_soft_update(self, net, target_net, soft_tau):
-        ''' soft update the target net with Polyak averaging '''
-        for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
-            target_param.assign(  # copy weight value into target parameters
-                target_param * (1.0 - soft_tau) + param * soft_tau
-            )
-        return target_net
-
-    def update(self, batch_size, reward_scale=10., auto_entropy=True, target_entropy=-2, gamma=0.99, soft_tau=1e-2):
-        ''' update all networks in SAC '''
-        state, action, reward, next_state, done = self.replay_buffer.sample(batch_size)
-
-        reward = reward[:, np.newaxis]  # expand dim
-        done = done[:, np.newaxis]
-
-        reward = reward_scale * (reward -
-                                 np.mean(reward, axis=0)) / np.std(reward, axis=0)  # normalize with batch mean and std
-
-        # Training Q Function
-        new_next_action, next_log_prob, _, _, _ = self.policy_net.evaluate(next_state)
-        target_q_input = tf.concat([next_state, new_next_action], 1)  # the dim 0 is number of samples
-        target_q_min = tf.minimum(
-            self.target_soft_q_net1(target_q_input), self.target_soft_q_net2(target_q_input)
-        ) - self.alpha * next_log_prob
-        target_q_value = reward + (1 - done) * gamma * target_q_min  # if done==1, only reward
-        q_input = tf.concat([state, action], 1)  # the dim 0 is number of samples
-
-        with tf.GradientTape() as q1_tape:
-            predicted_q_value1 = self.soft_q_net1(q_input)
-            q_value_loss1 = tf.reduce_mean(tf.losses.mean_squared_error(predicted_q_value1, target_q_value))
-        q1_grad = q1_tape.gradient(q_value_loss1, self.soft_q_net1.trainable_weights)
-        self.soft_q_optimizer1.apply_gradients(zip(q1_grad, self.soft_q_net1.trainable_weights))
-
-        with tf.GradientTape() as q2_tape:
-            predicted_q_value2 = self.soft_q_net2(q_input)
-            q_value_loss2 = tf.reduce_mean(tf.losses.mean_squared_error(predicted_q_value2, target_q_value))
-        q2_grad = q2_tape.gradient(q_value_loss2, self.soft_q_net2.trainable_weights)
-        self.soft_q_optimizer2.apply_gradients(zip(q2_grad, self.soft_q_net2.trainable_weights))
-
-        # Training Policy Function
-        with tf.GradientTape() as p_tape:
-            new_action, log_prob, z, mean, log_std = self.policy_net.evaluate(state)
-            new_q_input = tf.concat([state, new_action], 1)  # the dim 0 is number of samples
-            ''' implementation 1 '''
-            predicted_new_q_value = tf.minimum(self.soft_q_net1(new_q_input), self.soft_q_net2(new_q_input))
-            ''' implementation 2 '''
-            # predicted_new_q_value = self.soft_q_net1(new_q_input)
-            policy_loss = tf.reduce_mean(self.alpha * log_prob - predicted_new_q_value)
-        p_grad = p_tape.gradient(policy_loss, self.policy_net.trainable_weights)
-        self.policy_optimizer.apply_gradients(zip(p_grad, self.policy_net.trainable_weights))
-
-        # Updating alpha w.r.t entropy
-        # alpha: trade-off between exploration (max entropy) and exploitation (max Q)
-        if auto_entropy is True:
-            with tf.GradientTape() as alpha_tape:
-                alpha_loss = -tf.reduce_mean((self.log_alpha * (log_prob + target_entropy)))
-            alpha_grad = alpha_tape.gradient(alpha_loss, [self.log_alpha])
-            self.alpha_optimizer.apply_gradients(zip(alpha_grad, [self.log_alpha]))
-            self.alpha = tf.math.exp(self.log_alpha)
-        else:  # fixed alpha
-            self.alpha = 1.
-            alpha_loss = 0
-
-    # Soft update the target value nets
-        self.target_soft_q_net1 = self.target_soft_update(self.soft_q_net1, self.target_soft_q_net1, soft_tau)
-        self.target_soft_q_net2 = self.target_soft_update(self.soft_q_net2, self.target_soft_q_net2, soft_tau)
-
-    def save_weights(self):  # save trained weights
-        save_model(self.soft_q_net1, 'model_q_net1', 'SAC')
-        save_model(self.soft_q_net2, 'model_q_net2', 'SAC')
-        save_model(self.target_soft_q_net1, 'model_target_q_net1', 'SAC')
-        save_model(self.target_soft_q_net2, 'model_target_q_net2', 'SAC')
-        save_model(self.policy_net, 'model_policy_net', 'SAC')
-
-        # tl.files.save_npz(self.soft_q_net1.trainable_weights, name='model_q_net1.npz')
-        # tl.files.save_npz(self.soft_q_net2.trainable_weights, name='model_q_net2.npz')
-        # tl.files.save_npz(self.target_soft_q_net1.trainable_weights, name='model_target_q_net1.npz')
-        # tl.files.save_npz(self.target_soft_q_net2.trainable_weights, name='model_target_q_net2.npz')
-        # tl.files.save_npz(self.policy_net.trainable_weights, name='model_policy_net.npz')
-
-    def load_weights(self):  # load trained weights
-        # tl.files.load_and_assign_npz(name='model_q_net1.npz', network=self.soft_q_net1)
-        # tl.files.load_and_assign_npz(name='model_q_net2.npz', network=self.soft_q_net2)
-        # tl.files.load_and_assign_npz(name='model_target_q_net1.npz', network=self.target_soft_q_net1)
-        # tl.files.load_and_assign_npz(name='model_target_q_net2.npz', network=self.target_soft_q_net2)
-        # tl.files.load_and_assign_npz(name='model_policy_net.npz', network=self.policy_net)
-        load_model(self.soft_q_net1, 'model_q_net1', 'SAC')
-        load_model(self.soft_q_net2, 'model_q_net2', 'SAC')
-        load_model(self.target_soft_q_net1, 'model_target_q_net1', 'SAC')
-        load_model(self.target_soft_q_net2, 'model_target_q_net2', 'SAC')
-        load_model(self.policy_net, 'model_policy_net', 'SAC')
-
-
-# def plot(frame_idx, rewards):
-#     clear_output(True)
-#     plt.figure(figsize=(20,5))
-#     plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))
-#     plt.plot(rewards)
-#     plt.xlabel('Episode')
-#     plt.ylabel('Episode Reward')
-#     plt.savefig('sac.png')
-# plt.show()
-
-# choose env
-ENV = 'Pendulum-v0'
-env = NormalizedActions(gym.make(ENV))
-action_dim = env.action_space.shape[0]
-state_dim = env.observation_space.shape[0]
-action_range = 1.
-
-replay_buffer_size = 5e5
-replay_buffer = ReplayBuffer(replay_buffer_size)
-
-# hyper-parameters for RL training
-max_frames = 30000  # total number of steps for training
-test_frames = 300  # total number of steps for testing
-max_steps = 150  # maximum number of steps for one episode
-batch_size = 64  # udpate batchsize
-explore_steps = 100  # 500 for random action sampling in the beginning of training
-update_itr = 3  # repeated updates for single step
-hidden_dim = 32  # size of hidden layers for networks
-soft_q_lr = 3e-4  # q_net learning rate
-policy_lr = 3e-4  # policy_net learning rate
-alpha_lr = 3e-4  # alpha learning rate
-policy_target_update_interval = 3  # delayed update for the policy network and target networks
-# explore_noise_scale = 1.0           # range of action noise for exploration
-# eval_noise_scale = 0.5              # range of action noise for evaluation of action value
-reward_scale = 1.  # value range of reward
-
-AUTO_ENTROPY = True  # automatically udpating variable alpha for entropy
-DETERMINISTIC = False  # stochastic action policy if False, otherwise deterministic
-
-
-sac_trainer=SAC_Trainer(replay_buffer, hidden_dim=hidden_dim, action_range=action_range, \
-soft_q_lr=soft_q_lr, policy_lr=policy_lr, alpha_lr=alpha_lr )
-
-#set train mode
-sac_trainer.soft_q_net1.train()
-sac_trainer.soft_q_net2.train()
-sac_trainer.target_soft_q_net1.train()
-sac_trainer.target_soft_q_net2.train()
-sac_trainer.policy_net.train()
-
-# training loop
-if args.train:
-    frame_idx = 0
-    rewards = []
-    while frame_idx < max_frames:
-        state = env.reset()
-        state = state.astype(np.float32)
-        episode_reward = 0
-        if frame_idx < 1:
-            print('intialize')
-            _ = sac_trainer.policy_net(
-                [state]
-            )  # need an extra call here to make inside functions be able to use model.forward
-
-        for step in range(max_steps):
-            if frame_idx > explore_steps:
-                action = sac_trainer.policy_net.get_action(state, deterministic=DETERMINISTIC)
-            else:
-                action = sac_trainer.policy_net.sample_action()
-
-            next_state, reward, done, _ = env.step(action)
-            next_state = next_state.astype(np.float32)
-            env.render()
-            done = 1 if done ==True else 0
-
-            replay_buffer.push(state, action, reward, next_state, done)
-
-            state = next_state
-            episode_reward += reward
-            frame_idx += 1
-
-            if len(replay_buffer) > batch_size:
-                for i in range(update_itr):
-                    sac_trainer.update(
-                        batch_size, reward_scale=reward_scale, auto_entropy=AUTO_ENTROPY,
-                        target_entropy=-1. * action_dim
-                    )
-
-            if frame_idx % 500 == 0:
-                plot(rewards, Algorithm_name='SAC', Env_name=ENV)
-
-            if done:
-                break
-        print('Episode: ', frame_idx / max_steps, '| Episode Reward: ', episode_reward)
-        rewards.append(episode_reward)
-    sac_trainer.save_weights()
-
-if args.test:
-    frame_idx = 0
-    rewards = []
-    sac_trainer.load_weights()
-
-    while frame_idx < test_frames:
-        state = env.reset()
-        state = state.astype(np.float32)
-        episode_reward = 0
-        if frame_idx < 1:
-            print('intialize')
-            _ = sac_trainer.policy_net([state])  # need an extra call to make inside functions be able to use forward
-
-        for step in range(max_steps):
-            action = sac_trainer.policy_net.get_action(state, deterministic=DETERMINISTIC)
-            next_state, reward, done, _ = env.step(action)
-            next_state = next_state.astype(np.float32)
-            env.render()
-            done = 1 if done ==True else 0
-
-            state = next_state
-            episode_reward += reward
-            frame_idx += 1
-
-            # if frame_idx % 50 == 0:
-            #     plot(frame_idx, rewards)
-
-            if done:
-                break
-        print('Episode: ', frame_idx / max_steps, '| Episode Reward: ', episode_reward)
-        rewards.append(episode_reward)
diff --git a/examples/reinforcement_learning/baselines/utils.py b/examples/reinforcement_learning/baselines/utils.py
deleted file mode 100644
index 89d8ffe5d..000000000
--- a/examples/reinforcement_learning/baselines/utils.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""
-Functions for utilization.
-
-# Requirements
-tensorflow==2.0.0a0
-tensorlayer==2.0.1
-
-"""
-import os
-import random
-import time
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-import tensorlayer as tl
-
-
-def plot(episode_rewards, Algorithm_name, Env_name):
-    '''
-    plot the learning curve, saved as ./img/Algorithm_name.png
-    :episode_rewards: array of floats
-    :Algorithm_name: string
-    :Env_name: string
-    '''
-    plt.figure(figsize=(10, 5))
-    plt.title(Algorithm_name + '-' + Env_name)
-    plt.plot(np.arange(len(episode_rewards)), episode_rewards)
-    plt.xlabel('Episode')
-    plt.ylabel('Episode Reward')
-    if not os.path.exists('img'):
-        os.makedirs('img')
-    plt.savefig('./img/' + Algorithm_name + '.png')
-
-
-def save_model(model, Model_name, Algorithm_name):
-    '''
-    save trained neural network model
-    :model: tensorlayer.models.Model
-    :Model_name: string, e.g. 'model_sac_q1'
-    :Algorithm_name: string, e.g. 'SAC'
-    '''
-    if not os.path.exists('model/' + Algorithm_name):
-        os.makedirs('model/' + Algorithm_name)
-    tl.files.save_npz(model.trainable_weights, './model/' + Algorithm_name + '/' + Model_name)
-
-
-def load_model(model, Model_name, Algorithm_name):
-    '''
-    load saved neural network model
-    :model: tensorlayer.models.Model
-    :Model_name: string, e.g. 'model_sac_q1'
-    :Algorithm_name: string, e.g. 'SAC'
-    '''
-    try:
-        tl.files.load_and_assign_npz('./model/' + Algorithm_name + '/' + Model_name + '.npz', model)
-    except:
-        print('Load Model Fails!')
-
-
-class ReplayBuffer:
-    '''
-    a ring buffer for storing transitions and sampling for training
-    :state: (state_dim,)
-    :action: (action_dim,)
-    :reward: (,), scalar
-    :next_state: (state_dim,)
-    :done: (,), scalar (0 and 1) or bool (True and False)
-    '''
-
-    def __init__(self, capacity):
-        self.capacity = capacity  # mamimum number of samples
-        self.buffer = []
-        self.position = 0  # pointer
-
-    def push(self, state, action, reward, next_state, done):
-        if len(self.buffer) < self.capacity:
-            self.buffer.append(None)
-        self.buffer[self.position] = (state, action, reward, next_state, done)
-        self.position = int((self.position + 1) % self.capacity)  # as a ring buffer
-
-    def sample(self, batch_size):
-        batch = random.sample(self.buffer, batch_size)
-        state, action, reward, next_state, done = map(np.stack, zip(*batch))  # stack for each element
-        ''' 
-        the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
-        zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
-        the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
-        np.stack((1,2)) => array([1, 2])
-        '''
-        return state, action, reward, next_state, done
-
-    def __len__(self):
-        return len(self.buffer)
diff --git a/examples/reinforcement_learning/baselines/wrappers.py b/examples/reinforcement_learning/baselines/wrappers.py
deleted file mode 100644
index 4ae724d3a..000000000
--- a/examples/reinforcement_learning/baselines/wrappers.py
+++ /dev/null
@@ -1,565 +0,0 @@
-"""Env wrappers
-Note that this file is adapted from `https://pypi.org/project/gym-vec-env` and
-`https://github.com/openai/baselines/blob/master/baselines/common/*wrappers.py`
-"""
-from collections import deque
-from functools import partial
-from multiprocessing import Pipe, Process, cpu_count
-from sys import platform
-
-import numpy as np
-
-import cv2
-import gym
-from gym import spaces
-
-__all__ = (
-    'build_env',  # build env
-    'TimeLimit',  # Time limit wrapper
-    'NoopResetEnv',  # Run random number of no-ops on reset
-    'FireResetEnv',  # Reset wrapper for envs with fire action
-    'EpisodicLifeEnv',  # end-of-life == end-of-episode wrapper
-    'MaxAndSkipEnv',  # skip frame wrapper
-    'ClipRewardEnv',  # clip reward wrapper
-    'WarpFrame',  # warp observation wrapper
-    'FrameStack',  # stack frame wrapper
-    'LazyFrames',  # lazy store wrapper
-    'RewardScaler',  # reward scale
-    'SubprocVecEnv',  # vectorized env wrapper
-    'VecFrameStack',  # stack frames in vectorized env
-    'Monitor',  # Episode reward and length monitor
-    'NormalizedActions',  # normalized action to actual space
-)
-cv2.ocl.setUseOpenCL(False)
-# env_id -> env_type
-id2type = dict()
-for _env in gym.envs.registry.all():
-    id2type[_env.id] = _env._entry_point.split(':')[0].rsplit('.', 1)[1]
-
-
-def build_env(env_id, vectorized=False, seed=0, reward_scale=1.0, nenv=0):
-    """Build env based on options"""
-    env_type = id2type[env_id]
-    nenv = nenv or cpu_count() // (1 + (platform == 'darwin'))
-    stack = env_type == 'atari'
-    if not vectorized:
-        env = _make_env(env_id, env_type, seed, reward_scale, stack)
-    else:
-        env = _make_vec_env(env_id, env_type, nenv, seed, reward_scale, stack)
-
-    return env
-
-
-def _make_env(env_id, env_type, seed, reward_scale, frame_stack=True):
-    """Make single env"""
-    if env_type == 'atari':
-        env = gym.make(env_id)
-        assert 'NoFrameskip' in env.spec.id
-        env = NoopResetEnv(env, noop_max=30)
-        env = MaxAndSkipEnv(env, skip=4)
-        env = Monitor(env)
-        # deepmind wrap
-        env = EpisodicLifeEnv(env)
-        if 'FIRE' in env.unwrapped.get_action_meanings():
-            env = FireResetEnv(env)
-        env = WarpFrame(env)
-        env = ClipRewardEnv(env)
-        if frame_stack:
-            env = FrameStack(env, 4)
-    elif env_type == 'classic_control':
-        env = Monitor(gym.make(env_id))
-    else:
-        raise NotImplementedError
-    if reward_scale != 1:
-        env = RewardScaler(env, reward_scale)
-    env.seed(seed)
-    return env
-
-
-def _make_vec_env(env_id, env_type, nenv, seed, reward_scale, frame_stack=True):
-    """Make vectorized env"""
-    env = SubprocVecEnv([partial(_make_env, env_id, env_type, seed + i, reward_scale, False) for i in range(nenv)])
-    if frame_stack:
-        env = VecFrameStack(env, 4)
-    return env
-
-
-class TimeLimit(gym.Wrapper):
-
-    def __init__(self, env, max_episode_steps=None):
-        super(TimeLimit, self).__init__(env)
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = 0
-
-    def step(self, ac):
-        observation, reward, done, info = self.env.step(ac)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            done = True
-            info['TimeLimit.truncated'] = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
-
-
-class NoopResetEnv(gym.Wrapper):
-
-    def __init__(self, env, noop_max=30):
-        """Sample initial states by taking random number of no-ops on reset.
-        No-op is assumed to be action 0.
-        """
-        super(NoopResetEnv, self).__init__(env)
-        self.noop_max = noop_max
-        self.override_num_noops = None
-        self.noop_action = 0
-        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
-
-    def reset(self, **kwargs):
-        """ Do no-op action for a number of steps in [1, noop_max]."""
-        self.env.reset(**kwargs)
-        if self.override_num_noops is not None:
-            noops = self.override_num_noops
-        else:
-            noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
-        assert noops > 0
-        obs = None
-        for _ in range(noops):
-            obs, _, done, _ = self.env.step(self.noop_action)
-            if done:
-                obs = self.env.reset(**kwargs)
-        return obs
-
-    def step(self, ac):
-        return self.env.step(ac)
-
-
-class FireResetEnv(gym.Wrapper):
-
-    def __init__(self, env):
-        """Take action on reset for environments that are fixed until firing."""
-        super(FireResetEnv, self).__init__(env)
-        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
-        assert len(env.unwrapped.get_action_meanings()) >= 3
-
-    def reset(self, **kwargs):
-        self.env.reset(**kwargs)
-        obs, _, done, _ = self.env.step(1)
-        if done:
-            self.env.reset(**kwargs)
-        obs, _, done, _ = self.env.step(2)
-        if done:
-            self.env.reset(**kwargs)
-        return obs
-
-    def step(self, ac):
-        return self.env.step(ac)
-
-
-class EpisodicLifeEnv(gym.Wrapper):
-
-    def __init__(self, env):
-        """Make end-of-life == end-of-episode, but only reset on true game over.
-        Done by DeepMind for the DQN and co. since it helps value estimation.
-        """
-        super(EpisodicLifeEnv, self).__init__(env)
-        self.lives = 0
-        self.was_real_done = True
-
-    def step(self, action):
-        obs, reward, done, info = self.env.step(action)
-        self.was_real_done = done
-        # check current lives, make loss of life terminal,
-        # then update lives to handle bonus lives
-        lives = self.env.unwrapped.ale.lives()
-        if 0 < lives < self.lives:
-            # for Qbert sometimes we stay in lives == 0 condition for a few
-            # frames so it's important to keep lives > 0, so that we only reset
-            # once the environment advertises done.
-            done = True
-        self.lives = lives
-        return obs, reward, done, info
-
-    def reset(self, **kwargs):
-        """Reset only when lives are exhausted.
-        This way all states are still reachable even though lives are episodic,
-        and the learner need not know about any of this behind-the-scenes.
-        """
-        if self.was_real_done:
-            obs = self.env.reset(**kwargs)
-        else:
-            # no-op step to advance from terminal/lost life state
-            obs, _, _, _ = self.env.step(0)
-        self.lives = self.env.unwrapped.ale.lives()
-        return obs
-
-
-class MaxAndSkipEnv(gym.Wrapper):
-
-    def __init__(self, env, skip=4):
-        """Return only every `skip`-th frame"""
-        super(MaxAndSkipEnv, self).__init__(env)
-        # most recent raw observations (for max pooling across time steps)
-        shape = (2, ) + env.observation_space.shape
-        self._obs_buffer = np.zeros(shape, dtype=np.uint8)
-        self._skip = skip
-
-    def step(self, action):
-        """Repeat action, sum reward, and max over last observations."""
-        total_reward = 0.0
-        done = info = None
-        for i in range(self._skip):
-            obs, reward, done, info = self.env.step(action)
-            if i == self._skip - 2:
-                self._obs_buffer[0] = obs
-            if i == self._skip - 1:
-                self._obs_buffer[1] = obs
-            total_reward += reward
-            if done:
-                break
-        # Note that the observation on the done=True frame doesn't matter
-        max_frame = self._obs_buffer.max(axis=0)
-
-        return max_frame, total_reward, done, info
-
-    def reset(self, **kwargs):
-        return self.env.reset(**kwargs)
-
-
-class ClipRewardEnv(gym.RewardWrapper):
-
-    def __init__(self, env):
-        super(ClipRewardEnv, self).__init__(env)
-
-    def reward(self, reward):
-        """Bin reward to {+1, 0, -1} by its sign."""
-        return np.sign(reward)
-
-
-class WarpFrame(gym.ObservationWrapper):
-
-    def __init__(self, env, width=84, height=84, grayscale=True):
-        """Warp frames to 84x84 as done in the Nature paper and later work."""
-        super(WarpFrame, self).__init__(env)
-        self.width = width
-        self.height = height
-        self.grayscale = grayscale
-        shape = (self.height, self.width, 1 if self.grayscale else 3)
-        self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=np.uint8)
-
-    def observation(self, frame):
-        if self.grayscale:
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        size = (self.width, self.height)
-        frame = cv2.resize(frame, size, interpolation=cv2.INTER_AREA)
-        if self.grayscale:
-            frame = np.expand_dims(frame, -1)
-        return frame
-
-
-class FrameStack(gym.Wrapper):
-
-    def __init__(self, env, k):
-        """Stack k last frames.
-        Returns lazy array, which is much more memory efficient.
-        See Also `LazyFrames`
-        """
-        super(FrameStack, self).__init__(env)
-        self.k = k
-        self.frames = deque([], maxlen=k)
-        shp = env.observation_space.shape
-        shape = shp[:-1] + (shp[-1] * k, )
-        self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=env.observation_space.dtype)
-
-    def reset(self):
-        ob = self.env.reset()
-        for _ in range(self.k):
-            self.frames.append(ob)
-        return np.asarray(self._get_ob())
-
-    def step(self, action):
-        ob, reward, done, info = self.env.step(action)
-        self.frames.append(ob)
-        return np.asarray(self._get_ob()), reward, done, info
-
-    def _get_ob(self):
-        assert len(self.frames) == self.k
-        return LazyFrames(list(self.frames))
-
-
-class LazyFrames(object):
-
-    def __init__(self, frames):
-        """This object ensures that common frames between the observations are
-        only stored once. It exists purely to optimize memory usage which can be
-        huge for DQN's 1M frames replay buffers.
-
-        This object should only be converted to numpy array before being passed
-        to the model. You'd not believe how complex the previous solution was.
-        """
-        self._frames = frames
-        self._out = None
-
-    def _force(self):
-        if self._out is None:
-            self._out = np.concatenate(self._frames, axis=-1)
-            self._frames = None
-        return self._out
-
-    def __array__(self, dtype=None):
-        out = self._force()
-        if dtype is not None:
-            out = out.astype(dtype)
-        return out
-
-    def __len__(self):
-        return len(self._force())
-
-    def __getitem__(self, i):
-        return self._force()[i]
-
-
-class RewardScaler(gym.RewardWrapper):
-    """Bring rewards to a reasonable scale for PPO.
-    This is incredibly important and effects performance drastically.
-    """
-
-    def __init__(self, env, scale=0.01):
-        super(RewardScaler, self).__init__(env)
-        self.scale = scale
-
-    def reward(self, reward):
-        return reward * self.scale
-
-
-class VecFrameStack(object):
-
-    def __init__(self, env, k):
-        self.env = env
-        self.k = k
-        self.action_space = env.action_space
-        self.frames = deque([], maxlen=k)
-        shp = env.observation_space.shape
-        shape = shp[:-1] + (shp[-1] * k, )
-        self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=env.observation_space.dtype)
-
-    def reset(self):
-        ob = self.env.reset()
-        for _ in range(self.k):
-            self.frames.append(ob)
-        return np.asarray(self._get_ob())
-
-    def step(self, action):
-        ob, reward, done, info = self.env.step(action)
-        self.frames.append(ob)
-        return np.asarray(self._get_ob()), reward, done, info
-
-    def _get_ob(self):
-        assert len(self.frames) == self.k
-        return LazyFrames(list(self.frames))
-
-
-def _worker(remote, parent_remote, env_fn_wrapper):
-    parent_remote.close()
-    env = env_fn_wrapper.x()
-    while True:
-        cmd, data = remote.recv()
-        if cmd == 'step':
-            ob, reward, done, info = env.step(data)
-            if done:
-                ob = env.reset()
-            remote.send((ob, reward, done, info))
-        elif cmd == 'reset':
-            ob = env.reset()
-            remote.send(ob)
-        elif cmd == 'reset_task':
-            ob = env._reset_task()
-            remote.send(ob)
-        elif cmd == 'close':
-            remote.close()
-            break
-        elif cmd == 'get_spaces':
-            remote.send((env.observation_space, env.action_space))
-        else:
-            raise NotImplementedError
-
-
-class CloudpickleWrapper(object):
-    """
-    Uses cloudpickle to serialize contents
-    """
-
-    def __init__(self, x):
-        self.x = x
-
-    def __getstate__(self):
-        import cloudpickle
-        return cloudpickle.dumps(self.x)
-
-    def __setstate__(self, ob):
-        import pickle
-        self.x = pickle.loads(ob)
-
-
-class SubprocVecEnv(object):
-
-    def __init__(self, env_fns):
-        """
-        envs: list of gym environments to run in subprocesses
-        """
-        self.num_envs = len(env_fns)
-
-        self.waiting = False
-        self.closed = False
-        nenvs = len(env_fns)
-        self.nenvs = nenvs
-        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
-        zipped_args = zip(self.work_remotes, self.remotes, env_fns)
-        self.ps = [
-            Process(target=_worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
-            for (work_remote, remote, env_fn) in zipped_args
-        ]
-
-        for p in self.ps:
-            # if the main process crashes, we should not cause things to hang
-            p.daemon = True
-            p.start()
-        for remote in self.work_remotes:
-            remote.close()
-
-        self.remotes[0].send(('get_spaces', None))
-        observation_space, action_space = self.remotes[0].recv()
-        self.observation_space = observation_space
-        self.action_space = action_space
-
-    def _step_async(self, actions):
-        """
-            Tell all the environments to start taking a step
-            with the given actions.
-            Call step_wait() to get the results of the step.
-            You should not call this if a step_async run is
-            already pending.
-            """
-        for remote, action in zip(self.remotes, actions):
-            remote.send(('step', action))
-        self.waiting = True
-
-    def _step_wait(self):
-        """
-            Wait for the step taken with step_async().
-            Returns (obs, rews, dones, infos):
-             - obs: an array of observations, or a tuple of
-                    arrays of observations.
-             - rews: an array of rewards
-             - dones: an array of "episode done" booleans
-             - infos: a sequence of info objects
-            """
-        results = [remote.recv() for remote in self.remotes]
-        self.waiting = False
-        obs, rews, dones, infos = zip(*results)
-        return np.stack(obs), np.stack(rews), np.stack(dones), infos
-
-    def reset(self):
-        """
-            Reset all the environments and return an array of
-            observations, or a tuple of observation arrays.
-            If step_async is still doing work, that work will
-            be cancelled and step_wait() should not be called
-            until step_async() is invoked again.
-            """
-        for remote in self.remotes:
-            remote.send(('reset', None))
-        return np.stack([remote.recv() for remote in self.remotes])
-
-    def _reset_task(self):
-        for remote in self.remotes:
-            remote.send(('reset_task', None))
-        return np.stack([remote.recv() for remote in self.remotes])
-
-    def close(self):
-        if self.closed:
-            return
-        if self.waiting:
-            for remote in self.remotes:
-                remote.recv()
-        for remote in self.remotes:
-            remote.send(('close', None))
-        for p in self.ps:
-            p.join()
-            self.closed = True
-
-    def __len__(self):
-        return self.nenvs
-
-    def step(self, actions):
-        self._step_async(actions)
-        return self._step_wait()
-
-
-class Monitor(gym.Wrapper):
-
-    def __init__(self, env):
-        super(Monitor, self).__init__(env)
-        self._monitor_rewards = None
-
-    def reset(self, **kwargs):
-        self._monitor_rewards = []
-        return self.env.reset(**kwargs)
-
-    def step(self, action):
-        o_, r, done, info = self.env.step(action)
-        self._monitor_rewards.append(r)
-        if done:
-            info['episode'] = {'r': sum(self._monitor_rewards), 'l': len(self._monitor_rewards)}
-        return o_, r, done, info
-
-
-class NormalizedActions(gym.ActionWrapper):
-
-    def _action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = low + (action + 1.0) * 0.5 * (high - low)
-        action = np.clip(action, low, high)
-
-        return action
-
-    def _reverse_action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = 2 * (action - low) / (high - low) - 1
-        action = np.clip(action, low, high)
-
-        return action
-
-
-def unit_test():
-    env_id = 'CartPole-v0'
-    unwrapped_env = gym.make(env_id)
-    wrapped_env = build_env(env_id, False)
-    o = wrapped_env.reset()
-    print('Reset {} observation shape {}'.format(env_id, o.shape))
-    done = False
-    while not done:
-        a = unwrapped_env.action_space.sample()
-        o_, r, done, info = wrapped_env.step(a)
-        print('Take action {} get reward {} info {}'.format(a, r, info))
-
-    env_id = 'PongNoFrameskip-v4'
-    nenv = 2
-    unwrapped_env = gym.make(env_id)
-    wrapped_env = build_env(env_id, True, nenv=nenv)
-    o = wrapped_env.reset()
-    print('Reset {} observation shape {}'.format(env_id, o.shape))
-    for _ in range(1000):
-        a = [unwrapped_env.action_space.sample() for _ in range(nenv)]
-        a = np.asarray(a, 'int64')
-        o_, r, done, info = wrapped_env.step(a)
-        print('Take action {} get reward {} info {}'.format(a, r, info))
-
-
-if __name__ == '__main__':
-    unit_test()
diff --git a/examples/reinforcement_learning/tutorial_A3C.py b/examples/reinforcement_learning/tutorial_A3C.py
index f904e7c4b..f20530ebf 100644
--- a/examples/reinforcement_learning/tutorial_A3C.py
+++ b/examples/reinforcement_learning/tutorial_A3C.py
@@ -46,24 +46,22 @@
 
 import argparse
 import multiprocessing
+import os
 import threading
 import time
 
-import numpy as np
-
 import gym
+import matplotlib.pyplot as plt
+import numpy as np
 import tensorflow as tf
+
 import tensorflow_probability as tfp
 import tensorlayer as tl
-from tensorlayer.layers import DenseLayer, InputLayer
 
 tfd = tfp.distributions
 
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-np.random.seed(2)
-tf.random.set_seed(2)  # reproducible
-
 # add arguments in command  --train/test
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
 parser.add_argument('--train', dest='train', action='store_true', default=False)
@@ -72,11 +70,15 @@
 
 #####################  hyper parameters  ####################
 
-GAME = 'BipedalWalker-v2'  # BipedalWalkerHardcore-v2   BipedalWalker-v2  LunarLanderContinuous-v2
-LOG_DIR = './log'  # the log file
-N_WORKERS = multiprocessing.cpu_count()  # number of workers accroding to number of cores in cpu
+ENV_ID = 'BipedalWalker-v2'  # BipedalWalkerHardcore-v2   BipedalWalker-v2  LunarLanderContinuous-v2
+RANDOM_SEED = 2  # random seed, can be either an int number or None
+RENDER = False  # render while training
+
+ALG_NAME = 'A3C'
+N_WORKERS = multiprocessing.cpu_count()  # number of workers according to number of cores in cpu
 # N_WORKERS = 2     # manually set number of workers
-MAX_GLOBAL_EP = 8  # number of training episodes
+MAX_GLOBAL_EP = 15000  # number of training episodes
+TEST_EPISODES = 10  # number of training episodes
 GLOBAL_NET_SCOPE = 'Global_Net'
 UPDATE_GLOBAL_ITER = 10  # update global policy after several episodes
 GAMMA = 0.99  # reward discount factor
@@ -91,9 +93,8 @@
 
 class ACNet(object):
 
-    def __init__(self, scope, globalAC=None):
+    def __init__(self, scope):
         self.scope = scope
-        self.save_path = './model'
 
         w_init = tf.keras.initializers.glorot_normal(seed=None)  # initializer, glorot=xavier
 
@@ -157,31 +158,37 @@ def pull_global(self, globalAC):  # run by a local, pull weights from the global
         for l_p, g_p in zip(self.critic.trainable_weights, globalAC.critic.trainable_weights):
             l_p.assign(g_p)
 
-    def choose_action(self, s):  # run by a local
+    def get_action(self, s, greedy=False):  # run by a local
         s = s[np.newaxis, :]
         self.mu, self.sigma = self.actor(s)
 
         with tf.name_scope('wrap_a_out'):
             self.mu, self.sigma = self.mu * A_BOUND[1], self.sigma + 1e-5
+        if greedy:
+            return self.mu.numpy()[0]
         normal_dist = tfd.Normal(self.mu, self.sigma)  # for continuous action space
         self.A = tf.clip_by_value(tf.squeeze(normal_dist.sample(1), axis=0), *A_BOUND)
         return self.A.numpy()[0]
 
-    def save_ckpt(self):  # save trained weights
-        tl.files.save_npz(self.actor.trainable_weights, name='model_actor.npz')
-        tl.files.save_npz(self.critic.trainable_weights, name='model_critic.npz')
+    def save(self):  # save trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_npz(self.actor.trainable_weights, name=os.path.join(path, 'model_actor.npz'))
+        tl.files.save_npz(self.critic.trainable_weights, name=os.path.join(path, 'model_critic.npz'))
 
-    def load_ckpt(self):  # load trained weights
-        tl.files.load_and_assign_npz(name='model_actor.npz', network=self.actor)
-        tl.files.load_and_assign_npz(name='model_critic.npz', network=self.critic)
+    def load(self):  # load trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_and_assign_npz(name=os.path.join(path, 'model_actor.npz'), network=self.actor)
+        tl.files.load_and_assign_npz(name=os.path.join(path, 'model_critic.npz'), network=self.critic)
 
 
 class Worker(object):
 
-    def __init__(self, name, globalAC):
-        self.env = gym.make(GAME)
+    def __init__(self, name):
+        self.env = gym.make(ENV_ID)
         self.name = name
-        self.AC = ACNet(name, globalAC)
+        self.AC = ACNet(name)
 
     # def work(self):
     def work(self, globalAC):
@@ -193,10 +200,10 @@ def work(self, globalAC):
             ep_r = 0
             while True:
                 # visualize Worker_0 during training
-                if self.name == 'Worker_0' and total_step % 30 == 0:
+                if RENDER and self.name == 'Worker_0' and total_step % 30 == 0:
                     self.env.render()
                 s = s.astype('float32')  # double to float
-                a = self.AC.choose_action(s)
+                a = self.AC.get_action(s)
                 s_, r, done, _info = self.env.step(a)
 
                 s_ = s_.astype('float32')  # double to float
@@ -223,11 +230,12 @@ def work(self, globalAC):
 
                     buffer_v_target.reverse()
 
-                    buffer_s, buffer_a, buffer_v_target = (
-                        np.vstack(buffer_s), np.vstack(buffer_a), np.vstack(buffer_v_target)
-                    )
+                    buffer_s = tf.convert_to_tensor(np.vstack(buffer_s))
+                    buffer_a = tf.convert_to_tensor(np.vstack(buffer_a))
+                    buffer_v_target = tf.convert_to_tensor(np.vstack(buffer_v_target).astype('float32'))
+
                     # update gradients on global network
-                    self.AC.update_global(buffer_s, buffer_a, buffer_v_target.astype('float32'), globalAC)
+                    self.AC.update_global(buffer_s, buffer_a, buffer_v_target, globalAC)
                     buffer_s, buffer_a, buffer_r = [], [], []
 
                     # update local network from global network
@@ -240,25 +248,18 @@ def work(self, globalAC):
                         GLOBAL_RUNNING_R.append(ep_r)
                     else:  # moving average
                         GLOBAL_RUNNING_R.append(0.95 * GLOBAL_RUNNING_R[-1] + 0.05 * ep_r)
-                    # print(
-                    #     self.name,
-                    #     "Episode: ",
-                    #     GLOBAL_EP,
-                    #     # "| pos: %i" % self.env.unwrapped.hull.position[0],  # number of move
-                    #     '| reward: %.1f' % ep_r,
-                    #     "| running_reward: %.1f" % GLOBAL_RUNNING_R[-1],
-                    #     # '| sigma:', test, # debug
-                    #     # 'WIN ' * 5 if self.env.unwrapped.hull.position[0] >= 88 else '',
-                    # )
-                    print('{}, Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'\
-                    .format(self.name, GLOBAL_EP, MAX_GLOBAL_EP, ep_r, time.time()-t0 ))
+                    print('Training  | {}, Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}' \
+                          .format(self.name, GLOBAL_EP, MAX_GLOBAL_EP, ep_r, time.time() - T0))
                     GLOBAL_EP += 1
                     break
 
 
 if __name__ == "__main__":
 
-    env = gym.make(GAME)
+    env = gym.make(ENV_ID)
+    # reproducible
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
 
     N_S = env.observation_space.shape[0]
     N_A = env.action_space.shape[0]
@@ -266,56 +267,57 @@ def work(self, globalAC):
     A_BOUND = [env.action_space.low, env.action_space.high]
     A_BOUND[0] = A_BOUND[0].reshape(1, N_A)
     A_BOUND[1] = A_BOUND[1].reshape(1, N_A)
-    # print(A_BOUND)
+
+    with tf.device("/cpu:0"):
+        GLOBAL_AC = ACNet(GLOBAL_NET_SCOPE)  # we only need its params
+
+    T0 = time.time()
     if args.train:
         # ============================= TRAINING ===============================
-        t0 = time.time()
         with tf.device("/cpu:0"):
-
             OPT_A = tf.optimizers.RMSprop(LR_A, name='RMSPropA')
             OPT_C = tf.optimizers.RMSprop(LR_C, name='RMSPropC')
-
-            GLOBAL_AC = ACNet(GLOBAL_NET_SCOPE)  # we only need its params
             workers = []
             # Create worker
             for i in range(N_WORKERS):
                 i_name = 'Worker_%i' % i  # worker name
-                workers.append(Worker(i_name, GLOBAL_AC))
+                workers.append(Worker(i_name))
 
         COORD = tf.train.Coordinator()
 
         # start TF threading
         worker_threads = []
         for worker in workers:
-            # t = threading.Thread(target=worker.work)
             job = lambda: worker.work(GLOBAL_AC)
             t = threading.Thread(target=job)
             t.start()
             worker_threads.append(t)
         COORD.join(worker_threads)
-        import matplotlib.pyplot as plt
-        plt.plot(GLOBAL_RUNNING_R)
-        plt.xlabel('episode')
-        plt.ylabel('global running reward')
-        plt.savefig('a3c.png')
-        plt.show()
 
-        GLOBAL_AC.save_ckpt()
+        GLOBAL_AC.save()
+
+        plt.plot(GLOBAL_RUNNING_R)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
 
     if args.test:
         # ============================= EVALUATION =============================
-        # env = gym.make(GAME)
-        # GLOBAL_AC = ACNet(GLOBAL_NET_SCOPE)
-        GLOBAL_AC.load_ckpt()
-        while True:
+        GLOBAL_AC.load()
+        for episode in range(TEST_EPISODES):
             s = env.reset()
-            rall = 0
+            episode_reward = 0
             while True:
                 env.render()
                 s = s.astype('float32')  # double to float
-                a = GLOBAL_AC.choose_action(s)
+                a = GLOBAL_AC.get_action(s, greedy=True)
                 s, r, d, _ = env.step(a)
-                rall += r
+                episode_reward += r
                 if d:
-                    print("reward", rall)
                     break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - T0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_AC.py b/examples/reinforcement_learning/tutorial_AC.py
index 0bee2735d..c497e714a 100644
--- a/examples/reinforcement_learning/tutorial_AC.py
+++ b/examples/reinforcement_learning/tutorial_AC.py
@@ -47,18 +47,17 @@
 """
 import argparse
 import time
-
-import numpy as np
+import matplotlib.pyplot as plt
+import os
 
 import gym
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-np.random.seed(2)
-tf.random.set_seed(2)  # reproducible
-
 # add arguments in command  --train/test
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
 parser.add_argument('--train', dest='train', action='store_true', default=False)
@@ -67,102 +66,100 @@
 
 #####################  hyper parameters  ####################
 
-OUTPUT_GRAPH = False
-MAX_EPISODE = 3000  # number of overall episodes for training
-DISPLAY_REWARD_THRESHOLD = 100  # renders environment if running reward is greater then this threshold
-MAX_EP_STEPS = 1000  # maximum time step in one episode
-RENDER = False  # rendering wastes time
-LAMBDA = 0.9  # reward discount in TD error
+ENV_ID = 'CartPole-v1'  # environment id
+RANDOM_SEED = 2  # random seed, can be either an int number or None
+RENDER = False  # render while training
+
+ALG_NAME = 'AC'
+TRAIN_EPISODES = 200  # number of overall episodes for training
+TEST_EPISODES = 10  # number of overall episodes for testing
+MAX_STEPS = 500  # maximum time step in one episode
+LAM = 0.9  # reward discount in TD error
 LR_A = 0.001  # learning rate for actor
 LR_C = 0.01  # learning rate for critic
 
+
+
 ###############################  Actor-Critic  ####################################
 
 
 class Actor(object):
 
-    def __init__(self, n_features, n_actions, lr=0.001):
+    def __init__(self, state_dim, action_num, lr=0.001):
 
-        def get_model(inputs_shape):
-            ni = tl.layers.Input(inputs_shape, name='state')
-            nn = tl.layers.Dense(
-                n_units=30, act=tf.nn.relu6, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden'
-            )(ni)
-            nn = tl.layers.Dense(
-                n_units=10, act=tf.nn.relu6, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden2'
-            )(nn)
-            nn = tl.layers.Dense(n_units=n_actions, name='actions')(nn)
-            return tl.models.Model(inputs=ni, outputs=nn, name="Actor")
+        input_layer = tl.layers.Input([None, state_dim], name='state')
+        layer = tl.layers.Dense(
+            n_units=30, act=tf.nn.relu6, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden'
+        )(input_layer)
+        layer = tl.layers.Dense(n_units=action_num, name='actions')(layer)
+        self.model = tl.models.Model(inputs=input_layer, outputs=layer, name="Actor")
 
-        self.model = get_model([None, n_features])
         self.model.train()
         self.optimizer = tf.optimizers.Adam(lr)
 
-    def learn(self, s, a, td):
+    def learn(self, state, action, td_error):
         with tf.GradientTape() as tape:
-            _logits = self.model(np.array([s]))
+            _logits = self.model(np.array([state]))
             ## cross-entropy loss weighted by td-error (advantage),
             # the cross-entropy mearsures the difference of two probability distributions: the predicted logits and sampled action distribution,
             # then weighted by the td-error: small difference of real and predict actions for large td-error (advantage); and vice versa.
-            _exp_v = tl.rein.cross_entropy_reward_loss(logits=_logits, actions=[a], rewards=td[0])
+            _exp_v = tl.rein.cross_entropy_reward_loss(logits=_logits, actions=[action], rewards=td_error[0])
         grad = tape.gradient(_exp_v, self.model.trainable_weights)
         self.optimizer.apply_gradients(zip(grad, self.model.trainable_weights))
         return _exp_v
 
-    def choose_action(self, s):
-        _logits = self.model(np.array([s]))
+    def get_action(self, state, greedy=False):
+        _logits = self.model(np.array([state]))
         _probs = tf.nn.softmax(_logits).numpy()
+        if greedy:
+            return np.argmax(_probs.ravel())
         return tl.rein.choice_action_by_probs(_probs.ravel())  # sample according to probability distribution
 
-    def choose_action_greedy(self, s):
-        _logits = self.model(np.array([s]))  # logits: probability distribution of actions
-        _probs = tf.nn.softmax(_logits).numpy()
-        return np.argmax(_probs.ravel())
-
-    def save_ckpt(self):  # save trained weights
-        tl.files.save_npz(self.model.trainable_weights, name='model_actor.npz')
+    def save(self):  # save trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_npz(self.model.trainable_weights, name=os.path.join(path, 'model_actor.npz'))
 
-    def load_ckpt(self):  # load trained weights
-        tl.files.load_and_assign_npz(name='model_actor.npz', network=self.model)
+    def load(self):  # load trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_and_assign_npz(name=os.path.join(path, 'model_actor.npz'), network=self.model)
 
 
 class Critic(object):
 
-    def __init__(self, n_features, lr=0.01):
-
-        def get_model(inputs_shape):
-            ni = tl.layers.Input(inputs_shape, name='state')
-            nn = tl.layers.Dense(
-                n_units=30, act=tf.nn.relu6, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden'
-            )(ni)
-            nn = tl.layers.Dense(
-                n_units=5, act=tf.nn.relu, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden2'
-            )(nn)
-            nn = tl.layers.Dense(n_units=1, act=None, name='value')(nn)
-            return tl.models.Model(inputs=ni, outputs=nn, name="Critic")
-
-        self.model = get_model([1, n_features])
+    def __init__(self, state_dim, lr=0.01):
+        input_layer = tl.layers.Input([1, state_dim], name='state')
+        layer = tl.layers.Dense(
+            n_units=30, act=tf.nn.relu6, W_init=tf.random_uniform_initializer(0, 0.01), name='hidden'
+        )(input_layer)
+        layer = tl.layers.Dense(n_units=1, act=None, name='value')(layer)
+        self.model = tl.models.Model(inputs=input_layer, outputs=layer, name="Critic")
         self.model.train()
 
         self.optimizer = tf.optimizers.Adam(lr)
 
-    def learn(self, s, r, s_):
-        v_ = self.model(np.array([s_]))
+    def learn(self, state, reward, state_, done):
+        d = 0 if done else 1
+        v_ = self.model(np.array([state_]))
         with tf.GradientTape() as tape:
-            v = self.model(np.array([s]))
-            ## TD_error = r + lambd * V(newS) - V(S)
-            td_error = r + LAMBDA * v_ - v
+            v = self.model(np.array([state]))
+            ## TD_error = r + d * lambda * V(newS) - V(S)
+            td_error = reward + d * LAM * v_ - v
             loss = tf.square(td_error)
         grad = tape.gradient(loss, self.model.trainable_weights)
         self.optimizer.apply_gradients(zip(grad, self.model.trainable_weights))
-
         return td_error
 
-    def save_ckpt(self):  # save trained weights
-        tl.files.save_npz(self.model.trainable_weights, name='model_critic.npz')
+    def save(self):  # save trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_npz(self.model.trainable_weights, name=os.path.join(path, 'model_critic.npz'))
 
-    def load_ckpt(self):  # load trained weights
-        tl.files.load_and_assign_npz(name='model_critic.npz', network=self.model)
+    def load(self):  # load trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_and_assign_npz(name=os.path.join(path, 'model_critic.npz'), network=self.model)
 
 
 if __name__ == '__main__':
@@ -173,14 +170,16 @@ def load_ckpt(self):  # load trained weights
     2. DeepMind Control Suite:
     env = dm_control2gym.make()
     '''
-    env = gym.make('CartPole-v0')
+    env = gym.make(ENV_ID).unwrapped
     # dm_control2gym.create_render_mode('example mode', show=True, return_pixel=False, height=240, width=320, camera_id=-1, overlays=(),
     #              depth=False, scene_option=None)
     # env = dm_control2gym.make(domain_name="cartpole", task_name="balance")
-    env.seed(2)  # reproducible
-    # env = env.unwrapped
+
+    env.seed(RANDOM_SEED)  # reproducible
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)  # reproducible
+
     N_F = env.observation_space.shape[0]
-    # N_A = env.action_space.shape[0]
     N_A = env.action_space.n
 
     print("observation dimension: %d" % N_F)  # 4
@@ -188,27 +187,26 @@ def load_ckpt(self):  # load trained weights
     print("observation low : %s" % env.observation_space.low)  # [-2.4 , -inf , -0.41887902 , -inf]
     print("num of actions: %d" % N_A)  # 2 : left or right
 
-    actor = Actor(n_features=N_F, n_actions=N_A, lr=LR_A)
+    actor = Actor(state_dim=N_F, action_num=N_A, lr=LR_A)
     # we need a good teacher, so the teacher should learn faster than the actor
-    critic = Critic(n_features=N_F, lr=LR_C)
+    critic = Critic(state_dim=N_F, lr=LR_C)
 
+    t0 = time.time()
     if args.train:
-        t0 = time.time()
-        for i_episode in range(MAX_EPISODE):
-            # episode_time = time.time()
-            s = env.reset().astype(np.float32)
-            t = 0  # number of step in this episode
-            all_r = []  # rewards of all steps
+        all_episode_reward = []
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset().astype(np.float32)
+            step = 0  # number of step in this episode
+            episode_reward = 0  # rewards of all steps
             while True:
-
                 if RENDER: env.render()
 
-                a = actor.choose_action(s)
+                action = actor.get_action(state)
 
-                s_new, r, done, info = env.step(a)
-                s_new = s_new.astype(np.float32)
+                state_new, reward, done, info = env.step(action)
+                state_new = state_new.astype(np.float32)
 
-                if done: r = -20
+                if done: reward = -20   # reward shaping trick
                 # these may helpful in some tasks
                 # if abs(s_new[0]) >= env.observation_space.high[0]:
                 # #  cart moves more than 2.4 units from the center
@@ -216,112 +214,64 @@ def load_ckpt(self):  # load trained weights
                 # reward for the distance between cart to the center
                 # r -= abs(s_new[0])  * .1
 
-                all_r.append(r)
+                episode_reward += reward
 
-                td_error = critic.learn(
-                    s, r, s_new
-                )  # learn Value-function : gradient = grad[r + lambda * V(s_new) - V(s)]
                 try:
-                    actor.learn(s, a, td_error)  # learn Policy : true_gradient = grad[logPi(s, a) * td_error]
+                    td_error = critic.learn(
+                        state, reward, state_new, done
+                    )  # learn Value-function : gradient = grad[r + lambda * V(s_new) - V(s)]
+                    actor.learn(state, action, td_error)  # learn Policy : true_gradient = grad[logPi(s, a) * td_error]
                 except KeyboardInterrupt:  # if Ctrl+C at running actor.learn(), then save model, or exit if not at actor.learn()
-                    actor.save_ckpt()
-                    critic.save_ckpt()
-                    # logging
+                    actor.save()
+                    critic.save()
 
-                s = s_new
-                t += 1
+                state = state_new
+                step += 1
 
-                if done or t >= MAX_EP_STEPS:
-                    ep_rs_sum = sum(all_r)
-
-                    if 'running_reward' not in globals():
-                        running_reward = ep_rs_sum
-                    else:
-                        running_reward = running_reward * 0.95 + ep_rs_sum * 0.05
-                    # start rending if running_reward greater than a threshold
-                    # if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True
-                    # print("Episode: %d reward: %f running_reward %f took: %.5f" % \
-                    #     (i_episode, ep_rs_sum, running_reward, time.time() - episode_time))
-                    print('Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'\
-                    .format(i_episode, MAX_EPISODE, ep_rs_sum, time.time()-t0 ))
-
-                    # Early Stopping for quick check
-                    if t >= MAX_EP_STEPS:
-                        print("Early Stopping")
-                        s = env.reset().astype(np.float32)
-                        rall = 0
-                        while True:
-                            env.render()
-                            # a = actor.choose_action(s)
-                            a = actor.choose_action_greedy(s)  # Hao Dong: it is important for this task
-                            s_new, r, done, info = env.step(a)
-                            s_new = np.concatenate((s_new[0:N_F], s[N_F:]), axis=0).astype(np.float32)
-                            rall += r
-                            s = s_new
-                            if done:
-                                print("reward", rall)
-                                s = env.reset().astype(np.float32)
-                                rall = 0
+                if done or step >= MAX_STEPS:
                     break
-        actor.save_ckpt()
-        critic.save_ckpt()
+
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+
+            print('Training  | Episode: {}/{}  | Episode Reward: {:.0f}  | Running Time: {:.4f}' \
+                  .format(episode + 1, TRAIN_EPISODES, episode_reward, time.time() - t0))
+
+            # Early Stopping for quick check
+            if step >= MAX_STEPS:
+                print("Early Stopping")     # Hao Dong: it is important for this task
+                break
+        actor.save()
+        critic.save()
+
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
 
     if args.test:
-        actor.load_ckpt()
-        critic.load_ckpt()
-        t0 = time.time()
+        actor.load()
+        critic.load()
 
-        for i_episode in range(MAX_EPISODE):
+        for episode in range(TEST_EPISODES):
             episode_time = time.time()
-            s = env.reset().astype(np.float32)
+            state = env.reset().astype(np.float32)
             t = 0  # number of step in this episode
-            all_r = []  # rewards of all steps
+            episode_reward = 0
             while True:
-                if RENDER: env.render()
-                a = actor.choose_action(s)
-                s_new, r, done, info = env.step(a)
-                s_new = s_new.astype(np.float32)
-                if done: r = -20
-                # these may helpful in some tasks
-                # if abs(s_new[0]) >= env.observation_space.high[0]:
-                # #  cart moves more than 2.4 units from the center
-                #     r = -20
-                # reward for the distance between cart to the center
-                # r -= abs(s_new[0])  * .1
-
-                all_r.append(r)
-                s = s_new
+                env.render()
+                action = actor.get_action(state, greedy=True)
+                state_new, reward, done, info = env.step(action)
+                state_new = state_new.astype(np.float32)
+                if done: reward = -20
+
+                episode_reward += reward
+                state = state_new
                 t += 1
 
-                if done or t >= MAX_EP_STEPS:
-                    ep_rs_sum = sum(all_r)
-
-                    if 'running_reward' not in globals():
-                        running_reward = ep_rs_sum
-                    else:
-                        running_reward = running_reward * 0.95 + ep_rs_sum * 0.05
-                    # start rending if running_reward greater than a threshold
-                    # if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True
-                    # print("Episode: %d reward: %f running_reward %f took: %.5f" % \
-                    #     (i_episode, ep_rs_sum, running_reward, time.time() - episode_time))
-                    print('Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'\
-                    .format(i_episode, MAX_EPISODE, ep_rs_sum, time.time()-t0 ))
-
-                    # Early Stopping for quick check
-                    if t >= MAX_EP_STEPS:
-                        print("Early Stopping")
-                        s = env.reset().astype(np.float32)
-                        rall = 0
-                        while True:
-                            env.render()
-                            # a = actor.choose_action(s)
-                            a = actor.choose_action_greedy(s)  # Hao Dong: it is important for this task
-                            s_new, r, done, info = env.step(a)
-                            s_new = np.concatenate((s_new[0:N_F], s[N_F:]), axis=0).astype(np.float32)
-                            rall += r
-                            s = s_new
-                            if done:
-                                print("reward", rall)
-                                s = env.reset().astype(np.float32)
-                                rall = 0
+                if done or t >= MAX_STEPS:
+                    print('Testing  | Episode: {}/{}  | Episode Reward: {:.0f}  | Running Time: {:.4f}' \
+                          .format(episode + 1, TEST_EPISODES, episode_reward, time.time() - t0))
                     break
diff --git a/examples/reinforcement_learning/tutorial_C51.py b/examples/reinforcement_learning/tutorial_C51.py
index 0ff50aa55..50b82d66e 100644
--- a/examples/reinforcement_learning/tutorial_C51.py
+++ b/examples/reinforcement_learning/tutorial_C51.py
@@ -1,308 +1,343 @@
-"""
-C51 Algorithm
-------------------------
-Categorical 51 distributional RL algorithm, 51 means the number of atoms. In
-this algorithm, instead of estimating actual expected value, value distribution
-over a series of continuous sub-intervals (atoms) is considered.
-
-
-Reference:
-------------------------
-Bellemare M G, Dabney W, Munos R. A distributional perspective on reinforcement
-learning[C]//Proceedings of the 34th International Conference on Machine
-Learning-Volume 70. JMLR. org, 2017: 449-458.
-
-
-Environment:
-------------------------
-Cartpole and Pong in OpenAI Gym
-
-
-Requirements:
-------------------------
-tensorflow>=2.0.0a0
-tensorlayer>=2.0.0
-
-
-To run:
-------------------------
-python tutorial_C51.py --mode=train
-python tutorial_C51.py --mode=test --save_path=c51/8000.npz
-"""
-import argparse
-import os
-import random
-import time
-
-import numpy as np
-
-import tensorflow as tf
-import tensorlayer as tl
-from tutorial_wrappers import build_env
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--mode', help='train or test', default='train')
-parser.add_argument(
-    '--save_path', default='c51', help='folder to save if mode == train else model path,'
-    'qnet will be saved once target net update'
-)
-parser.add_argument('--seed', help='random seed', type=int, default=0)
-parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
-args = parser.parse_args()
-
-if args.mode == 'train':
-    os.makedirs(args.save_path, exist_ok=True)
-random.seed(args.seed)
-np.random.seed(args.seed)
-tf.random.set_seed(args.seed)  # reproducible
-env_id = args.env_id
-env = build_env(env_id, seed=args.seed)
-
-# ####################  hyper parameters  ####################
-if env_id == 'CartPole-v0':
-    qnet_type = 'MLP'
-    number_timesteps = 10000  # total number of time steps to train on
-    explore_timesteps = 100
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 5e-3  # learning rate
-    buffer_size = 1000  # replay buffer size
-    target_q_update_freq = 50  # how frequency target q net update
-    ob_scale = 1.0  # scale observations
-else:
-    # reward will increase obviously after 1e5 time steps
-    qnet_type = 'CNN'
-    number_timesteps = int(1e6)  # total number of time steps to train on
-    explore_timesteps = 1e5
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 1e-4  # learning rate
-    buffer_size = 10000  # replay buffer size
-    target_q_update_freq = 200  # how frequency target q net update
-    ob_scale = 1.0 / 255  # scale observations
-
-in_dim = env.observation_space.shape
-out_dim = env.action_space.n
-reward_gamma = 0.99  # reward discount
-batch_size = 32  # batch size for sampling from replay buffer
-warm_start = buffer_size / 10  # sample times before learning
-atom_num = 51
-min_value = -10
-max_value = 10
-vrange = np.linspace(min_value, max_value, atom_num)
-deltaz = float(max_value - min_value) / (atom_num - 1)
-
-
-# ##############################  C51  ####################################
-class MLP(tl.models.Model):
-
-    def __init__(self, name):
-        super(MLP, self).__init__(name=name)
-        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0], W_init=tf.initializers.GlorotUniform())
-        self.qvalue = tl.layers.Dense(
-            out_dim * atom_num, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.reshape = tl.layers.Reshape((-1, out_dim, atom_num))
-
-    def forward(self, ni):
-        qvalues = self.qvalue(self.h1(ni))
-        return tf.nn.log_softmax(self.reshape(qvalues), 2)
-
-
-class CNN(tl.models.Model):
-
-    def __init__(self, name):
-        super(CNN, self).__init__(name=name)
-        h, w, in_channels = in_dim
-        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
-        self.conv1 = tl.layers.Conv2d(
-            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv2 = tl.layers.Conv2d(
-            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv3 = tl.layers.Conv2d(
-            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.flatten = tl.layers.Flatten(name='flatten')
-        self.preq = tl.layers.Dense(
-            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.qvalue = tl.layers.Dense(
-            out_dim * atom_num, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.reshape = tl.layers.Reshape((-1, out_dim, atom_num))
-
-    def forward(self, ni):
-        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
-        qvalues = self.qvalue(self.preq(feature))
-        return tf.nn.log_softmax(self.reshape(qvalues), 2)
-
-
-class ReplayBuffer(object):
-
-    def __init__(self, size):
-        self._storage = []
-        self._maxsize = size
-        self._next_idx = 0
-
-    def __len__(self):
-        return len(self._storage)
-
-    def add(self, *args):
-        if self._next_idx >= len(self._storage):
-            self._storage.append(args)
-        else:
-            self._storage[self._next_idx] = args
-        self._next_idx = (self._next_idx + 1) % self._maxsize
-
-    def _encode_sample(self, idxes):
-        # encode sample to numpy.array with right dtype
-        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
-        for i in idxes:
-            o, a, r, o_, d = self._storage[i]
-            b_o.append(o)
-            b_a.append(a)
-            b_r.append(r)
-            b_o_.append(o_)
-            b_d.append(d)
-        return (
-            np.stack(b_o).astype('float32') * ob_scale,
-            np.stack(b_a).astype('int32'),
-            np.stack(b_r).astype('float32'),
-            np.stack(b_o_).astype('float32') * ob_scale,
-            np.stack(b_d).astype('float32'),
-        )
-
-    def sample(self, batch_size):
-        indexes = range(len(self._storage))
-        # allow sampling with replacement
-        idxes = [random.choice(indexes) for _ in range(batch_size)]
-        return self._encode_sample(idxes)
-
-
-def sync(net, net_tar):
-    """Copy q network to target q network"""
-    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
-        var_tar.assign(var)
-
-
-if __name__ == '__main__':
-    if args.mode == 'train':
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        qnet.train()
-        trainabel_weights = qnet.trainable_weights
-        targetqnet = MLP('targetq') if qnet_type == 'MLP' else CNN('targetq')
-        targetqnet.infer()
-        sync(qnet, targetqnet)
-        optimizer = tf.optimizers.Adam(learning_rate=lr)
-        buffer = ReplayBuffer(buffer_size)
-
-        o = env.reset()
-        nepisode = 0
-        t = time.time()
-        for i in range(1, number_timesteps + 1):
-            eps = epsilon(i)
-
-            # select action
-            if random.random() < eps:
-                a = int(random.random() * out_dim)
-            else:
-                obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-                qdist = np.exp(qnet(obv).numpy())
-                qvalues = (qdist * vrange).sum(-1)
-                a = qvalues.argmax(1)[0]
-
-            # execute action and feed to replay buffer
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-            buffer.add(o, a, r, o_, done)
-
-            if i >= warm_start:
-                # sync q net and target q net
-                if i % target_q_update_freq == 0:
-                    sync(qnet, targetqnet)
-                    path = os.path.join(args.save_path, '{}.npz'.format(i))
-                    tl.files.save_npz(qnet.trainable_weights, name=path)
-
-                # sample from replay buffer
-                b_o, b_a, b_r, b_o_, b_d = buffer.sample(batch_size)
-
-                # q estimation, see Algorithm 1 in paper for detail
-                b_dist_ = np.exp(targetqnet(b_o_).numpy())
-                b_a_ = (b_dist_ * vrange).sum(-1).argmax(1)
-                b_tzj = np.clip(
-                    reward_gamma * (1 - b_d[:, None]) * vrange[None, :] + b_r[:, None], min_value, max_value
-                )
-                b_i = (b_tzj - min_value) / deltaz
-                b_l = np.floor(b_i).astype('int64')
-                b_u = np.ceil(b_i).astype('int64')
-                templ = b_dist_[range(batch_size), b_a_, :] * (b_u - b_i)
-                tempu = b_dist_[range(batch_size), b_a_, :] * (b_i - b_l)
-                b_m = np.zeros((batch_size, atom_num))
-                # TODO: aggregate value by index and batch update (scatter_add)
-                for j in range(batch_size):
-                    for k in range(atom_num):
-                        b_m[j][b_l[j][k]] += templ[j][k]
-                        b_m[j][b_u[j][k]] += tempu[j][k]
-                b_m = tf.convert_to_tensor(b_m, dtype='float32')
-
-                # calculate loss
-                with tf.GradientTape() as q_tape:
-                    b_index = np.stack([range(batch_size), b_a], 1)
-                    b_index = tf.convert_to_tensor(b_index, 'int64')
-                    b_dist_a = tf.gather_nd(qnet(b_o), b_index)
-                    loss = -tf.reduce_mean(tf.reduce_sum(b_dist_a * b_m, 1))
-
-                # backward gradients
-                q_grad = q_tape.gradient(loss, trainabel_weights)
-                optimizer.apply_gradients(zip(q_grad, trainabel_weights))
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                fps = int(length / (time.time() - t))
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}, FPS: {}'.format(i, nepisode, reward, length, fps)
-                )
-                t = time.time()
-    else:
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        tl.files.load_and_assign_npz(name=args.save_path, network=qnet)
-        qnet.eval()
-
-        nepisode = 0
-        o = env.reset()
-        for i in range(1, number_timesteps + 1):
-            obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-            qdist = np.exp(qnet(obv).numpy())
-            qvalues = (qdist * vrange).sum(-1)
-            a = qvalues.argmax(1)[0]
-
-            # execute action
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}'.format(i, nepisode, reward, length)
-                )
+"""
+C51 Algorithm
+------------------------
+Categorical 51 distributional RL algorithm, 51 means the number of atoms. In
+this algorithm, instead of estimating actual expected value, value distribution
+over a series of continuous sub-intervals (atoms) is considered.
+Reference:
+------------------------
+Bellemare M G, Dabney W, Munos R. A distributional perspective on reinforcement
+learning[C]//Proceedings of the 34th International Conference on Machine
+Learning-Volume 70. JMLR. org, 2017: 449-458.
+Environment:
+------------------------
+Cartpole and Pong in OpenAI Gym
+Requirements:
+------------------------
+tensorflow>=2.0.0a0
+tensorlayer>=2.0.0
+To run:
+------------------------
+python tutorial_C51.py --mode=train
+python tutorial_C51.py --mode=test --save_path=c51/8000.npz
+"""
+import argparse
+import os
+import random
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+import tensorlayer as tl
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--train', dest='train', action='store_true', default=True)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+parser.add_argument(
+    '--save_path', default=None, help='folder to save if mode == train else model path,'
+    'qnet will be saved once target net update'
+)
+parser.add_argument('--seed', help='random seed', type=int, default=0)
+parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
+args = parser.parse_args()
+
+random.seed(args.seed)
+np.random.seed(args.seed)
+tf.random.set_seed(args.seed)  # reproducible
+env_id = args.env_id
+env = gym.make(env_id)
+env.seed(args.seed)
+alg_name = 'C51'
+
+# ####################  hyper parameters  ####################
+if env_id == 'CartPole-v0':
+    qnet_type = 'MLP'
+    number_timesteps = 10000  # total number of time steps to train on
+    explore_timesteps = 100
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 5e-3  # learning rate
+    buffer_size = 1000  # replay buffer size
+    target_q_update_freq = 50  # how frequency target q net update
+    ob_scale = 1.0  # scale observations
+    clipnorm = None
+else:
+    # reward will increase obviously after 1e5 time steps
+    qnet_type = 'CNN'
+    number_timesteps = int(1e6)  # total number of time steps to train on
+    explore_timesteps = 1e5
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 1e-4  # learning rate
+    buffer_size = 10000  # replay buffer size
+    target_q_update_freq = 200  # how frequency target q net update
+    ob_scale = 1.0 / 255  # scale observations
+    clipnorm = 10
+
+in_dim = env.observation_space.shape
+out_dim = env.action_space.n
+reward_gamma = 0.99  # reward discount
+batch_size = 32  # batch size for sampling from replay buffer
+warm_start = buffer_size / 10  # sample times befor learning
+atom_num = 51
+min_value = -10
+max_value = 10
+vrange = np.linspace(min_value, max_value, atom_num)
+deltaz = float(max_value - min_value) / (atom_num - 1)
+
+
+# ##############################  Network  ####################################
+class MLP(tl.models.Model):
+
+    def __init__(self, name):
+        super(MLP, self).__init__(name=name)
+        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0], W_init=tf.initializers.GlorotUniform())
+        self.qvalue = tl.layers.Dense(
+            out_dim * atom_num, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform()
+        )
+        self.reshape = tl.layers.Reshape((-1, out_dim, atom_num))
+
+    def forward(self, ni):
+        qvalues = self.qvalue(self.h1(ni))
+        return tf.nn.log_softmax(self.reshape(qvalues), 2)
+
+
+class CNN(tl.models.Model):
+
+    def __init__(self, name):
+        super(CNN, self).__init__(name=name)
+        h, w, in_channels = in_dim
+        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
+        self.conv1 = tl.layers.Conv2d(
+            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv2 = tl.layers.Conv2d(
+            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv3 = tl.layers.Conv2d(
+            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.flatten = tl.layers.Flatten(name='flatten')
+        self.preq = tl.layers.Dense(
+            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
+        )
+        self.qvalue = tl.layers.Dense(
+            out_dim * atom_num, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform()
+        )
+        self.reshape = tl.layers.Reshape((-1, out_dim, atom_num))
+
+    def forward(self, ni):
+        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
+        qvalues = self.qvalue(self.preq(feature))
+        return tf.nn.log_softmax(self.reshape(qvalues), 2)
+
+
+# ##############################  Replay  ####################################
+class ReplayBuffer(object):
+
+    def __init__(self, size):
+        self._storage = []
+        self._maxsize = size
+        self._next_idx = 0
+
+    def __len__(self):
+        return len(self._storage)
+
+    def add(self, *args):
+        if self._next_idx >= len(self._storage):
+            self._storage.append(args)
+        else:
+            self._storage[self._next_idx] = args
+        self._next_idx = (self._next_idx + 1) % self._maxsize
+
+    def _encode_sample(self, idxes):
+        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
+        for i in idxes:
+            o, a, r, o_, d = self._storage[i]
+            b_o.append(o)
+            b_a.append(a)
+            b_r.append(r)
+            b_o_.append(o_)
+            b_d.append(d)
+        return (
+            np.stack(b_o).astype('float32') * ob_scale,
+            np.stack(b_a).astype('int32'),
+            np.stack(b_r).astype('float32'),
+            np.stack(b_o_).astype('float32') * ob_scale,
+            np.stack(b_d).astype('float32'),
+        )
+
+    def sample(self, batch_size):
+        indexes = range(len(self._storage))
+        idxes = [random.choice(indexes) for _ in range(batch_size)]
+        return self._encode_sample(idxes)
+
+
+# #############################  Functions  ###################################
+def huber_loss(x):
+    """Loss function for value"""
+    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
+
+
+def sync(net, net_tar):
+    """Copy q network to target q network"""
+    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
+        var_tar.assign(var)
+
+
+# ###############################  DQN  #####################################
+class DQN(object):
+
+    def __init__(self):
+        model = MLP if qnet_type == 'MLP' else CNN
+        self.qnet = model('q')
+        if args.train:
+            self.qnet.train()
+            self.targetqnet = model('targetq')
+            self.targetqnet.infer()
+            sync(self.qnet, self.targetqnet)
+        else:
+            self.qnet.infer()
+            self.load(args.save_path)
+        self.niter = 0
+        if clipnorm is not None:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr, clipnorm=clipnorm)
+        else:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr)
+
+    def get_action(self, obv):
+        eps = epsilon(self.niter)
+        if args.train and random.random() < eps:
+            return int(random.random() * out_dim)
+        else:
+            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
+            qdist = np.exp(self._qvalues_func(obv).numpy())
+            qvalues = (qdist * vrange).sum(-1)
+            return qvalues.argmax(1)[0]
+
+    @tf.function
+    def _qvalues_func(self, obv):
+        return self.qnet(obv)
+
+    def train(self, b_o, b_a, b_r, b_o_, b_d):
+        # TODO: move q_estimation in tf.function
+        b_dist_ = np.exp(self.targetqnet(b_o_).numpy())
+        b_a_ = (b_dist_ * vrange).sum(-1).argmax(1)
+        b_tzj = np.clip(reward_gamma * (1 - b_d[:, None]) * vrange[None, :] + b_r[:, None], min_value, max_value)
+        b_i = (b_tzj - min_value) / deltaz
+        b_l = np.floor(b_i).astype('int64')
+        b_u = np.ceil(b_i).astype('int64')
+        templ = b_dist_[range(batch_size), b_a_, :] * (b_u - b_i)
+        tempu = b_dist_[range(batch_size), b_a_, :] * (b_i - b_l)
+        b_m = np.zeros((batch_size, atom_num))
+        # TODO: aggregate value by index and batch update (scatter_add)
+        for j in range(batch_size):
+            for k in range(atom_num):
+                b_m[j][b_l[j][k]] += templ[j][k]
+                b_m[j][b_u[j][k]] += tempu[j][k]
+        b_m = tf.convert_to_tensor(b_m, dtype='float32')
+        b_index = np.stack([range(batch_size), b_a], 1)
+        b_index = tf.convert_to_tensor(b_index, 'int64')
+
+        self._train_func(b_o, b_index, b_m)
+
+        self.niter += 1
+        if self.niter % target_q_update_freq == 0:
+            sync(self.qnet, self.targetqnet)
+            self.save(args.save_path)
+
+    def save(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+    def load(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+    @tf.function
+    def _train_func(self, b_o, b_index, b_m):
+        with tf.GradientTape() as tape:
+            b_dist_a = tf.gather_nd(self.qnet(b_o), b_index)
+            loss = tf.reduce_mean(tf.negative(tf.reduce_sum(b_dist_a * b_m, 1)))
+
+        grad = tape.gradient(loss, self.qnet.trainable_weights)
+        self.optimizer.apply_gradients(zip(grad, self.qnet.trainable_weights))
+
+
+# #############################  Trainer  ###################################
+if __name__ == '__main__':
+    dqn = DQN()
+    t0 = time.time()
+    if args.train:
+        buffer = ReplayBuffer(buffer_size)
+        nepisode = 0
+        all_episode_reward = []
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                a = dqn.get_action(o)
+                # execute action and feed to replay buffer
+                # note that `_` tail in var name means next
+                o_, r, done, info = env.step(a)
+                buffer.add(o, a, r, o_, done)
+                episode_reward += r
+
+                if i >= warm_start:
+                    transitions = buffer.sample(batch_size)
+                    dqn.train(*transitions)
+
+                if done:
+                    break
+                else:
+                    o = o_
+
+            if nepisode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            nepisode += 1
+            print(
+                'Training  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )  # episode num starts from 1 in print
+
+        dqn.save(args.save_path)
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
+
+    if args.test:
+        nepisode = 0
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                env.render()
+                a = dqn.get_action(o)
+                o_, r, done, info = env.step(a)
+                episode_reward += r
+                if done:
+                    break
+                else:
+                    o = o_
+            nepisode += 1
+            print(
+                'Testing  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_DDPG.py b/examples/reinforcement_learning/tutorial_DDPG.py
index a0079a014..c006a7bf4 100644
--- a/examples/reinforcement_learning/tutorial_DDPG.py
+++ b/examples/reinforcement_learning/tutorial_DDPG.py
@@ -18,7 +18,7 @@
 Prerequisites
 -------------
 tensorflow >=2.0.0a0
-tensorflow-probability 0.6.0
+tensorflow-proactionsbility 0.6.0
 tensorlayer >=2.0.0
 
 To run
@@ -31,34 +31,37 @@
 import os
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
-
-import gym
 import tensorflow as tf
+
 import tensorlayer as tl
 
+# add arguments in command  --train/test
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=True)
-parser.add_argument('--test', dest='train', action='store_false')
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
 args = parser.parse_args()
 
 #####################  hyper parameters  ####################
 
-ENV_NAME = 'Pendulum-v0'  # environment name
-RANDOMSEED = 1  # random seed
+ENV_ID = 'Pendulum-v0'  # environment id
+RANDOM_SEED = 2  # random seed, can be either an int number or None
+RENDER = False  # render while training
+
+ALG_NAME = 'DDPG'
+TRAIN_EPISODES = 100  # total number of episodes for training
+TEST_EPISODES = 10  # total number of episodes for training
+MAX_STEPS = 200  # total number of steps for each episode
 
 LR_A = 0.001  # learning rate for actor
 LR_C = 0.002  # learning rate for critic
 GAMMA = 0.9  # reward discount
 TAU = 0.01  # soft replacement
 MEMORY_CAPACITY = 10000  # size of replay buffer
-BATCH_SIZE = 32  # update batchsize
-
-MAX_EPISODES = 200  # total number of episodes for training
-MAX_EP_STEPS = 200  # total number of steps for each episode
-TEST_PER_EPISODES = 10  # test the model per episodes
-VAR = 3  # control exploration
+BATCH_SIZE = 32  # update action batch size
+VAR = 2  # control exploration
 
 ###############################  DDPG  ####################################
 
@@ -68,10 +71,11 @@ class DDPG(object):
     DDPG class
     """
 
-    def __init__(self, a_dim, s_dim, a_bound):
-        self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32)
+    def __init__(self, action_dim, state_dim, action_range):
+        self.memory = np.zeros((MEMORY_CAPACITY, state_dim * 2 + action_dim + 1), dtype=np.float32)
         self.pointer = 0
-        self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound
+        self.action_dim, self.state_dim, self.action_range = action_dim, state_dim, action_range
+        self.var = VAR
 
         W_init = tf.random_normal_initializer(mean=0, stddev=0.3)
         b_init = tf.constant_initializer(0.1)
@@ -83,11 +87,12 @@ def get_actor(input_state_shape, name=''):
             :param name: name
             :return: act
             """
-            inputs = tl.layers.Input(input_state_shape, name='A_input')
-            x = tl.layers.Dense(n_units=30, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='A_l1')(inputs)
-            x = tl.layers.Dense(n_units=a_dim, act=tf.nn.tanh, W_init=W_init, b_init=b_init, name='A_a')(x)
-            x = tl.layers.Lambda(lambda x: np.array(a_bound) * x)(x)
-            return tl.models.Model(inputs=inputs, outputs=x, name='Actor' + name)
+            input_layer = tl.layers.Input(input_state_shape, name='A_input')
+            layer = tl.layers.Dense(n_units=64, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='A_l1')(input_layer)
+            layer = tl.layers.Dense(n_units=64, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='A_l2')(layer)
+            layer = tl.layers.Dense(n_units=action_dim, act=tf.nn.tanh, W_init=W_init, b_init=b_init, name='A_a')(layer)
+            layer = tl.layers.Lambda(lambda x: action_range * x)(layer)
+            return tl.models.Model(inputs=input_layer, outputs=layer, name='Actor' + name)
 
         def get_critic(input_state_shape, input_action_shape, name=''):
             """
@@ -97,15 +102,16 @@ def get_critic(input_state_shape, input_action_shape, name=''):
             :param name: name
             :return: Q value Q(s,a)
             """
-            s = tl.layers.Input(input_state_shape, name='C_s_input')
-            a = tl.layers.Input(input_action_shape, name='C_a_input')
-            x = tl.layers.Concat(1)([s, a])
-            x = tl.layers.Dense(n_units=60, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='C_l1')(x)
-            x = tl.layers.Dense(n_units=1, W_init=W_init, b_init=b_init, name='C_out')(x)
-            return tl.models.Model(inputs=[s, a], outputs=x, name='Critic' + name)
-
-        self.actor = get_actor([None, s_dim])
-        self.critic = get_critic([None, s_dim], [None, a_dim])
+            state_input = tl.layers.Input(input_state_shape, name='C_s_input')
+            action_input = tl.layers.Input(input_action_shape, name='C_a_input')
+            layer = tl.layers.Concat(1)([state_input, action_input])
+            layer = tl.layers.Dense(n_units=64, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='C_l1')(layer)
+            layer = tl.layers.Dense(n_units=64, act=tf.nn.relu, W_init=W_init, b_init=b_init, name='C_l2')(layer)
+            layer = tl.layers.Dense(n_units=1, W_init=W_init, b_init=b_init, name='C_out')(layer)
+            return tl.models.Model(inputs=[state_input, action_input], outputs=layer, name='Critic' + name)
+
+        self.actor = get_actor([None, state_dim])
+        self.critic = get_critic([None, state_dim], [None, action_dim])
         self.actor.train()
         self.critic.train()
 
@@ -119,16 +125,14 @@ def copy_para(from_model, to_model):
             for i, j in zip(from_model.trainable_weights, to_model.trainable_weights):
                 j.assign(i)
 
-        self.actor_target = get_actor([None, s_dim], name='_target')
+        self.actor_target = get_actor([None, state_dim], name='_target')
         copy_para(self.actor, self.actor_target)
         self.actor_target.eval()
 
-        self.critic_target = get_critic([None, s_dim], [None, a_dim], name='_target')
+        self.critic_target = get_critic([None, state_dim], [None, action_dim], name='_target')
         copy_para(self.critic, self.critic_target)
         self.critic_target.eval()
 
-        self.R = tl.layers.Input([None, 1], tf.float32, 'r')
-
         self.ema = tf.train.ExponentialMovingAverage(decay=1 - TAU)  # soft replacement
 
         self.actor_opt = tf.optimizers.Adam(LR_A)
@@ -144,42 +148,48 @@ def ema_update(self):
         for i, j in zip(self.actor_target.trainable_weights + self.critic_target.trainable_weights, paras):
             i.assign(self.ema.average(j))
 
-    def choose_action(self, s):
+    def get_action(self, s, greedy=False):
         """
         Choose action
         :param s: state
+        :param greedy: get action greedy or not
         :return: act
         """
-        return self.actor(np.array([s], dtype=np.float32))[0]
+        a = self.actor(np.array([s], dtype=np.float32))[0]
+        if greedy:
+            return a
+        return np.clip(
+            np.random.normal(a, self.var), -self.action_range, self.action_range
+        )  # add randomness to action selection for exploration
 
     def learn(self):
         """
         Update parameters
         :return: None
         """
+        self.var *= .9995
         indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)
-        bt = self.memory[indices, :]
-        bs = bt[:, :self.s_dim]
-        ba = bt[:, self.s_dim:self.s_dim + self.a_dim]
-        br = bt[:, -self.s_dim - 1:-self.s_dim]
-        bs_ = bt[:, -self.s_dim:]
+        datas = self.memory[indices, :]
+        states = datas[:, :self.state_dim]
+        actions = datas[:, self.state_dim:self.state_dim + self.action_dim]
+        rewards = datas[:, -self.state_dim - 1:-self.state_dim]
+        states_ = datas[:, -self.state_dim:]
 
         with tf.GradientTape() as tape:
-            a_ = self.actor_target(bs_)
-            q_ = self.critic_target([bs_, a_])
-            y = br + GAMMA * q_
-            q = self.critic([bs, ba])
+            actions_ = self.actor_target(states_)
+            q_ = self.critic_target([states_, actions_])
+            y = rewards + GAMMA * q_
+            q = self.critic([states, actions])
             td_error = tf.losses.mean_squared_error(y, q)
-        c_grads = tape.gradient(td_error, self.critic.trainable_weights)
-        self.critic_opt.apply_gradients(zip(c_grads, self.critic.trainable_weights))
+        critic_grads = tape.gradient(td_error, self.critic.trainable_weights)
+        self.critic_opt.apply_gradients(zip(critic_grads, self.critic.trainable_weights))
 
         with tf.GradientTape() as tape:
-            a = self.actor(bs)
-            q = self.critic([bs, a])
-            a_loss = - tf.reduce_mean(q)  # maximize the q
-        a_grads = tape.gradient(a_loss, self.actor.trainable_weights)
-        self.actor_opt.apply_gradients(zip(a_grads, self.actor.trainable_weights))
-
+            a = self.actor(states)
+            q = self.critic([states, a])
+            actor_loss = -tf.reduce_mean(q)  # maximize the q
+        actor_grads = tape.gradient(actor_loss, self.actor.trainable_weights)
+        self.actor_opt.apply_gradients(zip(actor_grads, self.actor.trainable_weights))
         self.ema_update()
 
     def store_transition(self, s, a, r, s_):
@@ -198,118 +208,98 @@ def store_transition(self, s, a, r, s_):
         self.memory[index, :] = transition
         self.pointer += 1
 
-    def save_ckpt(self):
+    def save(self):
         """
         save trained weights
         :return: None
         """
-        if not os.path.exists('model'):
-            os.makedirs('model')
-
-        tl.files.save_weights_to_hdf5('model/ddpg_actor.hdf5', self.actor)
-        tl.files.save_weights_to_hdf5('model/ddpg_actor_target.hdf5', self.actor_target)
-        tl.files.save_weights_to_hdf5('model/ddpg_critic.hdf5', self.critic)
-        tl.files.save_weights_to_hdf5('model/ddpg_critic_target.hdf5', self.critic_target)
-
-    def load_ckpt(self):
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'actor_target.hdf5'), self.actor_target)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'critic.hdf5'), self.critic)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'critic_target.hdf5'), self.critic_target)
+
+    def load(self):
         """
         load trained weights
         :return: None
         """
-        tl.files.load_hdf5_to_weights_in_order('model/ddpg_actor.hdf5', self.actor)
-        tl.files.load_hdf5_to_weights_in_order('model/ddpg_actor_target.hdf5', self.actor_target)
-        tl.files.load_hdf5_to_weights_in_order('model/ddpg_critic.hdf5', self.critic)
-        tl.files.load_hdf5_to_weights_in_order('model/ddpg_critic_target.hdf5', self.critic_target)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'actor_target.hdf5'), self.actor_target)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'critic.hdf5'), self.critic)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'critic_target.hdf5'), self.critic_target)
 
 
 if __name__ == '__main__':
-
-    env = gym.make(ENV_NAME)
-    env = env.unwrapped
+    env = gym.make(ENV_ID).unwrapped
 
     # reproducible
-    env.seed(RANDOMSEED)
-    np.random.seed(RANDOMSEED)
-    tf.random.set_seed(RANDOMSEED)
+    env.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
 
-    s_dim = env.observation_space.shape[0]
-    a_dim = env.action_space.shape[0]
-    a_bound = env.action_space.high
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_range = env.action_space.high  # scale action, [-action_range, action_range]
 
-    ddpg = DDPG(a_dim, s_dim, a_bound)
+    agent = DDPG(action_dim, state_dim, action_range)
 
+    t0 = time.time()
     if args.train:  # train
-
-        reward_buffer = []
-        t0 = time.time()
-        for i in range(MAX_EPISODES):
-            t1 = time.time()
-            s = env.reset()
-            ep_reward = 0
-            for j in range(MAX_EP_STEPS):
+        all_episode_reward = []
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                if RENDER:
+                    env.render()
                 # Add exploration noise
-                a = ddpg.choose_action(s)
-                a = np.clip(np.random.normal(a, VAR), -2, 2)  # add randomness to action selection for exploration
-                s_, r, done, info = env.step(a)
-
-                ddpg.store_transition(s, a, r / 10, s_)
-
-                if ddpg.pointer > MEMORY_CAPACITY:
-                    ddpg.learn()
-
-                s = s_
-                ep_reward += r
-                if j == MAX_EP_STEPS - 1:
-                    print(
-                        '\rEpisode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                            i, MAX_EPISODES, ep_reward,
-                            time.time() - t1
-                        ), end=''
-                    )
-                plt.show()
-            # test
-            if i and not i % TEST_PER_EPISODES:
-                t1 = time.time()
-                s = env.reset()
-                ep_reward = 0
-                for j in range(MAX_EP_STEPS):
-
-                    a = ddpg.choose_action(s)  # without exploration noise
-                    s_, r, done, info = env.step(a)
-
-                    s = s_
-                    ep_reward += r
-                    if j == MAX_EP_STEPS - 1:
-                        print(
-                            '\rEpisode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                                i, MAX_EPISODES, ep_reward,
-                                time.time() - t1
-                            )
-                        )
-
-                        reward_buffer.append(ep_reward)
-
-            if reward_buffer:
-                plt.ion()
-                plt.cla()
-                plt.title('DDPG')
-                plt.plot(np.array(range(len(reward_buffer))) * TEST_PER_EPISODES, reward_buffer)  # plot the episode vt
-                plt.xlabel('episode steps')
-                plt.ylabel('normalized state-action value')
-                plt.ylim(-2000, 0)
-                plt.show()
-                plt.pause(0.1)
-        plt.ioff()
-        plt.show()
-        print('\nRunning time: ', time.time() - t0)
-        ddpg.save_ckpt()
-
-    # test
-    ddpg.load_ckpt()
-    while True:
-        s = env.reset()
-        for i in range(MAX_EP_STEPS):
-            env.render()
-            s, r, done, info = env.step(ddpg.choose_action(s))
-            if done:
-                break
+                action = agent.get_action(state)
+                state_, reward, done, info = env.step(action)
+                agent.store_transition(state, action, reward, state_)
+
+                if agent.pointer > MEMORY_CAPACITY:
+                    agent.learn()
+
+                state = state_
+                episode_reward += reward
+                if done:
+                    break
+
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            print(
+                'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
+        agent.save()
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
+
+    if args.test:
+        # test
+        agent.load()
+        for episode in range(TEST_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                env.render()
+                state, reward, done, info = env.step(agent.get_action(state, greedy=True))
+                episode_reward += reward
+                if done:
+                    break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_DPPO.py b/examples/reinforcement_learning/tutorial_DPPO.py
index abe4be035..dbfd78db5 100644
--- a/examples/reinforcement_learning/tutorial_DPPO.py
+++ b/examples/reinforcement_learning/tutorial_DPPO.py
@@ -25,8 +25,6 @@
 To run
 ------
 python tutorial_DPPO.py --train/test
-
-
 """
 
 import argparse
@@ -35,303 +33,284 @@
 import threading
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
-
-import gym
 import tensorflow as tf
 import tensorflow_probability as tfp
+
 import tensorlayer as tl
 
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=True)
-parser.add_argument('--test', dest='train', action='store_false')
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
 args = parser.parse_args()
 
 #####################  hyper parameters  ####################
 
-GAME = 'Pendulum-v0'  # environment name
-RANDOMSEED = 1  # random seed
+ENV_ID = 'Pendulum-v0'  # environment name
+RANDOMSEED = 2  # random seed
+RENDER = False  # render while training
 
-EP_MAX = 1000  # total number of episodes for training
-EP_LEN = 200  # total number of steps for each episode
+ALG_NAME = 'DPPO'
+TRAIN_EPISODES = 1000  # total number of episodes for training
+TEST_EPISODES = 10  # number of overall episodes for testing
+MAX_STEPS = 200  # total number of steps for each episode
 GAMMA = 0.9  # reward discount
-A_LR = 0.0001  # learning rate for actor
-C_LR = 0.0002  # learning rate for critic
-BATCH = 32  # update batchsize
-A_UPDATE_STEPS = 10  # actor update steps
-C_UPDATE_STEPS = 10  # critic update steps
-S_DIM, A_DIM = 3, 1  # state dimension, action dimension
-EPS = 1e-8  # epsilon
-METHOD = [
-    dict(name='kl_pen', kl_target=0.01, lam=0.5),  # KL penalty
-    dict(name='clip', epsilon=0.2),  # Clipped surrogate objective, find this is better
-][1]  # choose the method for optimization
+LR_A = 0.0001  # learning rate for actor
+LR_C = 0.0002  # learning rate for critic
+ACTOR_UPDATE_STEPS = 10  # actor update steps
+CRITIC_UPDATE_STEPS = 10  # critic update steps
+MIN_BATCH_SIZE = 64  # minimum batch size for updating PPO
 
 N_WORKER = 4  # parallel workers
-MIN_BATCH_SIZE = 64  # minimum batch size for updating PPO
 UPDATE_STEP = 10  # loop update operation n-steps
 
+# ppo-penalty parameters
+KL_TARGET = 0.01
+LAM = 0.5
+
+# ppo-clip parameters
+EPSILON = 0.2
+
+
 ###############################  DPPO  ####################################
 
 
 class PPO(object):
-    '''
+    """
     PPO class
-    '''
+    """
 
-    def __init__(self):
+    def __init__(self, state_dim, action_dim, action_bound, method='clip'):
 
         # critic
-        tfs = tl.layers.Input([None, S_DIM], tf.float32, 'state')
-        l1 = tl.layers.Dense(100, tf.nn.relu)(tfs)
-        v = tl.layers.Dense(1)(l1)
-        self.critic = tl.models.Model(tfs, v)
+        with tf.name_scope('critic'):
+            inputs = tl.layers.Input([None, state_dim], tf.float32, 'state')
+            layer = tl.layers.Dense(64, tf.nn.relu)(inputs)
+            layer = tl.layers.Dense(64, tf.nn.relu)(layer)
+            v = tl.layers.Dense(1)(layer)
+        self.critic = tl.models.Model(inputs, v)
         self.critic.train()
+        self.method = method
 
         # actor
-        self.actor = self._build_anet('pi', trainable=True)
-        self.actor_old = self._build_anet('oldpi', trainable=False)
-        self.actor_opt = tf.optimizers.Adam(A_LR)
-        self.critic_opt = tf.optimizers.Adam(C_LR)
-
-    def a_train(self, tfs, tfa, tfadv):
-        '''
+        with tf.name_scope('actor'):
+            inputs = tl.layers.Input([None, state_dim], tf.float32, 'state')
+            layer = tl.layers.Dense(64, tf.nn.relu)(inputs)
+            layer = tl.layers.Dense(64, tf.nn.relu)(layer)
+            a = tl.layers.Dense(action_dim, tf.nn.tanh)(layer)
+            mean = tl.layers.Lambda(lambda x: x * action_bound, name='lambda')(a)
+            logstd = tf.Variable(np.zeros(action_dim, dtype=np.float32))
+        self.actor = tl.models.Model(inputs, mean)
+        self.actor.trainable_weights.append(logstd)
+        self.actor.logstd = logstd
+        self.actor.train()
+
+        self.actor_opt = tf.optimizers.Adam(LR_A)
+        self.critic_opt = tf.optimizers.Adam(LR_C)
+
+        self.method = method
+        if method == 'penalty':
+            self.kl_target = KL_TARGET
+            self.lam = LAM
+        elif method == 'clip':
+            self.epsilon = EPSILON
+
+        self.state_buffer, self.action_buffer = [], []
+        self.reward_buffer, self.cumulative_reward_buffer = [], []
+        self.action_bound = action_bound
+
+    def train_actor(self, state, action, adv, old_pi):
+        """
         Update policy network
-        :param tfs: state
-        :param tfa: act
-        :param tfadv: advantage
-        :return:
-        '''
-        tfs = np.array(tfs, np.float32)
-        tfa = np.array(tfa, np.float32)
-        tfadv = np.array(tfadv, np.float32)
+        :param state: state batch
+        :param action: action batch
+        :param adv: advantage batch
+        :param old_pi: old pi distribution
+        :return: kl_mean or None
+        """
         with tf.GradientTape() as tape:
-            mu, sigma = self.actor(tfs)
-            pi = tfp.distributions.Normal(mu, sigma)
-
-            mu_old, sigma_old = self.actor_old(tfs)
-            oldpi = tfp.distributions.Normal(mu_old, sigma_old)
-
-            # ratio = tf.exp(pi.log_prob(self.tfa) - oldpi.log_prob(self.tfa))
-            ratio = pi.prob(tfa) / (oldpi.prob(tfa) + EPS)
-            surr = ratio * tfadv
-            if METHOD['name'] == 'kl_pen':
-                tflam = METHOD['lam']
-                kl = tfp.distributions.kl_divergence(oldpi, pi)
+            mean, std = self.actor(state), tf.exp(self.actor.logstd)
+            pi = tfp.distributions.Normal(mean, std)
+
+            ratio = tf.exp(pi.log_prob(action) - old_pi.log_prob(action))
+            surr = ratio * adv
+            if self.method == 'penalty':  # ppo penalty
+                kl = tfp.distributions.kl_divergence(old_pi, pi)
                 kl_mean = tf.reduce_mean(kl)
-                aloss = -(tf.reduce_mean(surr - tflam * kl))
-            else:  # clipping method, find this is better
-                aloss = -tf.reduce_mean(
+                loss = -(tf.reduce_mean(surr - self.lam * kl))
+            else:  # ppo clip
+                loss = -tf.reduce_mean(
                     tf.minimum(surr,
-                               tf.clip_by_value(ratio, 1. - METHOD['epsilon'], 1. + METHOD['epsilon']) * tfadv)
+                               tf.clip_by_value(ratio, 1. - self.epsilon, 1. + self.epsilon) * adv)
                 )
-        a_gard = tape.gradient(aloss, self.actor.trainable_weights)
-
+        a_gard = tape.gradient(loss, self.actor.trainable_weights)
         self.actor_opt.apply_gradients(zip(a_gard, self.actor.trainable_weights))
 
-        if METHOD['name'] == 'kl_pen':
+        if self.method == 'kl_pen':
             return kl_mean
 
-    def update_old_pi(self):
-        '''
-        Update old policy parameter
-        :return: None
-        '''
-        for p, oldp in zip(self.actor.trainable_weights, self.actor_old.trainable_weights):
-            oldp.assign(p)
-
-    def c_train(self, tfdc_r, s):
-        '''
+    def train_critic(self, reward, state):
+        """
         Update actor network
-        :param tfdc_r: cumulative reward
-        :param s: state
+        :param reward: cumulative reward batch
+        :param state: state batch
         :return: None
-        '''
-        tfdc_r = np.array(tfdc_r, dtype=np.float32)
+        """
+        reward = np.array(reward, dtype=np.float32)
         with tf.GradientTape() as tape:
-            advantage = tfdc_r - self.critic(s)
-            closs = tf.reduce_mean(tf.square(advantage))
-        grad = tape.gradient(closs, self.critic.trainable_weights)
+            advantage = reward - self.critic(state)
+            loss = tf.reduce_mean(tf.square(advantage))
+        grad = tape.gradient(loss, self.critic.trainable_weights)
         self.critic_opt.apply_gradients(zip(grad, self.critic.trainable_weights))
 
-    def cal_adv(self, tfs, tfdc_r):
-        '''
-        Calculate advantage
-        :param tfs: state
-        :param tfdc_r: cumulative reward
-        :return: advantage
-        '''
-        tfdc_r = np.array(tfdc_r, dtype=np.float32)
-        advantage = tfdc_r - self.critic(tfs)
-        return advantage.numpy()
-
     def update(self):
-        '''
+        """
         Update parameter with the constraint of KL divergent
         :return: None
-        '''
+        """
         global GLOBAL_UPDATE_COUNTER
         while not COORD.should_stop():
-            if GLOBAL_EP < EP_MAX:
+            if GLOBAL_EP < TRAIN_EPISODES:
                 UPDATE_EVENT.wait()  # wait until get batch of data
-                self.update_old_pi()  # copy pi to old pi
-                data = [QUEUE.get() for _ in range(QUEUE.qsize())]  # collect data from all workers
-                data = np.vstack(data)
-
-                s, a, r = data[:, :S_DIM].astype(np.float32), \
-                          data[:, S_DIM: S_DIM + A_DIM].astype(np.float32), \
-                          data[:, -1:].astype(np.float32)
 
-                adv = self.cal_adv(s, r)
+                data = [QUEUE.get() for _ in range(QUEUE.qsize())]  # collect data from all workers
+                s, a, r = zip(*data)
+                s = np.vstack(s).astype(np.float32)
+                a = np.vstack(a).astype(np.float32)
+                r = np.vstack(r).astype(np.float32)
+                mean, std = self.actor(s), tf.exp(self.actor.logstd)
+                pi = tfp.distributions.Normal(mean, std)
+                adv = r - self.critic(s)
                 # adv = (adv - adv.mean())/(adv.std()+1e-6)     # sometimes helpful
 
                 # update actor
-                if METHOD['name'] == 'kl_pen':
-                    for _ in range(A_UPDATE_STEPS):
-                        kl = self.a_train(s, a, adv)
-                        if kl > 4 * METHOD['kl_target']:  # this in in google's paper
-                            break
-                    if kl < METHOD['kl_target'] / 1.5:  # adaptive lambda, this is in OpenAI's paper
-                        METHOD['lam'] /= 2
-                    elif kl > METHOD['kl_target'] * 1.5:
-                        METHOD['lam'] *= 2
-
-                    # sometimes explode, this clipping is MorvanZhou's solution
-                    METHOD['lam'] = np.clip(METHOD['lam'], 1e-4, 10)
-
-                else:  # clipping method, find this is better (OpenAI's paper)
-                    for _ in range(A_UPDATE_STEPS):
-                        self.a_train(s, a, adv)
+                if self.method == 'kl_pen':
+                    for _ in range(ACTOR_UPDATE_STEPS):
+                        kl = self.train_actor(s, a, adv, pi)
+                    if kl < self.kl_target / 1.5:
+                        self.lam /= 2
+                    elif kl > self.kl_target * 1.5:
+                        self.lam *= 2
+                else:
+                    for _ in range(ACTOR_UPDATE_STEPS):
+                        self.train_actor(s, a, adv, pi)
 
                 # update critic
-                for _ in range(C_UPDATE_STEPS):
-                    self.c_train(r, s)
+                for _ in range(CRITIC_UPDATE_STEPS):
+                    self.train_critic(r, s)
 
                 UPDATE_EVENT.clear()  # updating finished
                 GLOBAL_UPDATE_COUNTER = 0  # reset counter
                 ROLLING_EVENT.set()  # set roll-out available
 
-    def _build_anet(self, name, trainable):
-        '''
-        Build policy network
-        :param name: name
-        :param trainable: trainable flag
-        :return: policy network
-        '''
-        tfs = tl.layers.Input([None, S_DIM], tf.float32, name + '_state')
-        l1 = tl.layers.Dense(100, tf.nn.relu, name=name + '_l1')(tfs)
-        a = tl.layers.Dense(A_DIM, tf.nn.tanh, name=name + '_a')(l1)
-        mu = tl.layers.Lambda(lambda x: x * 2, name=name + '_lambda')(a)
-        sigma = tl.layers.Dense(A_DIM, tf.nn.softplus, name=name + '_sigma')(l1)
-        model = tl.models.Model(tfs, [mu, sigma], name)
-
-        if trainable:
-            model.train()
+    def get_action(self, state, greedy=False):
+        """
+        Choose action
+        :param state: state
+        :param greedy: choose action greedy or not
+        :return: clipped action
+        """
+        state = state[np.newaxis, :].astype(np.float32)
+        mean, std = self.actor(state), tf.exp(self.actor.logstd)
+        if greedy:
+            action = mean[0]
         else:
-            model.eval()
-        return model
+            pi = tfp.distributions.Normal(mean, std)
+            action = tf.squeeze(pi.sample(1), axis=0)[0]  # choosing action
+        return np.clip(action, -self.action_bound, self.action_bound)
 
-    def choose_action(self, s):
-        '''
-        Choose action
-        :param s: state
-        :return: clipped act
-        '''
-        s = s[np.newaxis, :].astype(np.float32)
-        mu, sigma = self.actor(s)
-        pi = tfp.distributions.Normal(mu, sigma)
-        a = tf.squeeze(pi.sample(1), axis=0)[0]  # choosing action
-        return np.clip(a, -2, 2)
-
-    def get_v(self, s):
-        '''
-        Compute value
-        :param s: state
-        :return: value
-        '''
-        s = s.astype(np.float32)
-        if s.ndim < 2: s = s[np.newaxis, :]
-        return self.critic(s)[0, 0]
-
-    def save_ckpt(self):
+    def save(self):
         """
         save trained weights
         :return: None
         """
-        if not os.path.exists('model'):
-            os.makedirs('model')
-        tl.files.save_weights_to_hdf5('model/dppo_actor.hdf5', self.actor)
-        tl.files.save_weights_to_hdf5('model/dppo_actor_old.hdf5', self.actor_old)
-        tl.files.save_weights_to_hdf5('model/dppo_critic.hdf5', self.critic)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'critic.hdf5'), self.critic)
 
-    def load_ckpt(self):
+    def load(self):
         """
         load trained weights
         :return: None
         """
-        tl.files.load_hdf5_to_weights_in_order('model/dppo_actor.hdf5', self.actor)
-        tl.files.load_hdf5_to_weights_in_order('model/dppo_actor_old.hdf5', self.actor_old)
-        tl.files.load_hdf5_to_weights_in_order('model/dppo_critic.hdf5', self.critic)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'critic.hdf5'), self.critic)
 
 
-'''--------------------------------------------------------------'''
+"""--------------------------------------------------------------"""
 
 
 class Worker(object):
-    '''
+    """
     Worker class for distributional running
-    '''
+    """
 
     def __init__(self, wid):
         self.wid = wid
-        self.env = gym.make(GAME).unwrapped
+        self.env = gym.make(ENV_ID).unwrapped
         self.env.seed(wid * 100 + RANDOMSEED)
         self.ppo = GLOBAL_PPO
 
     def work(self):
-        '''
+        """
         Define a worker
         :return: None
-        '''
+        """
         global GLOBAL_EP, GLOBAL_RUNNING_R, GLOBAL_UPDATE_COUNTER
         while not COORD.should_stop():
             s = self.env.reset()
             ep_r = 0
             buffer_s, buffer_a, buffer_r = [], [], []
-            t0 = time.time()
-            for t in range(EP_LEN):
+            for t in range(MAX_STEPS):
                 if not ROLLING_EVENT.is_set():  # while global PPO is updating
                     ROLLING_EVENT.wait()  # wait until PPO is updated
                     buffer_s, buffer_a, buffer_r = [], [], []  # clear history buffer, use new policy to collect data
-                a = self.ppo.choose_action(s)
+                a = self.ppo.get_action(s)
                 s_, r, done, _ = self.env.step(a)
+                if RENDER and self.wid == 0:
+                    self.env.render()
                 buffer_s.append(s)
                 buffer_a.append(a)
-                buffer_r.append((r + 8) / 8)  # normalize reward, find to be useful
+                buffer_r.append(r)
                 s = s_
                 ep_r += r
 
                 GLOBAL_UPDATE_COUNTER += 1  # count to minimum batch size, no need to wait other workers
-                if t == EP_LEN - 1 or GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
-                    v_s_ = self.ppo.get_v(s_)
+                if t == MAX_STEPS - 1 or GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
+                    # finish patyh
+                    if done:
+                        v_s_ = 0
+                    else:
+                        v_s_ = self.ppo.critic(np.array([s_], np.float32))[0][0]
                     discounted_r = []  # compute discounted reward
                     for r in buffer_r[::-1]:
                         v_s_ = r + GAMMA * v_s_
                         discounted_r.append(v_s_)
                     discounted_r.reverse()
-
-                    bs, ba, br = np.vstack(buffer_s), np.vstack(buffer_a), np.array(discounted_r)[:, np.newaxis]
+                    buffer_r = np.array(discounted_r)[:, np.newaxis]
+                    QUEUE.put([buffer_s, buffer_a, buffer_r])  # put data in the queue
                     buffer_s, buffer_a, buffer_r = [], [], []
-                    QUEUE.put(np.hstack((bs, ba, br)))  # put data in the queue
+
+                    # update
                     if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
                         ROLLING_EVENT.clear()  # stop collecting data
                         UPDATE_EVENT.set()  # globalPPO update
 
-                    if GLOBAL_EP >= EP_MAX:  # stop training
+                    # stop training
+                    if GLOBAL_EP >= TRAIN_EPISODES:
                         COORD.request_stop()
                         break
 
+            print(
+                'Training  | Episode: {}/{}  | Worker: {} | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    GLOBAL_EP + 1, TRAIN_EPISODES, self.wid, ep_r, time.time() - T0
+                )
+            )
             # record reward changes, plot later
             if len(GLOBAL_RUNNING_R) == 0:
                 GLOBAL_RUNNING_R.append(ep_r)
@@ -339,13 +318,6 @@ def work(self):
                 GLOBAL_RUNNING_R.append(GLOBAL_RUNNING_R[-1] * 0.9 + ep_r * 0.1)
             GLOBAL_EP += 1
 
-            print(
-                'Episode: {}/{}  | Worker: {} | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                    GLOBAL_EP, EP_MAX, self.wid, ep_r,
-                    time.time() - t0
-                )
-            )
-
 
 if __name__ == '__main__':
 
@@ -353,7 +325,14 @@ def work(self):
     np.random.seed(RANDOMSEED)
     tf.random.set_seed(RANDOMSEED)
 
-    GLOBAL_PPO = PPO()
+    env = gym.make(ENV_ID)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_bound = env.action_space.high
+    env.close()
+
+    GLOBAL_PPO = PPO(state_dim, action_dim, action_bound)
+    T0 = time.time()
     if args.train:  # train
         UPDATE_EVENT, ROLLING_EVENT = threading.Event(), threading.Event()
         UPDATE_EVENT.clear()  # not update now
@@ -366,31 +345,34 @@ def work(self):
         QUEUE = queue.Queue()  # workers putting data in this queue
         threads = []
         for worker in workers:  # worker threads
-            t = threading.Thread(target=worker.work, args=())
+            t = threading.Thread(target=worker.work)
             t.start()  # training
             threads.append(t)
         # add a PPO updating thread
-        threads.append(threading.Thread(target=GLOBAL_PPO.update, ))
+        threads.append(threading.Thread(target=GLOBAL_PPO.update))
         threads[-1].start()
         COORD.join(threads)
 
-        GLOBAL_PPO.save_ckpt()
+        GLOBAL_PPO.save()
 
-        # plot reward change and test
-        plt.title('DPPO')
-        plt.plot(np.arange(len(GLOBAL_RUNNING_R)), GLOBAL_RUNNING_R)
-        plt.xlabel('Episode')
-        plt.ylabel('Moving reward')
-        plt.ylim(-2000, 0)
-        plt.show()
+        plt.plot(GLOBAL_RUNNING_R)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
 
     # test
-    GLOBAL_PPO.load_ckpt()
-    env = gym.make(GAME)
-    while True:
-        s = env.reset()
-        for t in range(EP_LEN):
-            env.render()
-            s, r, done, info = env.step(GLOBAL_PPO.choose_action(s))
-            if done:
-                break
+    if args.test:
+        GLOBAL_PPO.load()
+        for episode in range(TEST_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                env.render()
+                state, reward, done, info = env.step(GLOBAL_PPO.get_action(state, greedy=True))
+                episode_reward += reward
+                if done:
+                    break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - T0))
diff --git a/examples/reinforcement_learning/tutorial_DQN.py b/examples/reinforcement_learning/tutorial_DQN.py
index c7d6a10cd..5fdabdeb2 100644
--- a/examples/reinforcement_learning/tutorial_DQN.py
+++ b/examples/reinforcement_learning/tutorial_DQN.py
@@ -1,183 +1,182 @@
-"""
-Deep Q-Network Q(a, s)
------------------------
-TD Learning, Off-Policy, e-Greedy Exploration (GLIE).
-
-Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
-delta_w = R + lambda * Q(newS, newA)
-
-See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
-
-Reference
-----------
-original paper: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
-EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
-CN: https://zhuanlan.zhihu.com/p/25710327
-
-Note: Policy Network has been proved to be better than Q-Learning, see tutorial_atari_pong.py
-
-Environment
------------
-# The FrozenLake v0 environment
-https://gym.openai.com/envs/FrozenLake-v0
-The agent controls the movement of a character in a grid world. Some tiles of
-the grid are walkable, and others lead to the agent falling into the water.
-Additionally, the movement direction of the agent is uncertain and only partially
-depends on the chosen direction. The agent is rewarded for finding a walkable
-path to a goal tile.
-SFFF       (S: starting point, safe)
-FHFH       (F: frozen surface, safe)
-FFFH       (H: hole, fall to your doom)
-HFFG       (G: goal, where the frisbee is located)
-The episode ends when you reach the goal or fall in a hole. You receive a reward
-of 1 if you reach the goal, and zero otherwise.
-
-Prerequisites
---------------
-tensorflow>=2.0.0a0
-tensorlayer>=2.0.0
-
-To run
--------
-python tutorial_DQN.py --train/test
-
-
-"""
-import argparse
-import time
-
-import numpy as np
-
-import gym
-import tensorflow as tf
-import tensorlayer as tl
-
-# add arguments in command  --train/test
-parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=False)
-parser.add_argument('--test', dest='test', action='store_true', default=True)
-args = parser.parse_args()
-
-tl.logging.set_verbosity(tl.logging.DEBUG)
-
-#####################  hyper parameters  ####################
-lambd = .99  # decay factor
-e = 0.1  # e-Greedy Exploration, the larger the more random
-num_episodes = 10000
-render = False  # display the game environment
-running_reward = None
-
-##################### DQN ##########################
-
-
-def to_one_hot(i, n_classes=None):
-    a = np.zeros(n_classes, 'uint8')
-    a[i] = 1
-    return a
-
-
-## Define Q-network q(a,s) that ouput the rewards of 4 actions by given state, i.e. Action-Value Function.
-# encoding for state: 4x4 grid can be represented by one-hot vector with 16 integers.
-def get_model(inputs_shape):
-    ni = tl.layers.Input(inputs_shape, name='observation')
-    nn = tl.layers.Dense(4, act=None, W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')(ni)
-    return tl.models.Model(inputs=ni, outputs=nn, name="Q-Network")
-
-
-def save_ckpt(model):  # save trained weights
-    tl.files.save_npz(model.trainable_weights, name='dqn_model.npz')
-
-
-def load_ckpt(model):  # load trained weights
-    tl.files.load_and_assign_npz(name='dqn_model.npz', network=model)
-
-
-if __name__ == '__main__':
-
-    qnetwork = get_model([None, 16])
-    qnetwork.train()
-    train_weights = qnetwork.trainable_weights
-
-    optimizer = tf.optimizers.SGD(learning_rate=0.1)
-    env = gym.make('FrozenLake-v0')
-
-    if args.train:
-        t0 = time.time()
-        for i in range(num_episodes):
-            ## Reset environment and get first new observation
-            # episode_time = time.time()
-            s = env.reset()  # observation is state, integer 0 ~ 15
-            rAll = 0
-            for j in range(99):  # step index, maximum step is 99
-                if render: env.render()
-                ## Choose an action by greedily (with e chance of random action) from the Q-network
-                allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
-                a = np.argmax(allQ, 1)
-
-                ## e-Greedy Exploration !!! sample random action
-                if np.random.rand(1) < e:
-                    a[0] = env.action_space.sample()
-                ## Get new state and reward from environment
-                s1, r, d, _ = env.step(a[0])
-                ## Obtain the Q' values by feeding the new state through our network
-                Q1 = qnetwork(np.asarray([to_one_hot(s1, 16)], dtype=np.float32)).numpy()
-
-                ## Obtain maxQ' and set our target value for chosen action.
-                maxQ1 = np.max(Q1)  # in Q-Learning, policy is greedy, so we use "max" to select the next action.
-                targetQ = allQ
-                targetQ[0, a[0]] = r + lambd * maxQ1
-                ## Train network using target and predicted Q values
-                # it is not real target Q value, it is just an estimation,
-                # but check the Q-Learning update formula:
-                #    Q'(s,a) <- Q(s,a) + alpha(r + lambd * maxQ(s',a') - Q(s, a))
-                # minimizing |r + lambd * maxQ(s',a') - Q(s, a)|^2 equals to force Q'(s,a) ≈ Q(s,a)
-                with tf.GradientTape() as tape:
-                    _qvalues = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32))
-                    _loss = tl.cost.mean_squared_error(targetQ, _qvalues, is_mean=False)
-                grad = tape.gradient(_loss, train_weights)
-                optimizer.apply_gradients(zip(grad, train_weights))
-
-                rAll += r
-                s = s1
-                ## Reduce chance of random action if an episode is done.
-                if d ==True:
-                    e = 1. / ((i / 50) + 10)  # reduce e, GLIE: Greey in the limit with infinite Exploration
-                    break
-
-            ## Note that, the rewards here with random action
-            running_reward = rAll if running_reward is None else running_reward * 0.99 + rAll * 0.01
-            # print("Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs " % \
-            #     (i, num_episodes, rAll, running_reward, time.time() - episode_time))
-            print('Episode: {}/{}  | Episode Reward: {:.4f} | Running Average Reward: {:.4f}  | Running Time: {:.4f}'\
-            .format(i, num_episodes, rAll, running_reward,  time.time()-t0 ))
-        save_ckpt(qnetwork)  # save model
-
-    if args.test:
-        t0 = time.time()
-        load_ckpt(qnetwork)  # load model
-        for i in range(num_episodes):
-            ## Reset environment and get first new observation
-            episode_time = time.time()
-            s = env.reset()  # observation is state, integer 0 ~ 15
-            rAll = 0
-            for j in range(99):  # step index, maximum step is 99
-                if render: env.render()
-                ## Choose an action by greedily (with e chance of random action) from the Q-network
-                allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
-                a = np.argmax(allQ, 1)  # no epsilon, only greedy for testing
-
-                ## Get new state and reward from environment
-                s1, r, d, _ = env.step(a[0])
-                rAll += r
-                s = s1
-                ## Reduce chance of random action if an episode is done.
-                if d ==True:
-                    e = 1. / ((i / 50) + 10)  # reduce e, GLIE: Greey in the limit with infinite Exploration
-                    break
-
-            ## Note that, the rewards here with random action
-            running_reward = rAll if running_reward is None else running_reward * 0.99 + rAll * 0.01
-            # print("Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs " % \
-            #     (i, num_episodes, rAll, running_reward, time.time() - episode_time))
-            print('Episode: {}/{}  | Episode Reward: {:.4f} | Running Average Reward: {:.4f}  | Running Time: {:.4f}'\
-            .format(i, num_episodes, rAll, running_reward,  time.time()-t0 ))
+"""
+Deep Q-Network Q(a, s)
+-----------------------
+TD Learning, Off-Policy, e-Greedy Exploration (GLIE).
+Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
+delta_w = R + lambda * Q(newS, newA)
+See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
+Reference
+----------
+original paper: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
+EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
+CN: https://zhuanlan.zhihu.com/p/25710327
+Note: Policy Network has been proved to be better than Q-Learning, see tutorial_atari_pong.py
+Environment
+-----------
+# The FrozenLake v0 environment
+https://gym.openai.com/envs/FrozenLake-v0
+The agent controls the movement of a character in a grid world. Some tiles of
+the grid are walkable, and others lead to the agent falling into the water.
+Additionally, the movement direction of the agent is uncertain and only partially
+depends on the chosen direction. The agent is rewarded for finding a walkable
+path to a goal tile.
+SFFF       (S: starting point, safe)
+FHFH       (F: frozen surface, safe)
+FFFH       (H: hole, fall to your doom)
+HFFG       (G: goal, where the frisbee is located)
+The episode ends when you reach the goal or fall in a hole. You receive a reward
+of 1 if you reach the goal, and zero otherwise.
+Prerequisites
+--------------
+tensorflow>=2.0.0a0
+tensorlayer>=2.0.0
+To run
+-------
+python tutorial_DQN.py --train/test
+"""
+import argparse
+import os
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+import tensorlayer as tl
+
+# add arguments in command  --train/test
+parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
+parser.add_argument('--train', dest='train', action='store_true', default=True)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+args = parser.parse_args()
+
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+#####################  hyper parameters  ####################
+env_id = 'FrozenLake-v0'
+alg_name = 'DQN'
+lambd = .99  # decay factor
+e = 0.1  # e-Greedy Exploration, the larger the more random
+num_episodes = 10000
+render = False  # display the game environment
+
+##################### DQN ##########################
+
+
+def to_one_hot(i, n_classes=None):
+    a = np.zeros(n_classes, 'uint8')
+    a[i] = 1
+    return a
+
+
+## Define Q-network q(a,s) that ouput the rewards of 4 actions by given state, i.e. Action-Value Function.
+# encoding for state: 4x4 grid can be represented by one-hot vector with 16 integers.
+def get_model(inputs_shape):
+    ni = tl.layers.Input(inputs_shape, name='observation')
+    nn = tl.layers.Dense(4, act=None, W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')(ni)
+    return tl.models.Model(inputs=ni, outputs=nn, name="Q-Network")
+
+
+def save_ckpt(model):  # save trained weights
+    path = os.path.join('model', '_'.join([alg_name, env_id]))
+    if not os.path.exists(path):
+        os.makedirs(path)
+    tl.files.save_weights_to_hdf5(os.path.join(path, 'dqn_model.hdf5'), model)
+
+
+def load_ckpt(model):  # load trained weights
+    path = os.path.join('model', '_'.join([alg_name, env_id]))
+    tl.files.save_weights_to_hdf5(os.path.join(path, 'dqn_model.hdf5'), model)
+
+
+if __name__ == '__main__':
+
+    qnetwork = get_model([None, 16])
+    qnetwork.train()
+    train_weights = qnetwork.trainable_weights
+
+    optimizer = tf.optimizers.SGD(learning_rate=0.1)
+    env = gym.make(env_id)
+
+    t0 = time.time()
+    if args.train:
+        all_episode_reward = []
+        for i in range(num_episodes):
+            ## Reset environment and get first new observation
+            s = env.reset()  # observation is state, integer 0 ~ 15
+            rAll = 0
+            if render: env.render()
+            for j in range(99):  # step index, maximum step is 99
+                ## Choose an action by greedily (with e chance of random action) from the Q-network
+                allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
+                a = np.argmax(allQ, 1)
+
+                ## e-Greedy Exploration !!! sample random action
+                if np.random.rand(1) < e:
+                    a[0] = env.action_space.sample()
+                ## Get new state and reward from environment
+                s1, r, d, _ = env.step(a[0])
+                if render: env.render()
+                ## Obtain the Q' values by feeding the new state through our network
+                Q1 = qnetwork(np.asarray([to_one_hot(s1, 16)], dtype=np.float32)).numpy()
+
+                ## Obtain maxQ' and set our target value for chosen action.
+                maxQ1 = np.max(Q1)  # in Q-Learning, policy is greedy, so we use "max" to select the next action.
+                targetQ = allQ
+                targetQ[0, a[0]] = r + lambd * maxQ1
+                ## Train network using target and predicted Q values
+                # it is not real target Q value, it is just an estimation,
+                # but check the Q-Learning update formula:
+                #    Q'(s,a) <- Q(s,a) + alpha(r + lambd * maxQ(s',a') - Q(s, a))
+                # minimizing |r + lambd * maxQ(s',a') - Q(s, a)|^2 equals to force Q'(s,a) ≈ Q(s,a)
+                with tf.GradientTape() as tape:
+                    _qvalues = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32))
+                    _loss = tl.cost.mean_squared_error(targetQ, _qvalues, is_mean=False)
+                grad = tape.gradient(_loss, train_weights)
+                optimizer.apply_gradients(zip(grad, train_weights))
+
+                rAll += r
+                s = s1
+                ## Reduce chance of random action if an episode is done.
+                if d ==True:
+                    e = 1. / ((i / 50) + 10)  # reduce e, GLIE: Greey in the limit with infinite Exploration
+                    break
+
+            ## Note that, the rewards here with random action
+            print('Training  | Episode: {}/{}  | Episode Reward: {:.4f} | Running Time: {:.4f}' \
+                  .format(i, num_episodes, rAll, time.time() - t0))
+
+            if i == 0:
+                all_episode_reward.append(rAll)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + rAll * 0.1)
+
+        save_ckpt(qnetwork)  # save model
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
+
+    if args.test:
+        load_ckpt(qnetwork)  # load model
+        for i in range(num_episodes):
+            ## Reset environment and get first new observation
+            s = env.reset()  # observation is state, integer 0 ~ 15
+            rAll = 0
+            if render: env.render()
+            for j in range(99):  # step index, maximum step is 99
+                ## Choose an action by greedily (with e chance of random action) from the Q-network
+                allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
+                a = np.argmax(allQ, 1)  # no epsilon, only greedy for testing
+
+                ## Get new state and reward from environment
+                s1, r, d, _ = env.step(a[0])
+                rAll += r
+                s = s1
+                if render: env.render()
+                ## Reduce chance of random action if an episode is done.
+                if d: break
+
+            print('Testing  | Episode: {}/{}  | Episode Reward: {:.4f} | Running Time: {:.4f}' \
+                  .format(i, num_episodes, rAll, time.time() - t0))
diff --git a/examples/reinforcement_learning/tutorial_DQN_variants.py b/examples/reinforcement_learning/tutorial_DQN_variants.py
index f4bf7954e..5195ef61f 100644
--- a/examples/reinforcement_learning/tutorial_DQN_variants.py
+++ b/examples/reinforcement_learning/tutorial_DQN_variants.py
@@ -1,373 +1,433 @@
-"""
-DQN and its variants
-------------------------
-We implement Double DQN, Dueling DQN and Noisy DQN here.
-
-The max operator in standard DQN uses the same values both to select and to
-evaluate an action by
-Q(s_t, a_t) = R_{t+1} + \gamma * max_{a}Q_{tar}(s_{t+1}, a).
-Double DQN propose to use following evaluation to address overestimation problem
-of max operator:
-Q(s_t, a_t) = R_{t+1} + \gamma * Q_{tar}(s_{t+1}, max_{a}Q(s_{t+1}, a)).
-
-Dueling DQN uses dueling architecture where the value of state and the advantage
-of each action is estimated separately.
-
-Noisy DQN propose to explore by adding parameter noises.
-
-
-Reference:
-------------------------
-1. Double DQN
-    Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double
-    q-learning[C]//Thirtieth AAAI Conference on Artificial Intelligence. 2016.
-2. Dueling DQN
-    Wang Z, Schaul T, Hessel M, et al. Dueling network architectures for deep
-    reinforcement learning[J]. arXiv preprint arXiv:1511.06581, 2015.
-3. Noisy DQN
-    Plappert M, Houthooft R, Dhariwal P, et al. Parameter space noise for
-    exploration[J]. arXiv preprint arXiv:1706.01905, 2017.
-
-
-Environment:
-------------------------
-Cartpole and Pong in OpenAI Gym
-
-
-Requirements:
-------------------------
-tensorflow>=2.0.0a0
-tensorlayer>=2.0.0
-
-
-To run:
-------------------------
-python tutorial_DQN_variantes.py --mode=train
-python tutorial_DQN_variantes.py --mode=test --save_path=dqn_variants/8000.npz
-"""
-import argparse
-import os
-import random
-import time
-
-import numpy as np
-
-import tensorflow as tf
-import tensorlayer as tl
-from tutorial_wrappers import build_env
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--mode', help='train or test', default='train')
-parser.add_argument(
-    '--save_path', default='dqn_variants', help='folder to save if mode == train else model path,'
-    'qnet will be saved once target net update'
-)
-parser.add_argument('--seed', help='random seed', type=int, default=0)
-parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
-args = parser.parse_args()
-
-if args.mode == 'train':
-    os.makedirs(args.save_path, exist_ok=True)
-random.seed(args.seed)
-np.random.seed(args.seed)
-tf.random.set_seed(args.seed)  # reproducible
-env_id = args.env_id
-env = build_env(env_id, seed=args.seed)
-
-# ####################  hyper parameters  ####################
-if env_id == 'CartPole-v0':
-    qnet_type = 'MLP'
-    number_timesteps = 10000  # total number of time steps to train on
-    explore_timesteps = 100
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 5e-3  # learning rate
-    buffer_size = 1000  # replay buffer size
-    target_q_update_freq = 50  # how frequency target q net update
-    ob_scale = 1.0  # scale observations
-else:
-    # reward will increase obviously after 1e5 time steps
-    qnet_type = 'CNN'
-    number_timesteps = int(1e6)  # total number of time steps to train on
-    explore_timesteps = 1e5
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 1e-4  # learning rate
-    buffer_size = 10000  # replay buffer size
-    target_q_update_freq = 200  # how frequency target q net update
-    ob_scale = 1.0 / 255  # scale observations
-
-in_dim = env.observation_space.shape
-out_dim = env.action_space.n
-reward_gamma = 0.99  # reward discount
-batch_size = 32  # batch size for sampling from replay buffer
-warm_start = buffer_size / 10  # sample times befor learning
-noise_update_freq = 50  # how frequency param noise net update
-
-
-# ##############################  DQN  ####################################
-class MLP(tl.models.Model):
-
-    def __init__(self, name):
-        super(MLP, self).__init__(name=name)
-        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
-        self.svalue = tl.layers.Dense(1, in_channels=64, name='s', W_init=tf.initializers.GlorotUniform())
-        self.noise_scale = 0
-
-    def forward(self, ni):
-        feature = self.h1(ni)
-
-        # apply noise to all linear layer
-        if self.noise_scale != 0:
-            noises = []
-            for layer in [self.qvalue, self.svalue]:
-                for var in layer.trainable_weights:
-                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
-                    noises.append(noise)
-                    var.assign_add(noise)
-
-        qvalue = self.qvalue(feature)
-        svalue = self.svalue(feature)
-
-        if self.noise_scale != 0:
-            idx = 0
-            for layer in [self.qvalue, self.svalue]:
-                for var in layer.trainable_weights:
-                    var.assign_sub(noises[idx])
-                    idx += 1
-
-        # dueling network
-        out = svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
-        return out
-
-
-class CNN(tl.models.Model):
-
-    def __init__(self, name):
-        super(CNN, self).__init__(name=name)
-        h, w, in_channels = in_dim
-        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
-        self.conv1 = tl.layers.Conv2d(
-            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv2 = tl.layers.Conv2d(
-            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv3 = tl.layers.Conv2d(
-            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.flatten = tl.layers.Flatten(name='flatten')
-        self.preq = tl.layers.Dense(
-            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
-        self.pres = tl.layers.Dense(
-            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_s', W_init=tf.initializers.GlorotUniform()
-        )
-        self.svalue = tl.layers.Dense(1, in_channels=256, name='state', W_init=tf.initializers.GlorotUniform())
-        self.noise_scale = 0
-
-    def forward(self, ni):
-        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
-
-        # apply noise to all linear layer
-        if self.noise_scale != 0:
-            noises = []
-            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
-                for var in layer.trainable_weights:
-                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
-                    noises.append(noise)
-                    var.assign_add(noise)
-
-        qvalue = self.qvalue(self.preq(feature))
-        svalue = self.svalue(self.pres(feature))
-
-        if self.noise_scale != 0:
-            idx = 0
-            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
-                for var in layer.trainable_weights:
-                    var.assign_sub(noises[idx])
-                    idx += 1
-
-        # dueling network
-        return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
-
-
-class ReplayBuffer(object):
-
-    def __init__(self, size):
-        self._storage = []
-        self._maxsize = size
-        self._next_idx = 0
-
-    def __len__(self):
-        return len(self._storage)
-
-    def add(self, *args):
-        if self._next_idx >= len(self._storage):
-            self._storage.append(args)
-        else:
-            self._storage[self._next_idx] = args
-        self._next_idx = (self._next_idx + 1) % self._maxsize
-
-    def _encode_sample(self, idxes):
-        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
-        for i in idxes:
-            o, a, r, o_, d = self._storage[i]
-            b_o.append(o)
-            b_a.append(a)
-            b_r.append(r)
-            b_o_.append(o_)
-            b_d.append(d)
-        return (
-            np.stack(b_o).astype('float32') * ob_scale,
-            np.stack(b_a).astype('int32'),
-            np.stack(b_r).astype('float32'),
-            np.stack(b_o_).astype('float32') * ob_scale,
-            np.stack(b_d).astype('float32'),
-        )
-
-    def sample(self, batch_size):
-        indexes = range(len(self._storage))
-        idxes = [random.choice(indexes) for _ in range(batch_size)]
-        return self._encode_sample(idxes)
-
-
-def huber_loss(x):
-    """Loss function for value"""
-    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
-
-
-def sync(net, net_tar):
-    """Copy q network to target q network"""
-    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
-        var_tar.assign(var)
-
-
-def log_softmax(x, dim):
-    temp = x - np.max(x, dim, keepdims=True)
-    return temp - np.log(np.exp(temp).sum(dim, keepdims=True))
-
-
-def softmax(x, dim):
-    temp = np.exp(x - np.max(x, dim, keepdims=True))
-    return temp / temp.sum(dim, keepdims=True)
-
-
-if __name__ == '__main__':
-    if args.mode == 'train':
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        qnet.train()
-        trainabel_weights = qnet.trainable_weights
-        targetqnet = MLP('targetq') if qnet_type == 'MLP' else CNN('targetq')
-        targetqnet.infer()
-        sync(qnet, targetqnet)
-        optimizer = tf.optimizers.Adam(learning_rate=lr)
-        buffer = ReplayBuffer(buffer_size)
-
-        o = env.reset()
-        nepisode = 0
-        t = time.time()
-        noise_scale = 1e-2
-        for i in range(1, number_timesteps + 1):
-            eps = epsilon(i)
-
-            # select action
-            if random.random() < eps:
-                a = int(random.random() * out_dim)
-            else:
-                # noise schedule is based on KL divergence between perturbed and
-                # non-perturbed policy, see https://arxiv.org/pdf/1706.01905.pdf
-                obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-                if i < explore_timesteps:
-                    qnet.noise_scale = noise_scale
-                    q_ptb = qnet(obv).numpy()
-                    qnet.noise_scale = 0
-                    if i % noise_update_freq == 0:
-                        q = qnet(obv).numpy()
-                        kl_ptb = (log_softmax(q, 1) - log_softmax(q_ptb, 1))
-                        kl_ptb = np.sum(kl_ptb * softmax(q, 1), 1).mean()
-                        kl_explore = -np.log(1 - eps + eps / out_dim)
-                        if kl_ptb < kl_explore:
-                            noise_scale *= 1.01
-                        else:
-                            noise_scale /= 1.01
-                    a = q_ptb.argmax(1)[0]
-                else:
-                    a = qnet(obv).numpy().argmax(1)[0]
-
-            # execute action and feed to replay buffer
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-            buffer.add(o, a, r, o_, done)
-
-            if i >= warm_start:
-                # sync q net and target q net
-                if i % target_q_update_freq == 0:
-                    sync(qnet, targetqnet)
-                    path = os.path.join(args.save_path, '{}.npz'.format(i))
-                    tl.files.save_npz(qnet.trainable_weights, name=path)
-
-                # sample from replay buffer
-                b_o, b_a, b_r, b_o_, b_d = buffer.sample(batch_size)
-
-                # double q estimation
-                b_a_ = tf.one_hot(tf.argmax(qnet(b_o_), 1), out_dim)
-                b_q_ = (1 - b_d) * tf.reduce_sum(targetqnet(b_o_) * b_a_, 1)
-
-                # calculate loss
-                with tf.GradientTape() as q_tape:
-                    b_q = tf.reduce_sum(qnet(b_o) * tf.one_hot(b_a, out_dim), 1)
-                    loss = tf.reduce_mean(huber_loss(b_q - (b_r + reward_gamma * b_q_)))
-
-                # backward gradients
-                q_grad = q_tape.gradient(loss, trainabel_weights)
-                optimizer.apply_gradients(zip(q_grad, trainabel_weights))
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                fps = int(length / (time.time() - t))
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}, FPS: {}'.format(i, nepisode, reward, length, fps)
-                )
-                t = time.time()
-    else:
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        tl.files.load_and_assign_npz(name=args.save_path, network=qnet)
-        qnet.eval()
-
-        nepisode = 0
-        o = env.reset()
-        for i in range(1, number_timesteps + 1):
-            obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-            a = qnet(obv).numpy().argmax(1)[0]
-
-            # execute action
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}'.format(i, nepisode, reward, length)
-                )
+"""
+DQN and its variants
+------------------------
+We implement Double DQN, Dueling DQN and Noisy DQN here.
+The max operator in standard DQN uses the same values both to select and to
+evaluate an action by
+Q(s_t, a_t) = R_{t+1} + \gamma * max_{a}Q_{tar}(s_{t+1}, a).
+Double DQN propose to use following evaluation to address overestimation problem
+of max operator:
+Q(s_t, a_t) = R_{t+1} + \gamma * Q_{tar}(s_{t+1}, max_{a}Q(s_{t+1}, a)).
+Dueling DQN uses dueling architecture where the value of state and the advantage
+of each action is estimated separately.
+Noisy DQN propose to explore by adding parameter noises.
+Reference:
+------------------------
+1. Double DQN
+    Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double
+    q-learning[C]//Thirtieth AAAI Conference on Artificial Intelligence. 2016.
+2. Dueling DQN
+    Wang Z, Schaul T, Hessel M, et al. Dueling network architectures for deep
+    reinforcement learning[J]. arXiv preprint arXiv:1511.06581, 2015.
+3. Noisy DQN
+    Plappert M, Houthooft R, Dhariwal P, et al. Parameter space noise for
+    exploration[J]. arXiv preprint arXiv:1706.01905, 2017.
+Environment:
+------------------------
+Cartpole and Pong in OpenAI Gym
+Requirements:
+------------------------
+tensorflow>=2.0.0a0
+tensorlayer>=2.0.0
+To run:
+------------------------
+python tutorial_DQN_variantes.py --mode=train
+python tutorial_DQN_variantes.py --mode=test --save_path=dqn_variants/8000.npz
+"""
+import argparse
+import os
+import random
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+import tensorlayer as tl
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--train', dest='train', action='store_true', default=True)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+parser.add_argument(
+    '--save_path', default=None, help='folder to save if mode == train else model path,'
+    'qnet will be saved once target net update'
+)
+parser.add_argument('--seed', help='random seed', type=int, default=0)
+parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
+parser.add_argument('--noisy_scale', type=float, default=1e-2)
+parser.add_argument('--disable_double', action='store_true', default=False)
+parser.add_argument('--disable_dueling', action='store_true', default=False)
+args = parser.parse_args()
+
+random.seed(args.seed)
+np.random.seed(args.seed)
+tf.random.set_seed(args.seed)  # reproducible
+
+env_id = args.env_id
+env = gym.make(env_id)
+env.seed(args.seed)
+noise_scale = args.noisy_scale
+double = not args.disable_double
+dueling = not args.disable_dueling
+
+alg_name = 'DQN'
+if dueling: alg_name = 'Dueling_' + alg_name
+if double: alg_name = 'Double_' + alg_name
+if noise_scale != 0: alg_name = 'Noisy_' + alg_name
+print(alg_name)
+# ####################  hyper parameters  ####################
+if env_id == 'CartPole-v0':
+    qnet_type = 'MLP'
+    number_timesteps = 10000  # total number of time steps to train on
+    explore_timesteps = 100
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 5e-3  # learning rate
+    buffer_size = 1000  # replay buffer size
+    target_q_update_freq = 50  # how frequency target q net update
+    ob_scale = 1.0  # scale observations
+    clipnorm = None
+else:
+    # reward will increase obviously after 1e5 time steps
+    qnet_type = 'CNN'
+    number_timesteps = int(1e6)  # total number of time steps to train on
+    explore_timesteps = 1e5
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 1e-4  # learning rate
+    buffer_size = 10000  # replay buffer size
+    target_q_update_freq = 200  # how frequency target q net update
+    ob_scale = 1.0 / 255  # scale observations
+    clipnorm = 10
+
+in_dim = env.observation_space.shape
+out_dim = env.action_space.n
+reward_gamma = 0.99  # reward discount
+batch_size = 32  # batch size for sampling from replay buffer
+warm_start = buffer_size / 10  # sample times befor learning
+noise_update_freq = 50  # how frequency param noise net update
+
+
+# ##############################  Network  ####################################
+class MLP(tl.models.Model):
+
+    def __init__(self, name):
+        super(MLP, self).__init__(name=name)
+        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
+        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
+        self.svalue = tl.layers.Dense(1, in_channels=64, name='s', W_init=tf.initializers.GlorotUniform())
+        self.noise_scale = 0
+
+    def forward(self, ni):
+        feature = self.h1(ni)
+
+        # apply noise to all linear layer
+        if self.noise_scale != 0:
+            noises = []
+            for layer in [self.qvalue, self.svalue]:
+                for var in layer.trainable_weights:
+                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
+                    noises.append(noise)
+                    var.assign_add(noise)
+
+        qvalue = self.qvalue(feature)
+        svalue = self.svalue(feature)
+
+        if self.noise_scale != 0:
+            idx = 0
+            for layer in [self.qvalue, self.svalue]:
+                for var in layer.trainable_weights:
+                    var.assign_sub(noises[idx])
+                    idx += 1
+
+        if dueling:
+            # dueling network
+            return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
+        else:
+            return qvalue
+
+
+class CNN(tl.models.Model):
+
+    def __init__(self, name):
+        super(CNN, self).__init__(name=name)
+        h, w, in_channels = in_dim
+        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
+        self.conv1 = tl.layers.Conv2d(
+            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv2 = tl.layers.Conv2d(
+            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv3 = tl.layers.Conv2d(
+            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.flatten = tl.layers.Flatten(name='flatten')
+        self.preq = tl.layers.Dense(
+            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
+        )
+        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
+        self.pres = tl.layers.Dense(
+            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_s', W_init=tf.initializers.GlorotUniform()
+        )
+        self.svalue = tl.layers.Dense(1, in_channels=256, name='state', W_init=tf.initializers.GlorotUniform())
+        self.noise_scale = 0
+
+    def forward(self, ni):
+        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
+
+        # apply noise to all linear layer
+        if self.noise_scale != 0:
+            noises = []
+            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
+                for var in layer.trainable_weights:
+                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
+                    noises.append(noise)
+                    var.assign_add(noise)
+
+        qvalue = self.qvalue(self.preq(feature))
+        svalue = self.svalue(self.pres(feature))
+
+        if self.noise_scale != 0:
+            idx = 0
+            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
+                for var in layer.trainable_weights:
+                    var.assign_sub(noises[idx])
+                    idx += 1
+
+        if dueling:
+            # dueling network
+            return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
+        else:
+            return qvalue
+
+
+# ##############################  Replay  ####################################
+class ReplayBuffer(object):
+
+    def __init__(self, size):
+        self._storage = []
+        self._maxsize = size
+        self._next_idx = 0
+
+    def __len__(self):
+        return len(self._storage)
+
+    def add(self, *args):
+        if self._next_idx >= len(self._storage):
+            self._storage.append(args)
+        else:
+            self._storage[self._next_idx] = args
+        self._next_idx = (self._next_idx + 1) % self._maxsize
+
+    def _encode_sample(self, idxes):
+        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
+        for i in idxes:
+            o, a, r, o_, d = self._storage[i]
+            b_o.append(o)
+            b_a.append(a)
+            b_r.append(r)
+            b_o_.append(o_)
+            b_d.append(d)
+        return (
+            np.stack(b_o).astype('float32') * ob_scale,
+            np.stack(b_a).astype('int32'),
+            np.stack(b_r).astype('float32'),
+            np.stack(b_o_).astype('float32') * ob_scale,
+            np.stack(b_d).astype('float32'),
+        )
+
+    def sample(self, batch_size):
+        indexes = range(len(self._storage))
+        idxes = [random.choice(indexes) for _ in range(batch_size)]
+        return self._encode_sample(idxes)
+
+
+# #############################  Functions  ###################################
+def huber_loss(x):
+    """Loss function for value"""
+    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
+
+
+def sync(net, net_tar):
+    """Copy q network to target q network"""
+    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
+        var_tar.assign(var)
+
+
+def log_softmax(x, dim):
+    temp = x - np.max(x, dim, keepdims=True)
+    return temp - np.log(np.exp(temp).sum(dim, keepdims=True))
+
+
+def softmax(x, dim):
+    temp = np.exp(x - np.max(x, dim, keepdims=True))
+    return temp / temp.sum(dim, keepdims=True)
+
+
+# ###############################  DQN  #####################################
+class DQN(object):
+
+    def __init__(self):
+        model = MLP if qnet_type == 'MLP' else CNN
+        self.qnet = model('q')
+        if args.train:
+            self.qnet.train()
+            self.targetqnet = model('targetq')
+            self.targetqnet.infer()
+            sync(self.qnet, self.targetqnet)
+        else:
+            self.qnet.infer()
+            self.load(args.save_path)
+        self.niter = 0
+        if clipnorm is not None:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr, clipnorm=clipnorm)
+        else:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr)
+        self.noise_scale = noise_scale
+
+    def get_action(self, obv):
+        eps = epsilon(self.niter)
+        if args.train:
+            if random.random() < eps:
+                return int(random.random() * out_dim)
+            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
+            if self.niter < explore_timesteps:
+                self.qnet.noise_scale = self.noise_scale
+                q_ptb = self._qvalues_func(obv).numpy()
+                self.qnet.noise_scale = 0
+                if i % noise_update_freq == 0:
+                    q = self._qvalues_func(obv).numpy()
+                    kl_ptb = (log_softmax(q, 1) - log_softmax(q_ptb, 1))
+                    kl_ptb = np.sum(kl_ptb * softmax(q, 1), 1).mean()
+                    kl_explore = -np.log(1 - eps + eps / out_dim)
+                    if kl_ptb < kl_explore:
+                        self.noise_scale *= 1.01
+                    else:
+                        self.noise_scale /= 1.01
+                return q_ptb.argmax(1)[0]
+            else:
+                return self._qvalues_func(obv).numpy().argmax(1)[0]
+        else:
+            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
+            return self._qvalues_func(obv).numpy().argmax(1)[0]
+
+    @tf.function
+    def _qvalues_func(self, obv):
+        return self.qnet(obv)
+
+    def train(self, b_o, b_a, b_r, b_o_, b_d):
+        self._train_func(b_o, b_a, b_r, b_o_, b_d)
+
+        self.niter += 1
+        if self.niter % target_q_update_freq == 0:
+            sync(self.qnet, self.targetqnet)
+            self.save(args.save_path)
+
+    @tf.function
+    def _train_func(self, b_o, b_a, b_r, b_o_, b_d):
+        with tf.GradientTape() as tape:
+            td_errors = self._tderror_func(b_o, b_a, b_r, b_o_, b_d)
+            loss = tf.reduce_mean(huber_loss(td_errors))
+
+        grad = tape.gradient(loss, self.qnet.trainable_weights)
+        self.optimizer.apply_gradients(zip(grad, self.qnet.trainable_weights))
+
+        return td_errors
+
+    @tf.function
+    def _tderror_func(self, b_o, b_a, b_r, b_o_, b_d):
+        if double:
+            b_a_ = tf.one_hot(tf.argmax(self.qnet(b_o_), 1), out_dim)
+            b_q_ = (1 - b_d) * tf.reduce_sum(self.targetqnet(b_o_) * b_a_, 1)
+        else:
+            b_q_ = (1 - b_d) * tf.reduce_max(self.targetqnet(b_o_), 1)
+
+        b_q = tf.reduce_sum(self.qnet(b_o) * tf.one_hot(b_a, out_dim), 1)
+        return b_q - (b_r + reward_gamma * b_q_)
+
+    def save(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+    def load(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+
+# #############################  Trainer  ###################################
+if __name__ == '__main__':
+    dqn = DQN()
+    t0 = time.time()
+    if args.train:
+        buffer = ReplayBuffer(buffer_size)
+        nepisode = 0
+        all_episode_reward = []
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                a = dqn.get_action(o)
+
+                # execute action and feed to replay buffer
+                # note that `_` tail in var name means next
+                o_, r, done, info = env.step(a)
+                buffer.add(o, a, r, o_, done)
+                episode_reward += r
+
+                if i >= warm_start:
+                    transitions = buffer.sample(batch_size)
+                    dqn.train(*transitions)
+
+                if done:
+                    break
+                else:
+                    o = o_
+
+            if nepisode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            nepisode += 1
+            print(
+                'Training  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )  # episode num starts from 1 in print
+
+        dqn.save(args.save_path)
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
+
+    if args.test:
+        nepisode = 0
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                env.render()
+                a = dqn.get_action(o)
+                o_, r, done, info = env.step(a)
+                episode_reward += r
+                if done:
+                    break
+                else:
+                    o = o_
+            nepisode += 1
+            print(
+                'Testing  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_PG.py b/examples/reinforcement_learning/tutorial_PG.py
index c4a658a99..776cd6ac4 100644
--- a/examples/reinforcement_learning/tutorial_PG.py
+++ b/examples/reinforcement_learning/tutorial_PG.py
@@ -4,7 +4,7 @@
 The policy gradient algorithm works by updating policy parameters via stochastic gradient ascent on policy performance.
 It's an on-policy algorithm can be used for environments with either discrete or continuous action spaces.
 Here is an example on discrete action space game CartPole-v0.
-To apply it on continuous action space, you need to change the last softmax layer and the choose_action function.
+To apply it on continuous action space, you need to change the last softmax layer and the get_action function.
 
 Reference
 ---------
@@ -30,26 +30,28 @@
 import os
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
-
-import gym
 import tensorflow as tf
+
 import tensorlayer as tl
 
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=True)
-parser.add_argument('--test', dest='train', action='store_false')
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
 args = parser.parse_args()
 
 #####################  hyper parameters  ####################
 
-ENV_NAME = 'CartPole-v0'  # environment name
-RANDOMSEED = 1  # random seed
+ENV_ID = 'CartPole-v1'  # environment id
+RANDOM_SEED = 1  # random seed, can be either an int number or None
+RENDER = False  # render while training
 
-DISPLAY_REWARD_THRESHOLD = 400  # renders environment if total episode reward is greater then this threshold
-RENDER = False  # rendering wastes time
-num_episodes = 3000
+ALG_NAME = 'PG'
+TRAIN_EPISODES = 200
+TEST_EPISODES = 10
+MAX_STEPS = 500
 
 ###############################  PG  ####################################
 
@@ -59,63 +61,38 @@ class PolicyGradient:
     PG class
     """
 
-    def __init__(self, n_features, n_actions, learning_rate=0.01, reward_decay=0.95):
-        self.n_actions = n_actions
-        self.n_features = n_features
-        self.lr = learning_rate
-        self.gamma = reward_decay
-
-        self.ep_obs, self.ep_as, self.ep_rs = [], [], []
-
-        def get_model(inputs_shape):
-            """
-            Build a neural network model.
-            :param inputs_shape: state_shape
-            :return: act
-            """
-            with tf.name_scope('inputs'):
-                self.tf_obs = tl.layers.Input(inputs_shape, tf.float32, name="observations")
-                self.tf_acts = tl.layers.Input([
-                    None,
-                ], tf.int32, name="actions_num")
-                self.tf_vt = tl.layers.Input([
-                    None,
-                ], tf.float32, name="actions_value")
-            # fc1
-            layer = tl.layers.Dense(
-                n_units=30, act=tf.nn.tanh, W_init=tf.random_normal_initializer(mean=0, stddev=0.3),
-                b_init=tf.constant_initializer(0.1), name='fc1'
-            )(self.tf_obs)
-            # fc2
-            all_act = tl.layers.Dense(
-                n_units=self.n_actions, act=None, W_init=tf.random_normal_initializer(mean=0, stddev=0.3),
-                b_init=tf.constant_initializer(0.1), name='all_act'
-            )(layer)
-            return tl.models.Model(inputs=self.tf_obs, outputs=all_act, name='PG model')
-
-        self.model = get_model([None, n_features])
+    def __init__(self, state_dim, action_num, learning_rate=0.02, gamma=0.99):
+        self.gamma = gamma
+
+        self.state_buffer, self.action_buffer, self.reward_buffer = [], [], []
+
+        input_layer = tl.layers.Input([None, state_dim], tf.float32)
+        layer = tl.layers.Dense(
+            n_units=30, act=tf.nn.tanh, W_init=tf.random_normal_initializer(mean=0, stddev=0.3),
+            b_init=tf.constant_initializer(0.1)
+        )(input_layer)
+        all_act = tl.layers.Dense(
+            n_units=action_num, act=None, W_init=tf.random_normal_initializer(mean=0, stddev=0.3),
+            b_init=tf.constant_initializer(0.1)
+        )(layer)
+
+        self.model = tl.models.Model(inputs=input_layer, outputs=all_act)
         self.model.train()
-        self.optimizer = tf.optimizers.Adam(self.lr)
+        self.optimizer = tf.optimizers.Adam(learning_rate)
 
-    def choose_action(self, s):
+    def get_action(self, s, greedy=False):
         """
         choose action with probabilities.
         :param s: state
+        :param greedy: choose action greedy or not
         :return: act
         """
         _logits = self.model(np.array([s], np.float32))
         _probs = tf.nn.softmax(_logits).numpy()
+        if greedy:
+            return np.argmax(_probs.ravel())
         return tl.rein.choice_action_by_probs(_probs.ravel())
 
-    def choose_action_greedy(self, s):
-        """
-        choose action with greedy policy
-        :param s: state
-        :return: act
-        """
-        _probs = tf.nn.softmax(self.model(np.array([s], np.float32))).numpy()
-        return np.argmax(_probs.ravel())
-
     def store_transition(self, s, a, r):
         """
         store data in memory buffer
@@ -124,35 +101,29 @@ def store_transition(self, s, a, r):
         :param r: reward
         :return:
         """
-        self.ep_obs.append(np.array([s], np.float32))
-        self.ep_as.append(a)
-        self.ep_rs.append(r)
+        self.state_buffer.append(np.array([s], np.float32))
+        self.action_buffer.append(a)
+        self.reward_buffer.append(r)
 
     def learn(self):
         """
         update policy parameters via stochastic gradient ascent
         :return: None
         """
-        # discount and normalize episode reward
-        discounted_ep_rs_norm = self._discount_and_norm_rewards()
+        discounted_reward_buffer_norm = self._discount_and_norm_rewards()
 
         with tf.GradientTape() as tape:
-
-            _logits = self.model(np.vstack(self.ep_obs))
-            # to maximize total reward (log_p * R) is to minimize -(log_p * R), and the tf only have minimize(loss)
-            neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=_logits, labels=np.array(self.ep_as))
-            # this is negative log of chosen action
-
-            # or in this way:
-            # neg_log_prob = tf.reduce_sum(-tf.log(self.all_act_prob)*tf.one_hot(self.tf_acts, self.n_actions), axis=1)
-
-            loss = tf.reduce_mean(neg_log_prob * discounted_ep_rs_norm)  # reward guided loss
+            _logits = self.model(np.vstack(self.state_buffer))
+            neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(
+                logits=_logits, labels=np.array(self.action_buffer)
+            )
+            loss = tf.reduce_mean(neg_log_prob * discounted_reward_buffer_norm)
 
         grad = tape.gradient(loss, self.model.trainable_weights)
         self.optimizer.apply_gradients(zip(grad, self.model.trainable_weights))
 
-        self.ep_obs, self.ep_as, self.ep_rs = [], [], []  # empty episode data
-        return discounted_ep_rs_norm
+        self.state_buffer, self.action_buffer, self.reward_buffer = [], [], []  # empty episode data
+        return discounted_reward_buffer_norm
 
     def _discount_and_norm_rewards(self):
         """
@@ -160,120 +131,103 @@ def _discount_and_norm_rewards(self):
         :return: discount_and_norm_rewards
         """
         # discount episode rewards
-        discounted_ep_rs = np.zeros_like(self.ep_rs)
+        discounted_reward_buffer = np.zeros_like(self.reward_buffer)
         running_add = 0
-        for t in reversed(range(0, len(self.ep_rs))):
-            running_add = running_add * self.gamma + self.ep_rs[t]
-            discounted_ep_rs[t] = running_add
+        for t in reversed(range(0, len(self.reward_buffer))):
+            running_add = running_add * self.gamma + self.reward_buffer[t]
+            discounted_reward_buffer[t] = running_add
 
         # normalize episode rewards
-        discounted_ep_rs -= np.mean(discounted_ep_rs)
-        discounted_ep_rs /= np.std(discounted_ep_rs)
-        return discounted_ep_rs
+        discounted_reward_buffer -= np.mean(discounted_reward_buffer)
+        discounted_reward_buffer /= np.std(discounted_reward_buffer)
+        return discounted_reward_buffer
 
-    def save_ckpt(self):
+    def save(self):
         """
         save trained weights
         :return: None
         """
-        if not os.path.exists('model'):
-            os.makedirs('model')
-        tl.files.save_weights_to_hdf5('model/pg_policy.hdf5', self.model)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'pg_policy.hdf5'), self.model)
 
-    def load_ckpt(self):
+    def load(self):
         """
         load trained weights
         :return: None
         """
-        tl.files.load_hdf5_to_weights_in_order('model/pg_policy.hdf5', self.model)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'pg_policy.hdf5'), self.model)
 
 
 if __name__ == '__main__':
+    env = gym.make(ENV_ID).unwrapped
 
     # reproducible
-    np.random.seed(RANDOMSEED)
-    tf.random.set_seed(RANDOMSEED)
-
-    tl.logging.set_verbosity(tl.logging.DEBUG)
-
-    env = gym.make(ENV_NAME)
-    env.seed(RANDOMSEED)  # reproducible, general Policy gradient has high variance
-    env = env.unwrapped
-
-    print(env.action_space)
-    print(env.observation_space)
-    print(env.observation_space.high)
-    print(env.observation_space.low)
-
-    RL = PolicyGradient(
-        n_actions=env.action_space.n,
-        n_features=env.observation_space.shape[0],
-        learning_rate=0.02,
-        reward_decay=0.99,
-        # output_graph=True,
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
+    env.seed(RANDOM_SEED)
+
+    agent = PolicyGradient(
+        action_num=env.action_space.n,
+        state_dim=env.observation_space.shape[0],
     )
 
-    if args.train:
-        reward_buffer = []
+    t0 = time.time()
 
-        for i_episode in range(num_episodes):
+    if args.train:
+        all_episode_reward = []
+        for episode in range(TRAIN_EPISODES):
 
-            episode_time = time.time()
-            observation = env.reset()
+            state = env.reset()
+            episode_reward = 0
 
-            while True:
+            for step in range(MAX_STEPS):  # in one episode
                 if RENDER:
                     env.render()
 
-                action = RL.choose_action(observation)
-
-                observation_, reward, done, info = env.step(action)
-
-                RL.store_transition(observation, action, reward)
-
+                action = agent.get_action(state)
+                next_state, reward, done, info = env.step(action)
+                agent.store_transition(state, action, reward)
+                state = next_state
+                episode_reward += reward
                 if done:
-                    ep_rs_sum = sum(RL.ep_rs)
-
-                    if 'running_reward' not in globals():
-                        running_reward = ep_rs_sum
-                    else:
-                        running_reward = running_reward * 0.99 + ep_rs_sum * 0.01
-
-                    if running_reward > DISPLAY_REWARD_THRESHOLD:
-                        RENDER = True  # rendering
-
-                    # print("episode:", i_episode, "  reward:", int(running_reward))
-
-                    print(
-                        "Episode [%d/%d] \tsum reward: %d  \trunning reward: %f \ttook: %.5fs " %
-                        (i_episode, num_episodes, ep_rs_sum, running_reward, time.time() - episode_time)
-                    )
-                    reward_buffer.append(running_reward)
-
-                    vt = RL.learn()
-
-                    plt.ion()
-                    plt.cla()
-                    plt.title('PG')
-                    plt.plot(reward_buffer, )  # plot the episode vt
-                    plt.xlabel('episode steps')
-                    plt.ylabel('normalized state-action value')
-                    plt.show()
-                    plt.pause(0.1)
-
                     break
-
-                observation = observation_
-        RL.save_ckpt()
-        plt.ioff()
-        plt.show()
-
-    # test
-    RL.load_ckpt()
-    observation = env.reset()
-    while True:
-        env.render()
-        action = RL.choose_action(observation)
-        observation, reward, done, info = env.step(action)
-        if done:
-            observation = env.reset()
+            agent.learn()
+            print(
+                'Training  | Episode: {}/{}  | Episode Reward: {:.0f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
+
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+
+        agent.save()
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
+
+    if args.test:
+        # test
+        agent.load()
+        for episode in range(TEST_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                env.render()
+                state, reward, done, info = env.step(agent.get_action(state, True))
+                episode_reward += reward
+                if done:
+                    break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.0f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_PPO.py b/examples/reinforcement_learning/tutorial_PPO.py
index b20d03196..82d20d2e3 100644
--- a/examples/reinforcement_learning/tutorial_PPO.py
+++ b/examples/reinforcement_learning/tutorial_PPO.py
@@ -1,333 +1,322 @@
-"""
-Proximal Policy Optimization (PPO)
-----------------------------
-A simple version of Proximal Policy Optimization (PPO) using single thread.
-PPO is a family of first-order methods that use a few other tricks to keep new policies close to old.
-PPO methods are significantly simpler to implement, and empirically seem to perform at least as well as TRPO.
-
-Reference
----------
-Proximal Policy Optimization Algorithms, Schulman et al. 2017
-High Dimensional Continuous Control Using Generalized Advantage Estimation, Schulman et al. 2016
-Emergence of Locomotion Behaviours in Rich Environments, Heess et al. 2017
-MorvanZhou's tutorial page: https://morvanzhou.github.io/tutorials
-
-Environment
------------
-Openai Gym Pendulum-v0, continual action space
-
-Prerequisites
---------------
-tensorflow >=2.0.0a0
-tensorflow-probability 0.6.0
-tensorlayer >=2.0.0
-
-To run
-------
-python tutorial_PPO.py --train/test
-
-"""
-import argparse
-import os
-import time
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-import gym
-import tensorflow as tf
-import tensorflow_probability as tfp
-import tensorlayer as tl
-
-parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=True)
-parser.add_argument('--test', dest='train', action='store_false')
-args = parser.parse_args()
-
-#####################  hyper parameters  ####################
-
-ENV_NAME = 'Pendulum-v0'  # environment name
-RANDOMSEED = 1  # random seed
-
-EP_MAX = 1000  # total number of episodes for training
-EP_LEN = 200  # total number of steps for each episode
-GAMMA = 0.9  # reward discount
-A_LR = 0.0001  # learning rate for actor
-C_LR = 0.0002  # learning rate for critic
-BATCH = 32  # update batchsize
-A_UPDATE_STEPS = 10  # actor update steps
-C_UPDATE_STEPS = 10  # critic update steps
-S_DIM, A_DIM = 3, 1  # state dimension, action dimension
-EPS = 1e-8  # epsilon
-METHOD = [
-    dict(name='kl_pen', kl_target=0.01, lam=0.5),  # KL penalty
-    dict(name='clip', epsilon=0.2),  # Clipped surrogate objective, find this is better
-][1]  # choose the method for optimization
-
-###############################  PPO  ####################################
-
-
-class PPO(object):
-    '''
-    PPO class
-    '''
-
-    def __init__(self):
-
-        # critic
-        tfs = tl.layers.Input([None, S_DIM], tf.float32, 'state')
-        l1 = tl.layers.Dense(100, tf.nn.relu)(tfs)
-        v = tl.layers.Dense(1)(l1)
-        self.critic = tl.models.Model(tfs, v)
-        self.critic.train()
-
-        # actor
-        self.actor = self._build_anet('pi', trainable=True)
-        self.actor_old = self._build_anet('oldpi', trainable=False)
-        self.actor_opt = tf.optimizers.Adam(A_LR)
-        self.critic_opt = tf.optimizers.Adam(C_LR)
-
-    def a_train(self, tfs, tfa, tfadv):
-        '''
-        Update policy network
-        :param tfs: state
-        :param tfa: act
-        :param tfadv: advantage
-        :return:
-        '''
-        tfs = np.array(tfs, np.float32)
-        tfa = np.array(tfa, np.float32)
-        tfadv = np.array(tfadv, np.float32)
-        with tf.GradientTape() as tape:
-            mu, sigma = self.actor(tfs)
-            pi = tfp.distributions.Normal(mu, sigma)
-
-            mu_old, sigma_old = self.actor_old(tfs)
-            oldpi = tfp.distributions.Normal(mu_old, sigma_old)
-
-            # ratio = tf.exp(pi.log_prob(self.tfa) - oldpi.log_prob(self.tfa))
-            ratio = pi.prob(tfa) / (oldpi.prob(tfa) + EPS)
-            surr = ratio * tfadv
-            if METHOD['name'] == 'kl_pen':
-                tflam = METHOD['lam']
-                kl = tfp.distributions.kl_divergence(oldpi, pi)
-                kl_mean = tf.reduce_mean(kl)
-                aloss = -(tf.reduce_mean(surr - tflam * kl))
-            else:  # clipping method, find this is better
-                aloss = -tf.reduce_mean(
-                    tf.minimum(surr,
-                               tf.clip_by_value(ratio, 1. - METHOD['epsilon'], 1. + METHOD['epsilon']) * tfadv)
-                )
-        a_gard = tape.gradient(aloss, self.actor.trainable_weights)
-
-        self.actor_opt.apply_gradients(zip(a_gard, self.actor.trainable_weights))
-
-        if METHOD['name'] == 'kl_pen':
-            return kl_mean
-
-    def update_old_pi(self):
-        '''
-        Update old policy parameter
-        :return: None
-        '''
-        for p, oldp in zip(self.actor.trainable_weights, self.actor_old.trainable_weights):
-            oldp.assign(p)
-
-    def c_train(self, tfdc_r, s):
-        '''
-        Update actor network
-        :param tfdc_r: cumulative reward
-        :param s: state
-        :return: None
-        '''
-        tfdc_r = np.array(tfdc_r, dtype=np.float32)
-        with tf.GradientTape() as tape:
-            v = self.critic(s)
-            advantage = tfdc_r - v
-            closs = tf.reduce_mean(tf.square(advantage))
-        # print('tfdc_r value', tfdc_r)
-        grad = tape.gradient(closs, self.critic.trainable_weights)
-        self.critic_opt.apply_gradients(zip(grad, self.critic.trainable_weights))
-
-    def cal_adv(self, tfs, tfdc_r):
-        '''
-        Calculate advantage
-        :param tfs: state
-        :param tfdc_r: cumulative reward
-        :return: advantage
-        '''
-        tfdc_r = np.array(tfdc_r, dtype=np.float32)
-        advantage = tfdc_r - self.critic(tfs)
-        return advantage.numpy()
-
-    def update(self, s, a, r):
-        '''
-        Update parameter with the constraint of KL divergent
-        :param s: state
-        :param a: act
-        :param r: reward
-        :return: None
-        '''
-        s, a, r = s.astype(np.float32), a.astype(np.float32), r.astype(np.float32)
-
-        self.update_old_pi()
-        adv = self.cal_adv(s, r)
-        # adv = (adv - adv.mean())/(adv.std()+1e-6)  # sometimes helpful
-
-        # update actor
-        if METHOD['name'] == 'kl_pen':
-            for _ in range(A_UPDATE_STEPS):
-                kl = self.a_train(s, a, adv)
-                if kl > 4 * METHOD['kl_target']:  # this in in google's paper
-                    break
-            if kl < METHOD['kl_target'] / 1.5:  # adaptive lambda, this is in OpenAI's paper
-                METHOD['lam'] /= 2
-            elif kl > METHOD['kl_target'] * 1.5:
-                METHOD['lam'] *= 2
-            METHOD['lam'] = np.clip(
-                METHOD['lam'], 1e-4, 10
-            )  # sometimes explode, this clipping is MorvanZhou's solution
-        else:  # clipping method, find this is better (OpenAI's paper)
-            for _ in range(A_UPDATE_STEPS):
-                self.a_train(s, a, adv)
-
-        # update critic
-        for _ in range(C_UPDATE_STEPS):
-            self.c_train(r, s)
-
-    def _build_anet(self, name, trainable):
-        '''
-        Build policy network
-        :param name: name
-        :param trainable: trainable flag
-        :return: policy network
-        '''
-        tfs = tl.layers.Input([None, S_DIM], tf.float32, name + '_state')
-        l1 = tl.layers.Dense(100, tf.nn.relu, name=name + '_l1')(tfs)
-        a = tl.layers.Dense(A_DIM, tf.nn.tanh, name=name + '_a')(l1)
-        mu = tl.layers.Lambda(lambda x: x * 2, name=name + '_lambda')(a)
-        sigma = tl.layers.Dense(A_DIM, tf.nn.softplus, name=name + '_sigma')(l1)
-        model = tl.models.Model(tfs, [mu, sigma], name)
-
-        if trainable:
-            model.train()
-        else:
-            model.eval()
-        return model
-
-    def choose_action(self, s):
-        '''
-        Choose action
-        :param s: state
-        :return: clipped act
-        '''
-        s = s[np.newaxis, :].astype(np.float32)
-        mu, sigma = self.actor(s)
-        pi = tfp.distributions.Normal(mu, sigma)
-        a = tf.squeeze(pi.sample(1), axis=0)[0]  # choosing action
-        return np.clip(a, -2, 2)
-
-    def get_v(self, s):
-        '''
-        Compute value
-        :param s: state
-        :return: value
-        '''
-        s = s.astype(np.float32)
-        if s.ndim < 2: s = s[np.newaxis, :]
-        return self.critic(s)[0, 0]
-
-    def save_ckpt(self):
-        """
-        save trained weights
-        :return: None
-        """
-        if not os.path.exists('model'):
-            os.makedirs('model')
-        tl.files.save_weights_to_hdf5('model/ppo_actor.hdf5', self.actor)
-        tl.files.save_weights_to_hdf5('model/ppo_actor_old.hdf5', self.actor_old)
-        tl.files.save_weights_to_hdf5('model/ppo_critic.hdf5', self.critic)
-
-    def load_ckpt(self):
-        """
-        load trained weights
-        :return: None
-        """
-        tl.files.load_hdf5_to_weights_in_order('model/ppo_actor.hdf5', self.actor)
-        tl.files.load_hdf5_to_weights_in_order('model/ppo_actor_old.hdf5', self.actor_old)
-        tl.files.load_hdf5_to_weights_in_order('model/ppo_critic.hdf5', self.critic)
-
-
-if __name__ == '__main__':
-
-    env = gym.make(ENV_NAME).unwrapped
-
-    # reproducible
-    env.seed(RANDOMSEED)
-    np.random.seed(RANDOMSEED)
-    tf.random.set_seed(RANDOMSEED)
-
-    ppo = PPO()
-
-    if args.train:
-        all_ep_r = []
-        for ep in range(EP_MAX):
-            s = env.reset()
-            buffer_s, buffer_a, buffer_r = [], [], []
-            ep_r = 0
-            t0 = time.time()
-            for t in range(EP_LEN):  # in one episode
-                # env.render()
-                a = ppo.choose_action(s)
-                s_, r, done, _ = env.step(a)
-                buffer_s.append(s)
-                buffer_a.append(a)
-                buffer_r.append((r + 8) / 8)  # normalize reward, find to be useful
-                s = s_
-                ep_r += r
-
-                # update ppo
-                if (t + 1) % BATCH == 0 or t == EP_LEN - 1:
-                    v_s_ = ppo.get_v(s_)
-                    discounted_r = []
-                    for r in buffer_r[::-1]:
-                        v_s_ = r + GAMMA * v_s_
-                        discounted_r.append(v_s_)
-                    discounted_r.reverse()
-
-                    bs, ba, br = np.vstack(buffer_s), np.vstack(buffer_a), np.array(discounted_r)[:, np.newaxis]
-                    buffer_s, buffer_a, buffer_r = [], [], []
-                    ppo.update(bs, ba, br)
-            if ep == 0:
-                all_ep_r.append(ep_r)
-            else:
-                all_ep_r.append(all_ep_r[-1] * 0.9 + ep_r * 0.1)
-            print(
-                'Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                    ep, EP_MAX, ep_r,
-                    time.time() - t0
-                )
-            )
-
-            plt.ion()
-            plt.cla()
-            plt.title('PPO')
-            plt.plot(np.arange(len(all_ep_r)), all_ep_r)
-            plt.ylim(-2000, 0)
-            plt.xlabel('Episode')
-            plt.ylabel('Moving averaged episode reward')
-            plt.show()
-            plt.pause(0.1)
-        ppo.save_ckpt()
-        plt.ioff()
-        plt.show()
-
-    # test
-    ppo.load_ckpt()
-    while True:
-        s = env.reset()
-        for i in range(EP_LEN):
-            env.render()
-            s, r, done, _ = env.step(ppo.choose_action(s))
-            if done:
-                break
+"""
+Proximal Policy Optimization (PPO)
+----------------------------
+A simple version of Proximal Policy Optimization (PPO) using single thread.
+PPO is a family of first-order methods that use a few other tricks to keep new policies close to old.
+PPO methods are significantly simpler to implement, and empirically seem to perform at least as well as TRPO.
+Reference
+---------
+Proximal Policy Optimization Algorithms, Schulman et al. 2017
+High Dimensional Continuous Control Using Generalized Advantage Estimation, Schulman et al. 2016
+Emergence of Locomotion Behaviours in Rich Environments, Heess et al. 2017
+MorvanZhou's tutorial page: https://morvanzhou.github.io/tutorials
+Environment
+-----------
+Openai Gym Pendulum-v0, continual action space
+Prerequisites
+--------------
+tensorflow >=2.0.0a0
+tensorflow-probability 0.6.0
+tensorlayer >=2.0.0
+To run
+------
+python tutorial_PPO.py --train/test
+"""
+import argparse
+import os
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+
+import tensorlayer as tl
+
+parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+args = parser.parse_args()
+
+#####################  hyper parameters  ####################
+
+ENV_ID = 'Pendulum-v0'  # environment id
+RANDOM_SEED = 1  # random seed
+RENDER = False  # render while training
+
+ALG_NAME = 'PPO'
+TRAIN_EPISODES = 1000  # total number of episodes for training
+TEST_EPISODES = 10  # total number of episodes for testing
+MAX_STEPS = 200  # total number of steps for each episode
+GAMMA = 0.9  # reward discount
+LR_A = 0.0001  # learning rate for actor
+LR_C = 0.0002  # learning rate for critic
+BATCH_SIZE = 32  # update batch size
+ACTOR_UPDATE_STEPS = 10  # actor update steps
+CRITIC_UPDATE_STEPS = 10  # critic update steps
+
+# ppo-penalty parameters
+KL_TARGET = 0.01
+LAM = 0.5
+
+# ppo-clip parameters
+EPSILON = 0.2
+
+
+###############################  PPO  ####################################
+
+
+class PPO(object):
+    """
+    PPO class
+    """
+    def __init__(self, state_dim, action_dim, action_bound, method='clip'):
+        # critic
+        with tf.name_scope('critic'):
+            inputs = tl.layers.Input([None, state_dim], tf.float32, 'state')
+            layer = tl.layers.Dense(64, tf.nn.relu)(inputs)
+            layer = tl.layers.Dense(64, tf.nn.relu)(layer)
+            v = tl.layers.Dense(1)(layer)
+        self.critic = tl.models.Model(inputs, v)
+        self.critic.train()
+
+        # actor
+        with tf.name_scope('actor'):
+            inputs = tl.layers.Input([None, state_dim], tf.float32, 'state')
+            layer = tl.layers.Dense(64, tf.nn.relu)(inputs)
+            layer = tl.layers.Dense(64, tf.nn.relu)(layer)
+            a = tl.layers.Dense(action_dim, tf.nn.tanh)(layer)
+            mean = tl.layers.Lambda(lambda x: x * action_bound, name='lambda')(a)
+            logstd = tf.Variable(np.zeros(action_dim, dtype=np.float32))
+        self.actor = tl.models.Model(inputs, mean)
+        self.actor.trainable_weights.append(logstd)
+        self.actor.logstd = logstd
+        self.actor.train()
+
+        self.actor_opt = tf.optimizers.Adam(LR_A)
+        self.critic_opt = tf.optimizers.Adam(LR_C)
+
+        self.method = method
+        if method == 'penalty':
+            self.kl_target = KL_TARGET
+            self.lam = LAM
+        elif method == 'clip':
+            self.epsilon = EPSILON
+
+        self.state_buffer, self.action_buffer = [], []
+        self.reward_buffer, self.cumulative_reward_buffer = [], []
+        self.action_bound = action_bound
+
+    def train_actor(self, state, action, adv, old_pi):
+        """
+        Update policy network
+        :param state: state batch
+        :param action: action batch
+        :param adv: advantage batch
+        :param old_pi: old pi distribution
+        :return: kl_mean or None
+        """
+        with tf.GradientTape() as tape:
+            mean, std = self.actor(state), tf.exp(self.actor.logstd)
+            pi = tfp.distributions.Normal(mean, std)
+
+            ratio = tf.exp(pi.log_prob(action) - old_pi.log_prob(action))
+            surr = ratio * adv
+            if self.method == 'penalty':  # ppo penalty
+                kl = tfp.distributions.kl_divergence(old_pi, pi)
+                kl_mean = tf.reduce_mean(kl)
+                loss = -(tf.reduce_mean(surr - self.lam * kl))
+            else:  # ppo clip
+                loss = -tf.reduce_mean(
+                    tf.minimum(surr,
+                               tf.clip_by_value(ratio, 1. - self.epsilon, 1. + self.epsilon) * adv)
+                )
+        a_gard = tape.gradient(loss, self.actor.trainable_weights)
+        self.actor_opt.apply_gradients(zip(a_gard, self.actor.trainable_weights))
+
+        if self.method == 'kl_pen':
+            return kl_mean
+
+    def train_critic(self, reward, state):
+        """
+        Update actor network
+        :param reward: cumulative reward batch
+        :param state: state batch
+        :return: None
+        """
+        reward = np.array(reward, dtype=np.float32)
+        with tf.GradientTape() as tape:
+            advantage = reward - self.critic(state)
+            loss = tf.reduce_mean(tf.square(advantage))
+        grad = tape.gradient(loss, self.critic.trainable_weights)
+        self.critic_opt.apply_gradients(zip(grad, self.critic.trainable_weights))
+
+    def update(self):
+        """
+        Update parameter with the constraint of KL divergent
+        :return: None
+        """
+        s = np.array(self.state_buffer, np.float32)
+        a = np.array(self.action_buffer, np.float32)
+        r = np.array(self.cumulative_reward_buffer, np.float32)
+        mean, std = self.actor(s), tf.exp(self.actor.logstd)
+        pi = tfp.distributions.Normal(mean, std)
+        adv = r - self.critic(s)
+
+        # update actor
+        if self.method == 'kl_pen':
+            for _ in range(ACTOR_UPDATE_STEPS):
+                kl = self.train_actor(s, a, adv, pi)
+            if kl < self.kl_target / 1.5:
+                self.lam /= 2
+            elif kl > self.kl_target * 1.5:
+                self.lam *= 2
+        else:
+            for _ in range(ACTOR_UPDATE_STEPS):
+                self.train_actor(s, a, adv, pi)
+
+        # update critic
+        for _ in range(CRITIC_UPDATE_STEPS):
+            self.train_critic(r, s)
+
+        self.state_buffer.clear()
+        self.action_buffer.clear()
+        self.cumulative_reward_buffer.clear()
+        self.reward_buffer.clear()
+
+    def get_action(self, state, greedy=False):
+        """
+        Choose action
+        :param state: state
+        :param greedy: choose action greedy or not
+        :return: clipped action
+        """
+        state = state[np.newaxis, :].astype(np.float32)
+        mean, std = self.actor(state), tf.exp(self.actor.logstd)
+        if greedy:
+            action = mean[0]
+        else:
+            pi = tfp.distributions.Normal(mean, std)
+            action = tf.squeeze(pi.sample(1), axis=0)[0]  # choosing action
+        return np.clip(action, -self.action_bound, self.action_bound)
+
+    def save(self):
+        """
+        save trained weights
+        :return: None
+        """
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'critic.hdf5'), self.critic)
+
+    def load(self):
+        """
+        load trained weights
+        :return: None
+        """
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'critic.hdf5'), self.critic)
+
+    def store_transition(self, state, action, reward):
+        """
+        Store state, action, reward at each step
+        :param state:
+        :param action:
+        :param reward:
+        :return: None
+        """
+        self.state_buffer.append(state)
+        self.action_buffer.append(action)
+        self.reward_buffer.append(reward)
+
+    def finish_path(self, next_state, done):
+        """
+        Calculate cumulative reward
+        :param next_state:
+        :return: None
+        """
+        if done:
+            v_s_ = 0
+        else:
+            v_s_ = self.critic(np.array([next_state], np.float32))[0, 0]
+        discounted_r = []
+        for r in self.reward_buffer[::-1]:
+            v_s_ = r + GAMMA * v_s_
+            discounted_r.append(v_s_)
+        discounted_r.reverse()
+        discounted_r = np.array(discounted_r)[:, np.newaxis]
+        self.cumulative_reward_buffer.extend(discounted_r)
+        self.reward_buffer.clear()
+
+
+if __name__ == '__main__':
+    env = gym.make(ENV_ID).unwrapped
+
+    # reproducible
+    env.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
+
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_bound = env.action_space.high
+
+    agent = PPO(state_dim, action_dim, action_bound)
+
+    t0 = time.time()
+    if args.train:
+        all_episode_reward = []
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):  # in one episode
+                if RENDER:
+                    env.render()
+                action = agent.get_action(state)
+                state_, reward, done, info = env.step(action)
+                agent.store_transition(state, action, reward)
+                state = state_
+                episode_reward += reward
+
+                # update ppo
+                if len(agent.state_buffer) >= BATCH_SIZE:
+                    agent.finish_path(state_, done)
+                    agent.update()
+                if done:
+                    break
+            agent.finish_path(state_, done)
+            print(
+                'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward, time.time() - t0)
+            )
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+        agent.save()
+
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
+
+    if args.test:
+        # test
+        agent.load()
+        for episode in range(TEST_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                env.render()
+                state, reward, done, info = env.step(agent.get_action(state, greedy=True))
+                episode_reward += reward
+                if done:
+                    break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - t0))
diff --git a/examples/reinforcement_learning/tutorial_Qlearning.py b/examples/reinforcement_learning/tutorial_Qlearning.py
index a8decb273..b2d553403 100644
--- a/examples/reinforcement_learning/tutorial_Qlearning.py
+++ b/examples/reinforcement_learning/tutorial_Qlearning.py
@@ -1,61 +1,113 @@
-"""Q-Table learning algorithm.
-
-Non deep learning - TD Learning, Off-Policy, e-Greedy Exploration
-
-Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
-
-See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
-
-For Q-Network, see tutorial_frozenlake_q_network.py
-
-EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
-CN: https://zhuanlan.zhihu.com/p/25710327
-
-tensorflow==2.0.0a0
-tensorlayer==2.0.0
-
-"""
-
-import time
-
-import numpy as np
-
-import gym
-
-## Load the environment
-env = gym.make('FrozenLake-v0')
-render = False  # display the game environment
-running_reward = None
-
-##================= Implement Q-Table learning algorithm =====================##
-## Initialize table with all zeros
-Q = np.zeros([env.observation_space.n, env.action_space.n])
-## Set learning parameters
-lr = .85  # alpha, if use value function approximation, we can ignore it
-lambd = .99  # decay factor
-num_episodes = 10000
-rList = []  # rewards for each episode
-for i in range(num_episodes):
-    ## Reset environment and get first new observation
-    episode_time = time.time()
-    s = env.reset()
-    rAll = 0
-    ## The Q-Table learning algorithm
-    for j in range(99):
-        if render: env.render()
-        ## Choose an action by greedily (with noise) picking from Q table
-        a = np.argmax(Q[s, :] + np.random.randn(1, env.action_space.n) * (1. / (i + 1)))
-        ## Get new state and reward from environment
-        s1, r, d, _ = env.step(a)
-        ## Update Q-Table with new knowledge
-        Q[s, a] = Q[s, a] + lr * (r + lambd * np.max(Q[s1, :]) - Q[s, a])
-        rAll += r
-        s = s1
-        if d ==True:
-            break
-    rList.append(rAll)
-    running_reward = r if running_reward is None else running_reward * 0.99 + r * 0.01
-    print("Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs " % \
-        (i, num_episodes, rAll, running_reward, time.time() - episode_time))
-
-print("Final Q-Table Values:/n %s" % Q)
+"""Q-Table learning algorithm.
+Non deep learning - TD Learning, Off-Policy, e-Greedy Exploration
+Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
+See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
+For Q-Network, see tutorial_frozenlake_q_network.py
+EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
+CN: https://zhuanlan.zhihu.com/p/25710327
+tensorflow==2.0.0a0
+tensorlayer==2.0.0
+"""
+
+import argparse
+import os
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--train', dest='train', action='store_true', default=True)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+
+parser.add_argument(
+    '--save_path', default=None, help='folder to save if mode == train else model path,'
+    'qnet will be saved once target net update'
+)
+parser.add_argument('--seed', help='random seed', type=int, default=0)
+parser.add_argument('--env_id', default='FrozenLake-v0')
+args = parser.parse_args()
+
+## Load the environment
+alg_name = 'Qlearning'
+env_id = args.env_id
+env = gym.make(env_id)
+render = False  # display the game environment
+
+##================= Implement Q-Table learning algorithm =====================##
+## Initialize table with all zeros
+Q = np.zeros([env.observation_space.n, env.action_space.n])
+## Set learning parameters
+lr = .85  # alpha, if use value function approximation, we can ignore it
+lambd = .99  # decay factor
+num_episodes = 10000
+t0 = time.time()
+
+if args.train:
+    all_episode_reward = []
+    for i in range(num_episodes):
+        ## Reset environment and get first new observation
+        s = env.reset()
+        rAll = 0
+        ## The Q-Table learning algorithm
+        for j in range(99):
+            if render: env.render()
+            ## Choose an action by greedily (with noise) picking from Q table
+            a = np.argmax(Q[s, :] + np.random.randn(1, env.action_space.n) * (1. / (i + 1)))
+            ## Get new state and reward from environment
+            s1, r, d, _ = env.step(a)
+            ## Update Q-Table with new knowledge
+            Q[s, a] = Q[s, a] + lr * (r + lambd * np.max(Q[s1, :]) - Q[s, a])
+            rAll += r
+            s = s1
+            if d is True:
+                break
+        print(
+            'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                i + 1, num_episodes, rAll,
+                time.time() - t0
+            )
+        )
+        if i == 0:
+            all_episode_reward.append(rAll)
+        else:
+            all_episode_reward.append(all_episode_reward[-1] * 0.9 + rAll * 0.1)
+
+    # save
+    path = os.path.join('model', '_'.join([alg_name, env_id]))
+    if not os.path.exists(path):
+        os.makedirs(path)
+    np.save(os.path.join(path, 'Q_table.npy'), Q)
+
+    plt.plot(all_episode_reward)
+    if not os.path.exists('image'):
+        os.makedirs('image')
+    plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
+
+    # print("Final Q-Table Values:/n %s" % Q)
+
+if args.test:
+    path = os.path.join('model', '_'.join([alg_name, env_id]))
+    Q = np.load(os.path.join(path, 'Q_table.npy'))
+    for i in range(num_episodes):
+        ## Reset environment and get first new observation
+        s = env.reset()
+        rAll = 0
+        ## The Q-Table learning algorithm
+        for j in range(99):
+            ## Choose an action by greedily (with noise) picking from Q table
+            a = np.argmax(Q[s, :])
+            ## Get new state and reward from environment
+            s1, r, d, _ = env.step(a)
+            ## Update Q-Table with new knowledge
+            rAll += r
+            s = s1
+            if d is True:
+                break
+        print(
+            'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                i + 1, num_episodes, rAll,
+                time.time() - t0
+            )
+        )
diff --git a/examples/reinforcement_learning/tutorial_Retrace.py b/examples/reinforcement_learning/tutorial_Retrace.py
deleted file mode 100644
index e1e03cf1d..000000000
--- a/examples/reinforcement_learning/tutorial_Retrace.py
+++ /dev/null
@@ -1,279 +0,0 @@
-"""
-Retrace(\lambda) algorithm
-------------------------
-Retrace(\lambda) is an off-policy algorithm that extend the idea of eligibility
-trace. It apply an importance sampling ratio truncated at 1 to several behaviour
-policies, which suffer from the variance explosion of standard IS and lead to
-safe and efficient learning.
-
-
-Reference:
-------------------------
-Munos R, Stepleton T, Harutyunyan A, et al. Safe and efficient off-policy
-reinforcement learning[C]//Advances in Neural Information Processing Systems.
-2016: 1054-1062.
-
-
-Environment:
-------------------------
-Cartpole and Pong in OpenAI Gym
-
-
-Requirements:
-------------------------
-tensorflow>=2.0.0a0
-tensorlayer>=2.0.0
-
-
-To run:
-------------------------
-python tutorial_Retrace.py --mode=train
-python tutorial_Retrace.py --mode=test --save_path=retrace/8000.npz
-"""
-import argparse
-import os
-import random
-import time
-
-import numpy as np
-
-import tensorflow as tf
-import tensorlayer as tl
-from tutorial_wrappers import build_env
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--mode', help='train or test', default='train')
-parser.add_argument(
-    '--save_path', default='retrace', help='folder to save if mode == train else model path,'
-    'qnet will be saved once target net update'
-)
-parser.add_argument('--seed', help='random seed', type=int, default=0)
-parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
-args = parser.parse_args()
-
-if args.mode == 'train':
-    os.makedirs(args.save_path, exist_ok=True)
-random.seed(args.seed)
-np.random.seed(args.seed)
-tf.random.set_seed(args.seed)  # reproducible
-env_id = args.env_id
-env = build_env(env_id, seed=args.seed)
-
-# ####################  hyper parameters  ####################
-if env_id == 'CartPole-v0':
-    qnet_type = 'MLP'
-    number_timesteps = 10000  # total number of time steps to train on
-    lr = 5e-3  # learning rate
-    buffer_size = 1000  # replay buffer size
-    target_q_update_freq = 50  # how frequency target q net update
-    ob_scale = 1.0  # scale observations
-else:
-    # reward will increase obviously after 1e5 time steps
-    qnet_type = 'CNN'
-    number_timesteps = int(1e6)  # total number of time steps to train on
-    lr = 1e-4  # learning rate
-    buffer_size = 10000  # replay buffer size
-    target_q_update_freq = 200  # how frequency target q net update
-    ob_scale = 1.0 / 255  # scale observations
-
-in_dim = env.observation_space.shape
-out_dim = env.action_space.n
-reward_gamma = 0.99  # reward discount
-batch_size = 32  # batch size for sampling from replay buffer
-warm_start = buffer_size / 10  # sample times befor learning
-retrace_lambda = 1.0
-
-
-# ##############################  Retrace  ####################################
-class MLP(tl.models.Model):
-
-    def __init__(self, name):
-        super(MLP, self).__init__(name=name)
-        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
-
-    def forward(self, ni):
-        feature = self.h1(ni)
-        qvalue = self.qvalue(feature)
-        return qvalue, tf.nn.softmax(qvalue, 1)
-
-
-class CNN(tl.models.Model):
-
-    def __init__(self, name):
-        super(CNN, self).__init__(name=name)
-        h, w, in_channels = in_dim
-        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
-        self.conv1 = tl.layers.Conv2d(
-            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv2 = tl.layers.Conv2d(
-            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv3 = tl.layers.Conv2d(
-            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.flatten = tl.layers.Flatten(name='flatten')
-        self.preq = tl.layers.Dense(
-            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
-
-    def forward(self, ni):
-        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
-        qvalue = self.qvalue(self.preq(feature))
-        return qvalue, tf.nn.softmax(qvalue, 1)
-
-
-class ReplayBuffer(object):
-
-    def __init__(self, size):
-        self._storage = []
-        self._maxsize = size
-        self._next_idx = 0
-
-    def __len__(self):
-        return len(self._storage)
-
-    def add(self, *args):
-        if self._next_idx >= len(self._storage):
-            self._storage.append(args)
-        else:
-            self._storage[self._next_idx] = args
-        self._next_idx = (self._next_idx + 1) % self._maxsize
-
-    def _encode_sample(self, idxes):
-        b_o, b_a, b_r, b_o_, b_d, b_pi = [], [], [], [], [], []
-        for i in idxes:
-            o, a, r, o_, d, pi = self._storage[i]
-            b_o.append(o)
-            b_a.append(a)
-            b_r.append(r)
-            b_o_.append(o_)
-            b_d.append(d)
-            b_pi.append(pi)
-        return (
-            np.stack(b_o).astype('float32') * ob_scale, np.stack(b_a).astype('int32'), np.stack(b_r).astype('float32'),
-            np.stack(b_o_).astype('float32') * ob_scale, np.stack(b_d).astype('float32'),
-            np.stack(b_pi).astype('float32')
-        )
-
-    def sample(self, batch_size):
-        indexes = range(len(self._storage))
-        idxes = [random.choice(indexes) for _ in range(batch_size)]
-        return self._encode_sample(idxes)
-
-
-def huber_loss(x):
-    """Loss function for value"""
-    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
-
-
-def sync(net, net_tar):
-    """Copy q network to target q network"""
-    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
-        var_tar.assign(var)
-
-
-if __name__ == '__main__':
-    if args.mode == 'train':
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        qnet.train()
-        trainabel_weights = qnet.trainable_weights
-        targetqnet = MLP('targetq') if qnet_type == 'MLP' else CNN('targetq')
-        targetqnet.infer()
-        sync(qnet, targetqnet)
-        optimizer = tf.optimizers.Adam(learning_rate=lr)
-        buffer = ReplayBuffer(buffer_size)
-
-        o = env.reset()
-        nepisode = 0
-        t = time.time()
-        for i in range(1, number_timesteps + 1):
-            # select action based on boltzmann exploration
-            obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-            qs, pi = qnet(obv)
-            a = np.random.multinomial(1, pi.numpy()[0]).argmax()
-            pi = pi.numpy()[0]
-
-            # execute action and feed to replay buffer
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-            buffer.add(o, a, r, o_, done, pi)
-
-            if i >= warm_start:
-                # sync q net and target q net
-                if i % target_q_update_freq == 0:
-                    sync(qnet, targetqnet)
-                    path = os.path.join(args.save_path, '{}.npz'.format(i))
-                    tl.files.save_npz(qnet.trainable_weights, name=path)
-
-                # sample from replay buffer
-                b_o, b_a, b_r, b_o_, b_d, b_old_pi = buffer.sample(batch_size)
-
-                # q estimation based on 1 step retrace(\lambda)
-                b_q_, b_pi_ = targetqnet(b_o_)
-                b_v_ = (b_q_ * b_pi_).numpy().sum(1)
-                b_q, b_pi = targetqnet(b_o)
-                b_q = tf.reduce_sum(b_q * tf.one_hot(b_a, out_dim), 1).numpy()
-                c = np.clip(b_pi.numpy() / (b_old_pi + 1e-8), None, 1)
-                c = c[range(batch_size), b_a]
-                td = b_r + reward_gamma * (1 - b_d) * b_v_ - b_q
-                q_target = c * td + b_q
-
-                # calculate loss
-                with tf.GradientTape() as q_tape:
-                    b_q, _ = qnet(b_o)
-                    b_q = tf.reduce_sum(b_q * tf.one_hot(b_a, out_dim), 1)
-                    loss = tf.reduce_mean(huber_loss(b_q - q_target))
-
-                # backward gradients
-                q_grad = q_tape.gradient(loss, trainabel_weights)
-                optimizer.apply_gradients(zip(q_grad, trainabel_weights))
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                fps = int(length / (time.time() - t))
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}, FPS: {}'.format(i, nepisode, reward, length, fps)
-                )
-                t = time.time()
-    else:
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        tl.files.load_and_assign_npz(name=args.save_path, network=qnet)
-        qnet.eval()
-
-        nepisode = 0
-        o = env.reset()
-        for i in range(1, number_timesteps + 1):
-            obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-            a = qnet(obv)[0].numpy().argmax(1)[0]
-
-            # execute action
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}'.format(i, nepisode, reward, length)
-                )
diff --git a/examples/reinforcement_learning/tutorial_SAC.py b/examples/reinforcement_learning/tutorial_SAC.py
index 24831e85f..58b089bb6 100644
--- a/examples/reinforcement_learning/tutorial_SAC.py
+++ b/examples/reinforcement_learning/tutorial_SAC.py
@@ -1,64 +1,50 @@
-''' 
+""" 
 Soft Actor-Critic (SAC)
 ------------------
 Actor policy in SAC is stochastic, with off-policy training. 
 And 'soft' in SAC indicates the trade-off between the entropy and expected return. 
 The additional consideration of entropy term helps with more explorative policy.
 And this implementation contains an automatic update for the entropy factor.
-
 This version of Soft Actor-Critic (SAC) implementation contains 5 networks: 
 2 Q net, 2 target Q net, 1 policy net.
 It uses alpha loss.
-
-
 Reference
 ---------
 paper: https://arxiv.org/pdf/1812.05905.pdf
-
 Environment
 ---
 Openai Gym Pendulum-v0, continuous action space
 https://gym.openai.com/envs/Pendulum-v0/
-
 Prerequisites
 --------------
 tensorflow >=2.0.0a0
 tensorflow-probability 0.6.0
 tensorlayer >=2.0.0
-
 &&
 pip install box2d box2d-kengz --user
-
 To run
 ------
 python tutorial_SAC.py --train/test
-'''
+"""
 
 import argparse
-import math
+import os
 import random
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
-from IPython.display import clear_output
-
-import gym
 import tensorflow as tf
+
 import tensorflow_probability as tfp
 import tensorlayer as tl
 from tensorlayer.layers import Dense
 from tensorlayer.models import Model
 
-tfd = tfp.distributions
-Normal = tfd.Normal
-
+Normal = tfp.distributions.Normal
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-random.seed(2)
-np.random.seed(2)
-tf.random.set_seed(2)  # reproducible
-
 # add arguments in command  --train/test
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
 parser.add_argument('--train', dest='train', action='store_true', default=False)
@@ -66,40 +52,42 @@
 args = parser.parse_args()
 
 #####################  hyper parameters  ####################
-# choose env
-ENV = 'Pendulum-v0'
-action_range = 1.  # scale action, [-action_range, action_range]
+
+ENV_ID = 'Pendulum-v0'  # environment id
+RANDOM_SEED = 2  # random seed
+RENDER = False  # render while training
 
 # RL training
-max_frames = 40000  # total number of steps for training
-test_frames = 300  # total number of steps for testing
-max_steps = 150  # maximum number of steps for one episode
-batch_size = 64  # udpate batchsize
-explore_steps = 100  # 500 for random action sampling in the beginning of training
-update_itr = 3  # repeated updates for single step
-hidden_dim = 32  # size of hidden layers for networks
-soft_q_lr = 3e-4  # q_net learning rate
-policy_lr = 3e-4  # policy_net learning rate
-alpha_lr = 3e-4  # alpha learning rate
-policy_target_update_interval = 3  # delayed update for the policy network and target networks
-reward_scale = 1.  # value range of reward
-replay_buffer_size = 5e5
-
-AUTO_ENTROPY = True  # automatically udpating variable alpha for entropy
-DETERMINISTIC = False  # stochastic action policy if False, otherwise deterministic
+ALG_NAME = 'SAC'
+TRAIN_EPISODES = 100  # total number of episodes for training
+TEST_EPISODES = 10  # total number of episodes for training
+MAX_STEPS = 200  # total number of steps for each episode
+EXPLORE_STEPS = 100  # 500 for random action sampling in the beginning of training
+
+BATCH_SIZE = 256  # update batch size
+HIDDEN_DIM = 32  # size of hidden layers for networks
+UPDATE_ITR = 3  # repeated updates for single step
+SOFT_Q_LR = 3e-4  # q_net learning rate
+POLICY_LR = 3e-4  # policy_net learning rate
+ALPHA_LR = 3e-4  # alpha learning rate
+POLICY_TARGET_UPDATE_INTERVAL = 3  # delayed update for the policy network and target networks
+REWARD_SCALE = 1.  # value range of reward
+REPLAY_BUFFER_SIZE = 5e5  # size of the replay buffer
+
+AUTO_ENTROPY = True  # automatically updating variable alpha for entropy
 
 ###############################  SAC  ####################################
 
 
 class ReplayBuffer:
-    '''
+    """
     a ring buffer for storing transitions and sampling for training
     :state: (state_dim,)
     :action: (action_dim,)
     :reward: (,), scalar
     :next_state: (state_dim,)
     :done: (,), scalar (0 and 1) or bool (True and False)
-    '''
+    """
 
     def __init__(self, capacity):
         self.capacity = capacity
@@ -112,45 +100,23 @@ def push(self, state, action, reward, next_state, done):
         self.buffer[self.position] = (state, action, reward, next_state, done)
         self.position = int((self.position + 1) % self.capacity)  # as a ring buffer
 
-    def sample(self, batch_size):
-        batch = random.sample(self.buffer, batch_size)
+    def sample(self, BATCH_SIZE):
+        batch = random.sample(self.buffer, BATCH_SIZE)
         state, action, reward, next_state, done = map(np.stack, zip(*batch))  # stack for each element
-        ''' 
+        """ 
         the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
         zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
         the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
         np.stack((1,2)) => array([1, 2])
-        '''
+        """
         return state, action, reward, next_state, done
 
     def __len__(self):
         return len(self.buffer)
 
 
-class NormalizedActions(gym.ActionWrapper):
-    ''' normalize the actions to be in reasonable range '''
-
-    def _action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = low + (action + 1.0) * 0.5 * (high - low)
-        action = np.clip(action, low, high)
-
-        return action
-
-    def _reverse_action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = 2 * (action - low) / (high - low) - 1
-        action = np.clip(action, low, high)
-
-        return action
-
-
 class SoftQNetwork(Model):
-    ''' the network for evaluate values of state-action pairs: Q(s,a) '''
+    """ the network for evaluate values of state-action pairs: Q(s,a) """
 
     def __init__(self, num_inputs, num_actions, hidden_dim, init_w=3e-3):
         super(SoftQNetwork, self).__init__()
@@ -172,7 +138,7 @@ def forward(self, input):
 
 
 class PolicyNetwork(Model):
-    ''' the network for generating non-determinstic (Gaussian distributed) action from the state input '''
+    """ the network for generating non-deterministic (Gaussian distributed) action from the state input """
 
     def __init__(
             self, num_inputs, num_actions, hidden_dim, action_range=1., init_w=3e-3, log_std_min=-20, log_std_max=2
@@ -189,10 +155,14 @@ def __init__(
         self.linear2 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy2')
         self.linear3 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy3')
 
-        self.mean_linear = Dense(n_units=num_actions, W_init=w_init, \
-        b_init=tf.random_uniform_initializer(-init_w, init_w), in_channels=hidden_dim, name='policy_mean')
-        self.log_std_linear = Dense(n_units=num_actions, W_init=w_init, \
-        b_init=tf.random_uniform_initializer(-init_w, init_w), in_channels=hidden_dim, name='policy_logstd')
+        self.mean_linear = Dense(
+            n_units=num_actions, W_init=w_init, b_init=tf.random_uniform_initializer(-init_w, init_w),
+            in_channels=hidden_dim, name='policy_mean'
+        )
+        self.log_std_linear = Dense(
+            n_units=num_actions, W_init=w_init, b_init=tf.random_uniform_initializer(-init_w, init_w),
+            in_channels=hidden_dim, name='policy_logstd'
+        )
 
         self.action_range = action_range
         self.num_actions = num_actions
@@ -209,13 +179,13 @@ def forward(self, state):
         return mean, log_std
 
     def evaluate(self, state, epsilon=1e-6):
-        ''' generate action with state for calculating gradients '''
+        """ generate action with state for calculating gradients """
         state = state.astype(np.float32)
         mean, log_std = self.forward(state)
         std = tf.math.exp(log_std)  # no clip in evaluation, clip affects gradients flow
 
         normal = Normal(0, 1)
-        z = normal.sample()
+        z = normal.sample(mean.shape)
         action_0 = tf.math.tanh(mean + std * z)  # TanhNormal distribution as actions; reparameterization trick
         action = self.action_range * action_0
         # according to original paper, with an extra last term for normalizing different action range
@@ -228,30 +198,32 @@ def evaluate(self, state, epsilon=1e-6):
 
         return action, log_prob, z, mean, log_std
 
-    def get_action(self, state, deterministic):
-        ''' generate action with state for interaction with envronment '''
+    def get_action(self, state, greedy=False):
+        """ generate action with state for interaction with envronment """
         mean, log_std = self.forward([state])
         std = tf.math.exp(log_std)
 
         normal = Normal(0, 1)
-        z = normal.sample()
+        z = normal.sample(mean.shape)
         action = self.action_range * tf.math.tanh(
             mean + std * z
         )  # TanhNormal distribution as actions; reparameterization trick
 
-        action = self.action_range * mean if deterministic else action
+        action = self.action_range * tf.math.tanh(mean) if greedy else action
         return action.numpy()[0]
 
     def sample_action(self, ):
-        ''' generate random actions for exploration '''
+        """ generate random actions for exploration """
         a = tf.random.uniform([self.num_actions], -1, 1)
-
         return self.action_range * a.numpy()
 
 
-class SAC_Trainer():
+class SAC:
 
-    def __init__(self, replay_buffer, hidden_dim, action_range, soft_q_lr=3e-4, policy_lr=3e-4, alpha_lr=3e-4):
+    def __init__(
+            self, state_dim, action_dim, action_range, hidden_dim, replay_buffer, SOFT_Q_LR=3e-4, POLICY_LR=3e-4,
+            ALPHA_LR=3e-4
+    ):
         self.replay_buffer = replay_buffer
 
         # initialize all networks
@@ -260,28 +232,40 @@ def __init__(self, replay_buffer, hidden_dim, action_range, soft_q_lr=3e-4, poli
         self.target_soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim)
         self.target_soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim)
         self.policy_net = PolicyNetwork(state_dim, action_dim, hidden_dim, action_range)
+        self.soft_q_net1.train()
+        self.soft_q_net2.train()
+        self.target_soft_q_net1.eval()
+        self.target_soft_q_net2.eval()
+        self.policy_net.train()
+
         self.log_alpha = tf.Variable(0, dtype=np.float32, name='log_alpha')
         self.alpha = tf.math.exp(self.log_alpha)
         print('Soft Q Network (1,2): ', self.soft_q_net1)
         print('Policy Network: ', self.policy_net)
+        # set mode
+        self.soft_q_net1.train()
+        self.soft_q_net2.train()
+        self.target_soft_q_net1.eval()
+        self.target_soft_q_net2.eval()
+        self.policy_net.train()
 
         # initialize weights of target networks
         self.target_soft_q_net1 = self.target_ini(self.soft_q_net1, self.target_soft_q_net1)
         self.target_soft_q_net2 = self.target_ini(self.soft_q_net2, self.target_soft_q_net2)
 
-        self.soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
-        self.soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
-        self.policy_optimizer = tf.optimizers.Adam(policy_lr)
-        self.alpha_optimizer = tf.optimizers.Adam(alpha_lr)
+        self.soft_q_optimizer1 = tf.optimizers.Adam(SOFT_Q_LR)
+        self.soft_q_optimizer2 = tf.optimizers.Adam(SOFT_Q_LR)
+        self.policy_optimizer = tf.optimizers.Adam(POLICY_LR)
+        self.alpha_optimizer = tf.optimizers.Adam(ALPHA_LR)
 
     def target_ini(self, net, target_net):
-        ''' hard-copy update for initializing target networks '''
+        """ hard-copy update for initializing target networks """
         for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
             target_param.assign(param)
         return target_net
 
     def target_soft_update(self, net, target_net, soft_tau):
-        ''' soft update the target net with Polyak averaging '''
+        """ soft update the target net with Polyak averaging """
         for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
             target_param.assign(  # copy weight value into target parameters
                 target_param * (1.0 - soft_tau) + param * soft_tau
@@ -289,14 +273,15 @@ def target_soft_update(self, net, target_net, soft_tau):
         return target_net
 
     def update(self, batch_size, reward_scale=10., auto_entropy=True, target_entropy=-2, gamma=0.99, soft_tau=1e-2):
-        ''' update all networks in SAC '''
+        """ update all networks in SAC """
         state, action, reward, next_state, done = self.replay_buffer.sample(batch_size)
 
         reward = reward[:, np.newaxis]  # expand dim
         done = done[:, np.newaxis]
 
-        reward = reward_scale * (reward -
-                                 np.mean(reward, axis=0)) / np.std(reward, axis=0)  # normalize with batch mean and std
+        reward = reward_scale * (reward - np.mean(reward, axis=0)) / (
+            np.std(reward, axis=0) + 1e-6
+        )  # normalize with batch mean and std; plus a small number to prevent numerical problem
 
         # Training Q Function
         new_next_action, next_log_prob, _, _, _ = self.policy_net.evaluate(next_state)
@@ -323,9 +308,9 @@ def update(self, batch_size, reward_scale=10., auto_entropy=True, target_entropy
         with tf.GradientTape() as p_tape:
             new_action, log_prob, z, mean, log_std = self.policy_net.evaluate(state)
             new_q_input = tf.concat([state, new_action], 1)  # the dim 0 is number of samples
-            ''' implementation 1 '''
+            """ implementation 1 """
             predicted_new_q_value = tf.minimum(self.soft_q_net1(new_q_input), self.soft_q_net2(new_q_input))
-            # ''' implementation 2 '''
+            # """ implementation 2 """
             # predicted_new_q_value = self.soft_q_net1(new_q_input)
             policy_loss = tf.reduce_mean(self.alpha * log_prob - predicted_new_q_value)
         p_grad = p_tape.gradient(policy_loss, self.policy_net.trainable_weights)
@@ -343,147 +328,126 @@ def update(self, batch_size, reward_scale=10., auto_entropy=True, target_entropy
             self.alpha = 1.
             alpha_loss = 0
 
-    # Soft update the target value nets
+        # Soft update the target value nets
         self.target_soft_q_net1 = self.target_soft_update(self.soft_q_net1, self.target_soft_q_net1, soft_tau)
         self.target_soft_q_net2 = self.target_soft_update(self.soft_q_net2, self.target_soft_q_net2, soft_tau)
 
-    def save_weights(self):  # save trained weights
-        tl.files.save_npz(self.soft_q_net1.trainable_weights, name='model_q_net1.npz')
-        tl.files.save_npz(self.soft_q_net2.trainable_weights, name='model_q_net2.npz')
-        tl.files.save_npz(self.target_soft_q_net1.trainable_weights, name='model_target_q_net1.npz')
-        tl.files.save_npz(self.target_soft_q_net2.trainable_weights, name='model_target_q_net2.npz')
-        tl.files.save_npz(self.policy_net.trainable_weights, name='model_policy_net.npz')
+    def save(self):  # save trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        extend_path = lambda s: os.path.join(path, s)
+        tl.files.save_npz(self.soft_q_net1.trainable_weights, extend_path('model_q_net1.npz'))
+        tl.files.save_npz(self.soft_q_net2.trainable_weights, extend_path('model_q_net2.npz'))
+        tl.files.save_npz(self.target_soft_q_net1.trainable_weights, extend_path('model_target_q_net1.npz'))
+        tl.files.save_npz(self.target_soft_q_net2.trainable_weights, extend_path('model_target_q_net2.npz'))
+        tl.files.save_npz(self.policy_net.trainable_weights, extend_path('model_policy_net.npz'))
+        np.save(extend_path('log_alpha.npy'), self.log_alpha.numpy())  # save log_alpha variable
 
     def load_weights(self):  # load trained weights
-        tl.files.load_and_assign_npz(name='model_q_net1.npz', network=self.soft_q_net1)
-        tl.files.load_and_assign_npz(name='model_q_net2.npz', network=self.soft_q_net2)
-        tl.files.load_and_assign_npz(name='model_target_q_net1.npz', network=self.target_soft_q_net1)
-        tl.files.load_and_assign_npz(name='model_target_q_net2.npz', network=self.target_soft_q_net2)
-        tl.files.load_and_assign_npz(name='model_policy_net.npz', network=self.policy_net)
-
-
-def plot(frame_idx, rewards):
-    clear_output(True)
-    plt.figure(figsize=(20, 5))
-    plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))
-    plt.plot(rewards)
-    plt.xlabel('Episode')
-    plt.ylabel('Episode Reward')
-    plt.savefig('sac.png')
-    # plt.show()
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        extend_path = lambda s: os.path.join(path, s)
+        tl.files.load_and_assign_npz(extend_path('model_q_net1.npz'), self.soft_q_net1)
+        tl.files.load_and_assign_npz(extend_path('model_q_net2.npz'), self.soft_q_net2)
+        tl.files.load_and_assign_npz(extend_path('model_target_q_net1.npz'), self.target_soft_q_net1)
+        tl.files.load_and_assign_npz(extend_path('model_target_q_net2.npz'), self.target_soft_q_net2)
+        tl.files.load_and_assign_npz(extend_path('model_policy_net.npz'), self.policy_net)
+        self.log_alpha.assign(np.load(extend_path('log_alpha.npy')))  # load log_alpha variable
 
 
 if __name__ == '__main__':
     # initialization of env
-    env = NormalizedActions(gym.make(ENV))
-    action_dim = env.action_space.shape[0]
+    env = gym.make(ENV_ID).unwrapped
     state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_range = env.action_space.high  # scale action, [-action_range, action_range]
+
+    # reproducible
+    env.seed(RANDOM_SEED)
+    random.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
+
     # initialization of buffer
-    replay_buffer = ReplayBuffer(replay_buffer_size)
+    replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
     # initialization of trainer
-    sac_trainer=SAC_Trainer(replay_buffer, hidden_dim=hidden_dim, action_range=action_range, \
-    soft_q_lr=soft_q_lr, policy_lr=policy_lr, alpha_lr=alpha_lr )
-    #set train mode
-    sac_trainer.soft_q_net1.train()
-    sac_trainer.soft_q_net2.train()
-    sac_trainer.target_soft_q_net1.train()
-    sac_trainer.target_soft_q_net2.train()
-    sac_trainer.policy_net.train()
+    agent = SAC(state_dim, action_dim, action_range, HIDDEN_DIM, replay_buffer, SOFT_Q_LR, POLICY_LR, ALPHA_LR)
 
+    t0 = time.time()
     # training loop
     if args.train:
         frame_idx = 0
-        rewards = []
-        t0 = time.time()
-        while frame_idx < max_frames:
-            state = env.reset()
-            state = state.astype(np.float32)
+        all_episode_reward = []
+
+        # need an extra call here to make inside functions be able to use model.forward
+        state = env.reset().astype(np.float32)
+        agent.policy_net([state])
+
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset().astype(np.float32)
             episode_reward = 0
-            if frame_idx < 1:
-                print('intialize')
-                _ = sac_trainer.policy_net(
-                    [state]
-                )  # need an extra call here to make inside functions be able to use model.forward
-
-            for step in range(max_steps):
-                if frame_idx > explore_steps:
-                    action = sac_trainer.policy_net.get_action(state, deterministic=DETERMINISTIC)
+            for step in range(MAX_STEPS):
+                if RENDER:
+                    env.render()
+                if frame_idx > EXPLORE_STEPS:
+                    action = agent.policy_net.get_action(state)
                 else:
-                    action = sac_trainer.policy_net.sample_action()
+                    action = agent.policy_net.sample_action()
 
                 next_state, reward, done, _ = env.step(action)
                 next_state = next_state.astype(np.float32)
-                env.render()
-                done = 1 if done ==True else 0
-                # print('s:', state, action, reward, next_state, done)
+                done = 1 if done is True else 0
 
                 replay_buffer.push(state, action, reward, next_state, done)
-
                 state = next_state
                 episode_reward += reward
                 frame_idx += 1
 
-                if len(replay_buffer) > batch_size:
-                    for i in range(update_itr):
-                        sac_trainer.update(
-                            batch_size, reward_scale=reward_scale, auto_entropy=AUTO_ENTROPY,
+                if len(replay_buffer) > BATCH_SIZE:
+                    for i in range(UPDATE_ITR):
+                        agent.update(
+                            BATCH_SIZE, reward_scale=REWARD_SCALE, auto_entropy=AUTO_ENTROPY,
                             target_entropy=-1. * action_dim
                         )
 
-                if frame_idx % 500 == 0:
-                    plot(frame_idx, rewards)
-
                 if done:
                     break
-            episode = int(frame_idx / max_steps)  # current episode
-            all_episodes = int(max_frames / max_steps)  # total episodes
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
             print(
-                'Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                    episode, all_episodes, episode_reward,
+                'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward,
                     time.time() - t0
                 )
             )
-            rewards.append(episode_reward)
-        sac_trainer.save_weights()
+        agent.save()
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
 
     if args.test:
-        frame_idx = 0
-        rewards = []
-        t0 = time.time()
-        sac_trainer.load_weights()
+        agent.load_weights()
 
-        while frame_idx < test_frames:
-            state = env.reset()
-            state = state.astype(np.float32)
+        # need an extra call here to make inside functions be able to use model.forward
+        state = env.reset().astype(np.float32)
+        agent.policy_net([state])
+
+        for episode in range(TEST_EPISODES):
+            state = env.reset().astype(np.float32)
             episode_reward = 0
-            if frame_idx < 1:
-                print('intialize')
-                _ = sac_trainer.policy_net(
-                    [state]
-                )  # need an extra call to make inside functions be able to use forward
-
-            for step in range(max_steps):
-                action = sac_trainer.policy_net.get_action(state, deterministic=DETERMINISTIC)
-                next_state, reward, done, _ = env.step(action)
-                next_state = next_state.astype(np.float32)
+            for step in range(MAX_STEPS):
                 env.render()
-                done = 1 if done ==True else 0
-
-                state = next_state
+                state, reward, done, info = env.step(agent.policy_net.get_action(state, greedy=True))
+                state = state.astype(np.float32)
                 episode_reward += reward
-                frame_idx += 1
-
-                # if frame_idx % 50 == 0:
-                #     plot(frame_idx, rewards)
-
                 if done:
                     break
-            episode = int(frame_idx / max_steps)
-            all_episodes = int(test_frames / max_steps)
             print(
-                'Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
-                    episode, all_episodes, episode_reward,
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
                     time.time() - t0
                 )
             )
-            rewards.append(episode_reward)
diff --git a/examples/reinforcement_learning/tutorial_TD3.py b/examples/reinforcement_learning/tutorial_TD3.py
index e90e5b8fb..d6fe82d32 100644
--- a/examples/reinforcement_learning/tutorial_TD3.py
+++ b/examples/reinforcement_learning/tutorial_TD3.py
@@ -1,12 +1,12 @@
-'''
+"""
 Twin Delayed DDPG (TD3)
 ------------------------
 DDPG suffers from problems like overestimate of Q-values and sensitivity to hyper-parameters.
 Twin Delayed DDPG (TD3) is a variant of DDPG with several tricks:
-* Trick One: Clipped Double-Q Learning. TD3 learns two Q-functions instead of one (hence “twin”), 
+* Trick One: Clipped Double-Q Learning. TD3 learns two Q-functions instead of one (hence "twin"),
 and uses the smaller of the two Q-values to form the targets in the Bellman error loss functions.
 
-* Trick Two: “Delayed” Policy Updates. TD3 updates the policy (and target networks) less frequently 
+* Trick Two: "Delayed" Policy Updates. TD3 updates the policy (and target networks) less frequently
 than the Q-function. 
 
 * Trick Three: Target Policy Smoothing. TD3 adds noise to the target action, to make it harder for 
@@ -38,33 +38,26 @@
 -------
 python tutorial_TD3.py --train/test
 
-'''
+"""
 
 import argparse
-import math
+import os
 import random
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
-from IPython.display import clear_output
-
-import gym
 import tensorflow as tf
+
 import tensorflow_probability as tfp
 import tensorlayer as tl
 from tensorlayer.layers import Dense
 from tensorlayer.models import Model
 
-tfd = tfp.distributions
-Normal = tfd.Normal
-
+Normal = tfp.distributions.Normal
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
-random.seed(2)
-np.random.seed(2)
-tf.random.set_seed(2)  # reproducible
-
 # add arguments in command  --train/test
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
 parser.add_argument('--train', dest='train', action='store_true', default=False)
@@ -73,37 +66,40 @@
 
 #####################  hyper parameters  ####################
 # choose env
-ENV = 'Pendulum-v0'
-action_range = 1.  # scale action, [-action_range, action_range]
+ENV_ID = 'Pendulum-v0'  # environment id
+RANDOM_SEED = 2  # random seed
+RENDER = False  # render while training
 
 # RL training
-max_frames = 40000  # total number of steps for training
-test_frames = 300  # total number of steps for testing
-max_steps = 150  # maximum number of steps for one episode
-batch_size = 64  # udpate batchsize
-explore_steps = 500  # 500 for random action sampling in the beginning of training
-update_itr = 3  # repeated updates for single step
-hidden_dim = 32  # size of hidden layers for networks
-q_lr = 3e-4  # q_net learning rate
-policy_lr = 3e-4  # policy_net learning rate
-policy_target_update_interval = 3  # delayed steps for updating the policy network and target networks
-explore_noise_scale = 1.0  # range of action noise for exploration
-eval_noise_scale = 0.5  # range of action noise for evaluation of action value
-reward_scale = 1.  # value range of reward
-replay_buffer_size = 5e5  # size of replay buffer
+ALG_NAME = 'TD3'
+TRAIN_EPISODES = 100  # total number of episodes for training
+TEST_EPISODES = 10  # total number of episodes for training
+MAX_STEPS = 200  # maximum number of steps for one episode
+BATCH_SIZE = 64  # update batch size
+EXPLORE_STEPS = 500  # 500 for random action sampling in the beginning of training
+
+HIDDEN_DIM = 64  # size of hidden layers for networks
+UPDATE_ITR = 3  # repeated updates for single step
+Q_LR = 3e-4  # q_net learning rate
+POLICY_LR = 3e-4  # policy_net learning rate
+POLICY_TARGET_UPDATE_INTERVAL = 3  # delayed steps for updating the policy network and target networks
+EXPLORE_NOISE_SCALE = 1.0  # range of action noise for exploration
+EVAL_NOISE_SCALE = 0.5  # range of action noise for evaluation of action value
+REWARD_SCALE = 1.  # value range of reward
+REPLAY_BUFFER_SIZE = 5e5  # size of replay buffer
 
 ###############################  TD3  ####################################
 
 
 class ReplayBuffer:
-    '''
+    """
     a ring buffer for storing transitions and sampling for training
     :state: (state_dim,)
     :action: (action_dim,)
     :reward: (,), scalar
     :next_state: (state_dim,)
     :done: (,), scalar (0 and 1) or bool (True and False)
-    '''
+    """
 
     def __init__(self, capacity):
         self.capacity = capacity
@@ -119,42 +115,20 @@ def push(self, state, action, reward, next_state, done):
     def sample(self, batch_size):
         batch = random.sample(self.buffer, batch_size)
         state, action, reward, next_state, done = map(np.stack, zip(*batch))  # stack for each element
-        ''' 
+        """ 
         the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
         zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
         the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
         np.stack((1,2)) => array([1, 2])
-        '''
+        """
         return state, action, reward, next_state, done
 
     def __len__(self):
         return len(self.buffer)
 
 
-class NormalizedActions(gym.ActionWrapper):
-    ''' normalize the actions to be in reasonable range '''
-
-    def _action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = low + (action + 1.0) * 0.5 * (high - low)
-        action = np.clip(action, low, high)
-
-        return action
-
-    def _reverse_action(self, action):
-        low = self.action_space.low
-        high = self.action_space.high
-
-        action = 2 * (action - low) / (high - low) - 1
-        action = np.clip(action, low, high)
-
-        return action
-
-
 class QNetwork(Model):
-    ''' the network for evaluate values of state-action pairs: Q(s,a) '''
+    """ the network for evaluate values of state-action pairs: Q(s,a) """
 
     def __init__(self, num_inputs, num_actions, hidden_dim, init_w=3e-3):
         super(QNetwork, self).__init__()
@@ -174,21 +148,19 @@ def forward(self, input):
 
 
 class PolicyNetwork(Model):
-    ''' the network for generating non-determinstic (Gaussian distributed) action from the state input '''
+    """ the network for generating non-deterministic (Gaussian distributed) action from the state input """
 
     def __init__(self, num_inputs, num_actions, hidden_dim, action_range=1., init_w=3e-3):
         super(PolicyNetwork, self).__init__()
-
-        # w_init = tf.keras.initializers.glorot_normal(seed=None)
         w_init = tf.random_uniform_initializer(-init_w, init_w)
 
         self.linear1 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=num_inputs, name='policy1')
         self.linear2 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy2')
         self.linear3 = Dense(n_units=hidden_dim, act=tf.nn.relu, W_init=w_init, in_channels=hidden_dim, name='policy3')
-
-        self.output_linear = Dense(n_units=num_actions, W_init=w_init, \
-        b_init=tf.random_uniform_initializer(-init_w, init_w), in_channels=hidden_dim, name='policy_output')
-
+        self.output_linear = Dense(
+            n_units=num_actions, W_init=w_init, b_init=tf.random_uniform_initializer(-init_w, init_w),
+            in_channels=hidden_dim, name='policy_output'
+        )
         self.action_range = action_range
         self.num_actions = num_actions
 
@@ -196,16 +168,14 @@ def forward(self, state):
         x = self.linear1(state)
         x = self.linear2(x)
         x = self.linear3(x)
-
         output = tf.nn.tanh(self.output_linear(x))  # unit range output [-1, 1]
-
         return output
 
     def evaluate(self, state, eval_noise_scale):
-        ''' 
+        """ 
         generate action with state for calculating gradients;
         eval_noise_scale: as the trick of target policy smoothing, for generating noisy actions.
-        '''
+        """
         state = state.astype(np.float32)
         action = self.forward(state)
 
@@ -217,32 +187,31 @@ def evaluate(self, state, eval_noise_scale):
         noise = normal.sample(action.shape) * eval_noise_scale
         noise = tf.clip_by_value(noise, -eval_noise_clip, eval_noise_clip)
         action = action + noise
-
         return action
 
-    def get_action(self, state, explore_noise_scale):
-        ''' generate action with state for interaction with envronment '''
+    def get_action(self, state, explore_noise_scale, greedy=False):
+        """ generate action with state for interaction with envronment """
         action = self.forward([state])
-        action = action.numpy()[0]
-
+        action = self.action_range * action.numpy()[0]
+        if greedy:
+            return action
         # add noise
         normal = Normal(0, 1)
         noise = normal.sample(action.shape) * explore_noise_scale
-        action = self.action_range * action + noise
-
+        action += noise
         return action.numpy()
 
-    def sample_action(self, ):
-        ''' generate random actions for exploration '''
+    def sample_action(self):
+        """ generate random actions for exploration """
         a = tf.random.uniform([self.num_actions], -1, 1)
-
         return self.action_range * a.numpy()
 
 
-class TD3_Trainer():
+class TD3:
 
     def __init__(
-            self, replay_buffer, hidden_dim, action_range, policy_target_update_interval=1, q_lr=3e-4, policy_lr=3e-4
+            self, state_dim, action_dim, action_range, hidden_dim, replay_buffer, policy_target_update_interval=1,
+            q_lr=3e-4, policy_lr=3e-4
     ):
         self.replay_buffer = replay_buffer
 
@@ -261,6 +230,14 @@ def __init__(
         self.target_q_net2 = self.target_ini(self.q_net2, self.target_q_net2)
         self.target_policy_net = self.target_ini(self.policy_net, self.target_policy_net)
 
+        # set train mode
+        self.q_net1.train()
+        self.q_net2.train()
+        self.target_q_net1.eval()
+        self.target_q_net2.eval()
+        self.policy_net.train()
+        self.target_policy_net.eval()
+
         self.update_cnt = 0
         self.policy_target_update_interval = policy_target_update_interval
 
@@ -269,13 +246,13 @@ def __init__(
         self.policy_optimizer = tf.optimizers.Adam(policy_lr)
 
     def target_ini(self, net, target_net):
-        ''' hard-copy update for initializing target networks '''
+        """ hard-copy update for initializing target networks """
         for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
             target_param.assign(param)
         return target_net
 
     def target_soft_update(self, net, target_net, soft_tau):
-        ''' soft update the target net with Polyak averaging '''
+        """ soft update the target net with Polyak averaging """
         for target_param, param in zip(target_net.trainable_weights, net.trainable_weights):
             target_param.assign(  # copy weight value into target parameters
                 target_param * (1.0 - soft_tau) + param * soft_tau
@@ -283,7 +260,7 @@ def target_soft_update(self, net, target_net, soft_tau):
         return target_net
 
     def update(self, batch_size, eval_noise_scale, reward_scale=10., gamma=0.9, soft_tau=1e-2):
-        ''' update all networks in TD3 '''
+        """ update all networks in TD3 """
         self.update_cnt += 1
         state, action, reward, next_state, done = self.replay_buffer.sample(batch_size)
 
@@ -293,8 +270,9 @@ def update(self, batch_size, eval_noise_scale, reward_scale=10., gamma=0.9, soft
         new_next_action = self.target_policy_net.evaluate(
             next_state, eval_noise_scale=eval_noise_scale
         )  # clipped normal noise
-        reward = reward_scale * (reward -
-                                 np.mean(reward, axis=0)) / np.std(reward, axis=0)  # normalize with batch mean and std
+        reward = reward_scale * (reward - np.mean(reward, axis=0)) / (
+            np.std(reward, axis=0) + 1e-6
+        )  # normalize with batch mean and std; plus a small number to prevent numerical problem
 
         # Training Q Function
         target_q_input = tf.concat([next_state, new_next_action], 1)  # the dim 0 is number of samples
@@ -322,9 +300,9 @@ def update(self, batch_size, eval_noise_scale, reward_scale=10., gamma=0.9, soft
                     state, eval_noise_scale=0.0
                 )  # no noise, deterministic policy gradients
                 new_q_input = tf.concat([state, new_action], 1)
-                # ''' implementation 1 '''
+                # """ implementation 1 """
                 # predicted_new_q_value = tf.minimum(self.q_net1(new_q_input),self.q_net2(new_q_input))
-                ''' implementation 2 '''
+                """ implementation 2 """
                 predicted_new_q_value = self.q_net1(new_q_input)
                 policy_loss = -tf.reduce_mean(predicted_new_q_value)
             p_grad = p_tape.gradient(policy_loss, self.policy_net.trainable_weights)
@@ -335,138 +313,124 @@ def update(self, batch_size, eval_noise_scale, reward_scale=10., gamma=0.9, soft
             self.target_q_net2 = self.target_soft_update(self.q_net2, self.target_q_net2, soft_tau)
             self.target_policy_net = self.target_soft_update(self.policy_net, self.target_policy_net, soft_tau)
 
-    def save_weights(self):  # save trained weights
-        tl.files.save_npz(self.q_net1.trainable_weights, name='model_q_net1.npz')
-        tl.files.save_npz(self.q_net2.trainable_weights, name='model_q_net2.npz')
-        tl.files.save_npz(self.target_q_net1.trainable_weights, name='model_target_q_net1.npz')
-        tl.files.save_npz(self.target_q_net2.trainable_weights, name='model_target_q_net2.npz')
-        tl.files.save_npz(self.policy_net.trainable_weights, name='model_policy_net.npz')
-        tl.files.save_npz(self.target_policy_net.trainable_weights, name='model_target_policy_net.npz')
-
-    def load_weights(self):  # load trained weights
-        tl.files.load_and_assign_npz(name='model_q_net1.npz', network=self.q_net1)
-        tl.files.load_and_assign_npz(name='model_q_net2.npz', network=self.q_net2)
-        tl.files.load_and_assign_npz(name='model_target_q_net1.npz', network=self.target_q_net1)
-        tl.files.load_and_assign_npz(name='model_target_q_net2.npz', network=self.target_q_net2)
-        tl.files.load_and_assign_npz(name='model_policy_net.npz', network=self.policy_net)
-        tl.files.load_and_assign_npz(name='model_target_policy_net.npz', network=self.target_policy_net)
-
-
-def plot(frame_idx, rewards):
-    clear_output(True)
-    plt.figure(figsize=(20, 5))
-    plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))
-    plt.plot(rewards)
-    plt.xlabel('Episode')
-    plt.ylabel('Episode Reward')
-    plt.savefig('td3.png')
-    # plt.show()
+    def save(self):  # save trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        extend_path = lambda s: os.path.join(path, s)
+        tl.files.save_npz(self.q_net1.trainable_weights, extend_path('model_q_net1.npz'))
+        tl.files.save_npz(self.q_net2.trainable_weights, extend_path('model_q_net2.npz'))
+        tl.files.save_npz(self.target_q_net1.trainable_weights, extend_path('model_target_q_net1.npz'))
+        tl.files.save_npz(self.target_q_net2.trainable_weights, extend_path('model_target_q_net2.npz'))
+        tl.files.save_npz(self.policy_net.trainable_weights, extend_path('model_policy_net.npz'))
+        tl.files.save_npz(self.target_policy_net.trainable_weights, extend_path('model_target_policy_net.npz'))
+
+    def load(self):  # load trained weights
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        extend_path = lambda s: os.path.join(path, s)
+        tl.files.load_and_assign_npz(extend_path('model_q_net1.npz'), self.q_net1)
+        tl.files.load_and_assign_npz(extend_path('model_q_net2.npz'), self.q_net2)
+        tl.files.load_and_assign_npz(extend_path('model_target_q_net1.npz'), self.target_q_net1)
+        tl.files.load_and_assign_npz(extend_path('model_target_q_net2.npz'), self.target_q_net2)
+        tl.files.load_and_assign_npz(extend_path('model_policy_net.npz'), self.policy_net)
+        tl.files.load_and_assign_npz(extend_path('model_target_policy_net.npz'), self.target_policy_net)
 
 
 if __name__ == '__main__':
-
     # initialization of env
-    env = NormalizedActions(gym.make(ENV))
-    action_dim = env.action_space.shape[0]
+    env = gym.make(ENV_ID).unwrapped
     state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_range = env.action_space.high  # scale action, [-action_range, action_range]
+
+    # reproducible
+    env.seed(RANDOM_SEED)
+    random.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
+
     # initialization of buffer
-    replay_buffer = ReplayBuffer(replay_buffer_size)
+    replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
     # initialization of trainer
-    td3_trainer=TD3_Trainer(replay_buffer, hidden_dim=hidden_dim, policy_target_update_interval=policy_target_update_interval, \
-    action_range=action_range, q_lr=q_lr, policy_lr=policy_lr )
-    # set train mode
-    td3_trainer.q_net1.train()
-    td3_trainer.q_net2.train()
-    td3_trainer.target_q_net1.train()
-    td3_trainer.target_q_net2.train()
-    td3_trainer.policy_net.train()
-    td3_trainer.target_policy_net.train()
+    agent = TD3(
+        state_dim, action_dim, action_range, HIDDEN_DIM, replay_buffer, POLICY_TARGET_UPDATE_INTERVAL, Q_LR, POLICY_LR
+    )
+    t0 = time.time()
 
     # training loop
     if args.train:
         frame_idx = 0
-        rewards = []
-        t0 = time.time()
-        while frame_idx < max_frames:
-            state = env.reset()
-            state = state.astype(np.float32)
+        all_episode_reward = []
+
+        # need an extra call here to make inside functions be able to use model.forward
+        state = env.reset().astype(np.float32)
+        agent.policy_net([state])
+        agent.target_policy_net([state])
+
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset().astype(np.float32)
             episode_reward = 0
-            if frame_idx < 1:
-                print('intialize')
-                _ = td3_trainer.policy_net(
-                    [state]
-                )  # need an extra call here to make inside functions be able to use model.forward
-                _ = td3_trainer.target_policy_net([state])
-
-            for step in range(max_steps):
-                if frame_idx > explore_steps:
-                    action = td3_trainer.policy_net.get_action(state, explore_noise_scale=1.0)
+
+            for step in range(MAX_STEPS):
+                if RENDER:
+                    env.render()
+                if frame_idx > EXPLORE_STEPS:
+                    action = agent.policy_net.get_action(state, EXPLORE_NOISE_SCALE)
                 else:
-                    action = td3_trainer.policy_net.sample_action()
+                    action = agent.policy_net.sample_action()
 
                 next_state, reward, done, _ = env.step(action)
                 next_state = next_state.astype(np.float32)
-                env.render()
-                done = 1 if done ==True else 0
+                done = 1 if done is True else 0
 
                 replay_buffer.push(state, action, reward, next_state, done)
-
                 state = next_state
                 episode_reward += reward
                 frame_idx += 1
 
-                if len(replay_buffer) > batch_size:
-                    for i in range(update_itr):
-                        td3_trainer.update(batch_size, eval_noise_scale=0.5, reward_scale=1.)
-
-                if frame_idx % 500 == 0:
-                    plot(frame_idx, rewards)
+                if len(replay_buffer) > BATCH_SIZE:
+                    for i in range(UPDATE_ITR):
+                        agent.update(BATCH_SIZE, EVAL_NOISE_SCALE, REWARD_SCALE)
 
                 if done:
                     break
-            episode = int(frame_idx / max_steps)  # current episode
-            all_episodes = int(max_frames / max_steps)  # total episodes
-            print('Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'\
-            .format(episode, all_episodes, episode_reward, time.time()-t0 ))
-            rewards.append(episode_reward)
-        td3_trainer.save_weights()
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            print(
+                'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
+        agent.save()
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
 
     if args.test:
-        frame_idx = 0
-        rewards = []
-        t0 = time.time()
+        agent.load()
 
-        td3_trainer.load_weights()
+        # need an extra call here to make inside functions be able to use model.forward
+        state = env.reset().astype(np.float32)
+        agent.policy_net([state])
 
-        while frame_idx < test_frames:
-            state = env.reset()
-            state = state.astype(np.float32)
+        for episode in range(TEST_EPISODES):
+            state = env.reset().astype(np.float32)
             episode_reward = 0
-            if frame_idx < 1:
-                print('intialize')
-                _ = td3_trainer.policy_net(
-                    [state]
-                )  # need an extra call to make inside functions be able to use forward
-                _ = td3_trainer.target_policy_net([state])
-
-            for step in range(max_steps):
-                action = td3_trainer.policy_net.get_action(state, explore_noise_scale=1.0)
-                next_state, reward, done, _ = env.step(action)
-                next_state = next_state.astype(np.float32)
+            for step in range(MAX_STEPS):
                 env.render()
-                done = 1 if done ==True else 0
-
-                state = next_state
+                action = agent.policy_net.get_action(state, EXPLORE_NOISE_SCALE, greedy=True)
+                state, reward, done, info = env.step(action)
+                state = state.astype(np.float32)
                 episode_reward += reward
-                frame_idx += 1
-
-                # if frame_idx % 50 == 0:
-                #     plot(frame_idx, rewards)
-
                 if done:
                     break
-            episode = int(frame_idx / max_steps)
-            all_episodes = int(test_frames / max_steps)
-            print('Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'\
-            .format(episode, all_episodes, episode_reward, time.time()-t0 ) )
-            rewards.append(episode_reward)
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_TRPO.py b/examples/reinforcement_learning/tutorial_TRPO.py
index 6f90b9aad..ae47a20bd 100644
--- a/examples/reinforcement_learning/tutorial_TRPO.py
+++ b/examples/reinforcement_learning/tutorial_TRPO.py
@@ -31,367 +31,72 @@
 import argparse
 import copy
 import os
+import threading
 import time
 
+import gym
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.signal
-
-import gym
 import tensorflow as tf
+
 import tensorflow_probability as tfp
 import tensorlayer as tl
-from gym.spaces import Box, Discrete
 
 parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
-parser.add_argument('--train', dest='train', action='store_true', default=True)
-parser.add_argument('--test', dest='train', action='store_false')
-
-parser.add_argument('--env', type=str, default='Pendulum-v0')  # environment name
-parser.add_argument('--hid', type=int, default=64)  # size of each hidden layer
-parser.add_argument('--l', type=int, default=2)  # hidden layer length
-parser.add_argument('--gamma', type=float, default=0.99)  # reward discount
-parser.add_argument('--seed', '-s', type=int, default=1)  # random seed
-parser.add_argument('--steps', type=int, default=4000)  # total number of steps for each episode
-parser.add_argument('--epochs', type=int, default=500)  # total number of episodes for training
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
 args = parser.parse_args()
 
 #####################  hyper parameters  ####################
 
-ENV_NAME = args.env  # environment name
-HIDDEN_SIZES = [args.hid] * args.l  # hidden layer size
-SEED = args.seed  # random seed
-STEPS_PER_EPOCH = args.steps  # total number of steps for each episode
-EPOCHS = args.epochs  # total number of episodes for training
-GAMMA = args.gamma  # reward discount
+ENV_ID = 'Pendulum-v0'  # environment id
+RANDOM_SEED = 2  # random seed
+RENDER = False
+
+ALG_NAME = 'TRPO'
+TRAIN_EPISODES = 1000  # total number of episodes for training
+TEST_EPISODES = 100  # total number of episodes for testing
+MAX_STEPS = 200  # total number of steps for each episode
 
+HIDDEN_SIZES = [64, 64]  # hidden layer size
+GAMMA = 0.99  # reward discount
 DELTA = 0.01  # KL-divergence limit for TRPO update.
 VF_LR = 1e-3  # Learning rate for value function optimizer
-TRAIN_V_ITERS = 80  # Number of gradient descent steps to take on value function per epoch
+TRAIN_VF_ITERS = 100  # Number of gradient descent steps to take on value function per epoch
 DAMPING_COEFF = 0.1  # Artifact for numerical stability
 CG_ITERS = 10  # Number of iterations of conjugate gradient to perform
 BACKTRACK_ITERS = 10  # Maximum number of steps allowed in the backtracking line search
 BACKTRACK_COEFF = 0.8  # How far back to step during backtracking line search
-LAM = 0.97  # Lambda for GAE-Lambda
-MAX_EP_LEN = 1000  # Maximum length of trajectory
+LAM = 0.97  # lambda for GAE-lambda
 SAVE_FREQ = 10  # How often (in terms of gap between epochs) to save the current policy and value function
 EPS = 1e-8  # epsilon
+BATCH_SIZE = 512  # batch size
 
 #####################  functions  ####################
 
 
-def combined_shape(length, shape=None):
-    """
-    combine length and shape based on shape type
-    :param length: int length
-    :param shape: shape, can be either scalar or array
-    :return: shape
-    """
-    if shape is None:
-        return length,
-    return (length, shape) if np.isscalar(shape) else (length, *shape)
-
-
-def keys_as_sorted_list(dict):
-    """
-    sorted keys of the dict
-    :param dict: dict input
-    :return: sorted key list
-    """
-    return sorted(list(dict.keys()))
-
-
-def values_as_sorted_list(dict):
-    """
-    sorted values of the dict
-    :param dict: dict input
-    :return: sorted value list
-    """
-    return [dict[k] for k in keys_as_sorted_list(dict)]
-
-
-def input_layer(dim=None):
-    """
-    create tensorlayer input layer from dimension input
-    :param dim: dimension int
-    :return: tensorlayer input layer
-    """
-    return tl.layers.Input(dtype=tf.float32, shape=combined_shape(None, dim))
-
-
-def input_layers(*args):
-    """
-    create tensorlayer input layers from a list of dimensions
-    :param args: a list of dimensions
-    :return: list of input layers
-    """
-    return [input_layer(dim) for dim in args]
-
-
-def input_layer_from_space(space):
-    """
-    create tensorlayer input layers from env.space input
-    :param space: env.space
-    :return: tensorlayer input layer
-    """
-    if isinstance(space, Box):
-        return input_layer(space.shape)
-    elif isinstance(space, Discrete):
-        return tl.layers.Input(dtype=tf.int32, shape=(None, ))
-    raise NotImplementedError
-
-
-def input_layers_from_spaces(*args):
-    """
-    create tensorlayer input layers from a list of env.space inputs
-    :param args: a list of env.space inputs
-    :return: tensorlayer input layer list
-    """
-    return [input_layer_from_space(space) for space in args]
-
-
-def mlp(x, hidden_sizes=(32, ), activation=tf.tanh, output_activation=None):
-    """
-    create Multi-Layer Perception
-    :param x: tensorlayer input layer
-    :param hidden_sizes: hidden layer size
-    :param activation: hidden layer activation function
-    :param output_activation: activation function for the output layer
-    :return: output layer
-    """
-    for h in hidden_sizes[:-1]:
-        x = tl.layers.Dense(n_units=h, act=activation)(x)
-    return tl.layers.Dense(n_units=hidden_sizes[-1], act=output_activation)(x)
-
-
-def get_vars(model: tl.models.Model):
-    """
-    get trainable parameters of the model
-    :param model: tensorlayer model
-    :return: a list of trainable parameters of the model
-    """
-    return model.trainable_weights
-
-
-def count_vars(model: tl.models.Model):
-    """
-    count trainable parameters of the model
-    :param model: tensorlayer model
-    :return: counts
-    """
-    v = get_vars(model)
-    return sum([np.prod(var.shape.as_list()) for var in v])
-
-
-def gaussian_likelihood(x, mu, log_std):
-    """
-    calculate gaussian likelihood
-    :param x: input distribution
-    :param mu: mu
-    :param log_std: log std
-    :return: gaussian likelihood
-    """
-    pre_sum = -0.5 * (((x - mu) / (tf.exp(log_std) + EPS))**2 + 2 * log_std + np.log(2 * np.pi))
-    return tf.reduce_sum(pre_sum, axis=1)
-
-
-def diagonal_gaussian_kl(mu0, log_std0, mu1, log_std1):
-    """
-    tf symbol for mean KL divergence between two batches of diagonal gaussian distributions,
-    where distributions are specified by means and log stds.
-    (https://en.wikipedia.org/wiki/Kullback-Leibler_divergence#Multivariate_normal_distributions)
-    """
-    var0, var1 = tf.exp(2 * log_std0), tf.exp(2 * log_std1)
-    pre_sum = 0.5 * (((mu1 - mu0)**2 + var0) / (var1 + EPS) - 1) + log_std1 - log_std0
-    all_kls = tf.reduce_sum(pre_sum, axis=1)
-    return tf.reduce_mean(all_kls)
-
-
-def categorical_kl(logp0, logp1):
-    """
-    tf symbol for mean KL divergence between two batches of categorical probability distributions,
-    where the distributions are input as log probs.
-    """
-    all_kls = tf.reduce_sum(tf.exp(logp1) * (logp1 - logp0), axis=1)
-    return tf.reduce_mean(all_kls)
-
-
-def flat_concat(xs):
-    """
-    flat concat input
-    :param xs: a list of tensor
-    :return: flat tensor
-    """
-    return tf.concat([tf.reshape(x, (-1, )) for x in xs], axis=0)
-
-
-def assign_params_from_flat(x, params):
-    """
-    assign params from flat input
-    :param x:
-    :param params:
-    :return: group
-    """
-    flat_size = lambda p: int(np.prod(p.shape.as_list()))  # the 'int' is important for scalars
-    splits = tf.split(x, [flat_size(p) for p in params])
-    new_params = [tf.reshape(p_new, p.shape) for p, p_new in zip(params, splits)]
-    return tf.group([p.assign(p_new) for p, p_new in zip(params, new_params)])
-
-
-def discount_cumsum(x, discount):
-    """
-    magic from rllab for computing discounted cumulative sums of vectors.
-
-    input:
-        vector x,
-        [x0,
-         x1,
-         x2]
-
-    output:
-        [x0 + discount * x1 + discount^2 * x2,
-         x1 + discount * x2,
-         x2]
-    """
-    return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1]
-
-
-"""
-Policies
-"""
-
-
-class MlpCategoricalPolicy:
-    """
-    Categorical Policy for discrete input
-    """
-
-    def __init__(self, x, a, hidden_sizes, activation, output_activation):
-        self.act_dim = a.n
-        x = input_layer_from_space(x)
-        logits = mlp(x, list(hidden_sizes) + [self.act_dim], activation, None)
-        self.model = tl.models.Model(x, logits)
-        self.model.train()
-
-    def cal_outputs_0(self, states):
-        states = states.astype(np.float32)
-        logits = self.model(states)
-        logp_all = tf.nn.log_softmax(logits)
-        pi = tf.squeeze(tfp.distributions.Multinomial(1, logits), axis=1)
-        logp_pi = tf.reduce_sum(tf.one_hot(pi, depth=self.act_dim) * logp_all, axis=1)
-        info = {'logp_all': logp_all}
-        return pi, logp_pi, info, logp_all
-
-    def cal_outputs_1(self, states, actions, old_logp_all):
-        pi, logp_pi, info, logp_all = self.cal_outputs_0(states)
-        logp = tf.reduce_sum(tf.one_hot(actions, depth=self.act_dim) * logp_all, axis=1)
-        d_kl = categorical_kl(logp_all, old_logp_all)
-
-        info_phs = {'logp_all': old_logp_all}
-
-        return pi, logp, logp_pi, info, info_phs, d_kl
-
-
-class MlpGaussianPolicy:
-    """
-    Gaussian Policy for continuous input
-    """
-
-    def __init__(self, x, a, hidden_sizes, activation, output_activation):
-        act_dim = a.shape[0]
-
-        x = input_layer_from_space(x)
-        mu = mlp(x, list(hidden_sizes) + [act_dim], activation, output_activation)
-        self.model = tl.models.Model(x, mu)
-        self.model.train()
-
-        self._log_std = tf.Variable(-0.5 * np.ones(act_dim, dtype=np.float32))
-        self.model.trainable_weights.append(self._log_std)
-
-    def cal_outputs_0(self, states):
-        states = states.astype(np.float32)
-        mu = self.model(states)
-        std = tf.exp(self._log_std)
-        pi = mu + tf.random.normal(tf.shape(mu)) * std
-        logp_pi = gaussian_likelihood(pi, mu, self._log_std)
-
-        info = {'mu': mu, 'log_std': self._log_std}
-
-        return pi, logp_pi, info, mu, self._log_std
-
-    def cal_outputs_1(self, states, actions, old_log_std_ph, old_mu_ph):
-        pi, logp_pi, info, mu, log_std = self.cal_outputs_0(states)
-        logp = gaussian_likelihood(actions, mu, log_std)
-        d_kl = diagonal_gaussian_kl(mu, log_std, old_mu_ph, old_log_std_ph)
-
-        info_phs = {'mu': old_mu_ph, 'log_std': old_log_std_ph}
-
-        return pi, logp, logp_pi, info, info_phs, d_kl
-
-
-"""
-Actor-Critics
-"""
-
-
-def mlp_actor_critic(
-        x: 'env.observation_space', a: 'env.action_space', hidden_sizes=(64, 64), activation=tf.tanh,
-        output_activation=None
-):
-    """
-    create actor and critic
-    :param x: observation space
-    :param a: action space
-    :param hidden_sizes: hidden layer size
-    :param activation: hidden layer activation function
-    :param output_activation: activation function for the output layer
-    :return: acter class and critic class
-    """
-    # default policy builder depends on action space
-    if isinstance(a, Box):
-        actor = MlpGaussianPolicy(x, a, hidden_sizes, activation, output_activation)
-    elif isinstance(a, Discrete):
-        actor = MlpCategoricalPolicy(x, a, hidden_sizes, activation, output_activation)
-    else:
-        raise ValueError('action space type error')
-
-    class Critic:
-
-        def __init__(self, obs_space, hidden_layer_sizes, activation_funcs):
-            inputs = input_layer_from_space(obs_space)
-            self.model = tl.models.Model(inputs, mlp(inputs, list(hidden_layer_sizes) + [1], activation_funcs, None))
-            self.model.train()
-
-        def critic_cal_func(self, states):
-            states = states.astype(np.float32)
-            return tf.squeeze(self.model(states), axis=1)
-
-    critic = Critic(x, hidden_sizes, activation)
-
-    return actor, critic
-
-
-class GAEBuffer:
+class GAE_Buffer:
     """
     A buffer for storing trajectories experienced by a TRPO agent interacting
-    with the environment, and using Generalized Advantage Estimation (GAE-Lambda)
+    with the environment, and using Generalized Advantage Estimation (GAE-lambda)
     for calculating the advantages of state-action pairs.
     """
 
-    def __init__(self, obs_dim, act_dim, size, info_shapes, gamma=0.99, lam=0.95):
-        self.obs_buf = np.zeros(combined_shape(size, obs_dim), dtype=np.float32)
-        self.act_buf = np.zeros(combined_shape(size, act_dim), dtype=np.float32)
+    def __init__(self, obs_dim, act_dim, size, gamma=0.99, lam=0.95):
+        self.obs_buf = np.zeros((size, obs_dim), dtype=np.float32)
+        self.act_buf = np.zeros((size, act_dim), dtype=np.float32)
         self.adv_buf = np.zeros(size, dtype=np.float32)
         self.rew_buf = np.zeros(size, dtype=np.float32)
         self.ret_buf = np.zeros(size, dtype=np.float32)
         self.val_buf = np.zeros(size, dtype=np.float32)
         self.logp_buf = np.zeros(size, dtype=np.float32)
-        self.info_bufs = {k: np.zeros([size] + list(v), dtype=np.float32) for k, v in info_shapes.items()}
-        self.sorted_info_keys = keys_as_sorted_list(self.info_bufs)
+        self.mean_buf = np.zeros(size, dtype=np.float32)
+        self.log_std_buf = np.zeros(size, dtype=np.float32)
         self.gamma, self.lam = gamma, lam
         self.ptr, self.path_start_idx, self.max_size = 0, 0, size
 
-    def store(self, obs, act, rew, val, logp, info):
+    def store(self, obs, act, rew, val, logp, mean, log_std):
         """
         Append one timestep of agent-environment interaction to the buffer.
         """
@@ -401,8 +106,8 @@ def store(self, obs, act, rew, val, logp, info):
         self.rew_buf[self.ptr] = rew
         self.val_buf[self.ptr] = val
         self.logp_buf[self.ptr] = logp
-        for i, k in enumerate(self.sorted_info_keys):
-            self.info_bufs[k][self.ptr] = info[i]
+        self.mean_buf[self.ptr] = mean
+        self.log_std_buf[self.ptr] = log_std
         self.ptr += 1
 
     def finish_path(self, last_val=0):
@@ -410,7 +115,7 @@ def finish_path(self, last_val=0):
         Call this at the end of a trajectory, or when one gets cut off
         by an epoch ending. This looks back in the buffer to where the
         trajectory started, and uses rewards and value estimates from
-        the whole trajectory to compute advantage estimates with GAE-Lambda,
+        the whole trajectory to compute advantage estimates with GAE-lambda,
         as well as compute the rewards-to-go for each state, to use as
         the targets for the value function.
 
@@ -420,20 +125,38 @@ def finish_path(self, last_val=0):
         This allows us to bootstrap the reward-to-go calculation to account
         for timesteps beyond the arbitrary episode horizon (or epoch cutoff).
         """
-
         path_slice = slice(self.path_start_idx, self.ptr)
         rews = np.append(self.rew_buf[path_slice], last_val)
         vals = np.append(self.val_buf[path_slice], last_val)
-
-        # the next two lines implement GAE-Lambda advantage calculation
+        # the next two lines implement GAE-lambda advantage calculation
         deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1]
-        self.adv_buf[path_slice] = discount_cumsum(deltas, self.gamma * self.lam)
+        self.adv_buf[path_slice] = self._discount_cumsum(deltas, self.gamma * self.lam)
 
         # the next line computes rewards-to-go, to be targets for the value function
-        self.ret_buf[path_slice] = discount_cumsum(rews, self.gamma)[:-1]
+        self.ret_buf[path_slice] = self._discount_cumsum(rews, self.gamma)[:-1]
 
         self.path_start_idx = self.ptr
 
+    def _discount_cumsum(self, x, discount):
+        """
+        magic from rllab for computing discounted cumulative sums of vectors.
+
+        input:
+            vector x,
+            [x0,
+             x1,
+             x2]
+
+        output:
+            [x0 + discount * x1 + discount^2 * x2,
+             x1 + discount * x2,
+             x2]
+        """
+        return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1]
+
+    def is_full(self):
+        return self.ptr == self.max_size
+
     def get(self):
         """
         Call this at the end of an epoch to get all of the data from
@@ -446,18 +169,11 @@ def get(self):
         # the next two lines implement the advantage normalization trick
         adv_mean, adv_std = np.mean(self.adv_buf), np.std(self.adv_buf)
         self.adv_buf = (self.adv_buf - adv_mean) / adv_std
-        return [self.obs_buf, self.act_buf, self.adv_buf, self.ret_buf, self.logp_buf
-                ] + values_as_sorted_list(self.info_bufs)
+        return [self.obs_buf, self.act_buf, self.adv_buf, self.ret_buf, self.logp_buf, self.mean_buf, self.log_std_buf]
 
 
-#####################  TRPO  ####################
-
 """
-
 Trust Region Policy Optimization 
-
-(with support for Natural Policy Gradient)
-
 """
 
 
@@ -466,151 +182,171 @@ class TRPO:
     trpo class
     """
 
-    def __init__(self, obs_space, act_space):
-
-        obs_dim = obs_space.shape
-        act_dim = act_space.shape
-
-        # # Main models and functions
-        self.actor, self.critic = mlp_actor_critic(obs_space, act_space, HIDDEN_SIZES)
-
-        if isinstance(act_space, Box):
-            act_dim = env.action_space.shape[0]
-            info_shapes = {'mu': [act_dim], 'log_std': [act_dim]}
-
-        elif isinstance(env.action_space, Discrete):
-            act_dim = env.action_space.n
-            info_shapes = {'logp_all': [act_dim]}
-        else:
-            raise Exception('info_shape error')
-
-        self.buf = GAEBuffer(obs_dim, act_dim, STEPS_PER_EPOCH, info_shapes, GAMMA, LAM)
-
-        # Optimizer for value function
+    def __init__(self, state_dim, action_dim, action_bound):
+        # critic
+        with tf.name_scope('critic'):
+            layer = input_layer = tl.layers.Input([None, state_dim], tf.float32)
+            for d in HIDDEN_SIZES:
+                layer = tl.layers.Dense(d, tf.nn.relu)(layer)
+            v = tl.layers.Dense(1)(layer)
+        self.critic = tl.models.Model(input_layer, v)
+        self.critic.train()
+
+        # actor
+        with tf.name_scope('actor'):
+            layer = input_layer = tl.layers.Input([None, state_dim], tf.float32)
+            for d in HIDDEN_SIZES:
+                layer = tl.layers.Dense(d, tf.nn.relu)(layer)
+            mean = tl.layers.Dense(action_dim, tf.nn.tanh)(layer)
+            mean = tl.layers.Lambda(lambda x: x * action_bound)(mean)
+            log_std = tf.Variable(np.zeros(action_dim, dtype=np.float32))
+
+        self.actor = tl.models.Model(input_layer, mean)
+        self.actor.trainable_weights.append(log_std)
+        self.actor.log_std = log_std
+        self.actor.train()
+
+        self.buf = GAE_Buffer(state_dim, action_dim, BATCH_SIZE, GAMMA, LAM)
         self.critic_optimizer = tf.optimizers.Adam(learning_rate=VF_LR)
+        self.action_bound = action_bound
 
-    # Every step, get: action, value, logprob, & info for pdist (for computing kl div)
-    def get_action_ops(self, states):
+    def get_action(self, state, greedy=False):
         """
         get action
-        :param states: state input
-        :return: pi, v, logp_pi and other outputs
+        :param state: state input
+        :param greedy: get action greedy or not
+        :return: pi, v, logp_pi, mean, log_std
         """
-        pi, logp_pi, info, *_ = self.actor.cal_outputs_0(states)
-        v = self.critic.critic_cal_func(states)
-        res0 = [pi, v, logp_pi] + values_as_sorted_list(info)
-        res = []
-        for i in res0:
-            res.append(i + 0)  # transfer to tensor
-        return res
+        state = np.array([state], np.float32)
+        mean = self.actor(state)
+        log_std = tf.convert_to_tensor(self.actor.log_std)
+        std = tf.exp(log_std)
+        std = tf.ones_like(mean) * std
+        pi = tfp.distributions.Normal(mean, std)
+
+        if greedy:
+            action = mean
+        else:
+            action = pi.sample()
+        action = np.clip(action, -self.action_bound, self.action_bound)
+        logp_pi = pi.log_prob(action)
 
-    # TRPO losses
-    def pi_loss(self, inputs):
+        value = self.critic(state)
+        return action[0], value, logp_pi, mean, log_std
+
+    def pi_loss(self, states, actions, adv, old_log_prob):
         """
         calculate pi loss
-        :param inputs: a list of x_ph, a_ph, adv_ph, ret_ph, logp_old_ph and other inputs
+        :param states: state batch
+        :param actions: action batch
+        :param adv: advantage batch
+        :param old_log_prob: old log probability
         :return: pi loss
         """
-        x_ph, a_ph, adv_ph, ret_ph, logp_old_ph, *info_values = inputs
-
-        pi, logp, logp_pi, info, info_phs, d_kl = self.actor.cal_outputs_1(x_ph, a_ph, *info_values)
-        ratio = tf.exp(logp - logp_old_ph)  # pi(a|s) / pi_old(a|s)
-        pi_loss = -tf.reduce_mean(ratio * adv_ph)
-        return pi_loss
-
-    def v_loss(self, inputs):
+        mean = self.actor(states)
+        pi = tfp.distributions.Normal(mean, tf.exp(self.actor.log_std))
+        log_prob = pi.log_prob(actions)[:, 0]
+        ratio = tf.exp(log_prob - old_log_prob)
+        surr = tf.reduce_mean(ratio * adv)
+        return -surr
+
+    def gradient(self, states, actions, adv, old_log_prob):
         """
-        calculate value loss
-        :param inputs: a list of x_ph, a_ph, adv_ph, ret_ph, logp_old_ph and other inputs
-        :return: v loss
+        pi gradients
+        :param states: state batch
+        :param actions: actions batch
+        :param adv: advantage batch
+        :param old_log_prob: old log probability batch
+        :return: gradient
         """
-        x_ph, a_ph, adv_ph, ret_ph, logp_old_ph, *info_values = inputs
-        v = self.critic.critic_cal_func(x_ph)
-        v_loss = tf.reduce_mean((ret_ph - v)**2)
-        return v_loss
+        pi_params = self.actor.trainable_weights
+        with tf.GradientTape() as tape:
+            loss = self.pi_loss(states, actions, adv, old_log_prob)
+        grad = tape.gradient(loss, pi_params)
+        gradient = self._flat_concat(grad)
+        return gradient, loss
 
-    def train_vf(self, inputs):
+    def train_vf(self, states, rewards_to_go):
         """
         train v function
-        :param inputs: a list of x_ph, a_ph, adv_ph, ret_ph, logp_old_ph and other inputs
+        :param states: state batch
+        :param rewards_to_go: rewards-to-go batch
         :return: None
         """
         with tf.GradientTape() as tape:
-            loss = self.v_loss(inputs)
-        grad = tape.gradient(loss, self.critic.model.trainable_weights)
-        self.critic_optimizer.apply_gradients(zip(grad, self.critic.model.trainable_weights))
+            value = self.critic(states)
+            loss = tf.reduce_mean((rewards_to_go - value[:, 0])**2)
+        grad = tape.gradient(loss, self.critic.trainable_weights)
+        self.critic_optimizer.apply_gradients(zip(grad, self.critic.trainable_weights))
 
-    # Symbols needed for CG solver
-    def gradient(self, inputs):
+    def kl(self, states, old_mean, old_log_std):
         """
-        pi gradients
-        :param inputs: a list of x_ph, a_ph, adv_ph, ret_ph, logp_old_ph and other inputs
-        :return: gradient
+        calculate kl-divergence
+        :param states: state batch
+        :param old_mean: mean batch of the old pi
+        :param old_log_std: log std batch of the old pi
+        :return: kl_mean or None
         """
-        pi_params = self.actor.model.trainable_weights
-        with tf.GradientTape() as tape:
-            loss = self.pi_loss(inputs)
-        grad = tape.gradient(loss, pi_params)
-        gradient = flat_concat(grad)
-        return gradient
+        old_mean = old_mean[:, np.newaxis]
+        old_log_std = old_log_std[:, np.newaxis]
+        old_std = tf.exp(old_log_std)
+        old_pi = tfp.distributions.Normal(old_mean, old_std)
 
-    def hvp(self, inputs, v_ph):
-        """
-        calculate hvp
-        :param inputs: a list of x_ph, a_ph, adv_ph, ret_ph, logp_old_ph and other inputs
-        :param v_ph: v input
-        :return: hvp
-        """
-        pi_params = self.actor.model.trainable_weights
-        x_ph, a_ph, adv_ph, ret_ph, logp_old_ph, *info_values = inputs
+        mean = self.actor(states)
+        std = tf.exp(self.actor.log_std) * tf.ones_like(mean)
+        pi = tfp.distributions.Normal(mean, std)
 
-        with tf.GradientTape() as tape1:
-            with tf.GradientTape() as tape0:
-                pi, logp, logp_pi, info, info_phs, d_kl = self.actor.cal_outputs_1(x_ph, a_ph, *info_values)
-            g = flat_concat(tape0.gradient(d_kl, pi_params))
-            l = tf.reduce_sum(g * v_ph)
-        hvp = flat_concat(tape1.gradient(l, pi_params))
+        kl = tfp.distributions.kl_divergence(pi, old_pi)
+        all_kls = tf.reduce_sum(kl, axis=1)
+        return tf.reduce_mean(all_kls)
 
-        if DAMPING_COEFF > 0:
-            hvp += DAMPING_COEFF * v_ph
-        return hvp
+    def _flat_concat(self, xs):
+        """
+        flat concat input
+        :param xs: a list of tensor
+        :return: flat tensor
+        """
+        return tf.concat([tf.reshape(x, (-1, )) for x in xs], axis=0)
 
-    # Symbols for getting and setting params
     def get_pi_params(self):
         """
         get actor trainable parameters
         :return: flat actor trainable parameters
         """
-        pi_params = self.actor.model.trainable_weights
-        return flat_concat(pi_params)
+        pi_params = self.actor.trainable_weights
+        return self._flat_concat(pi_params)
 
-    def set_pi_params(self, v_ph):
+    def set_pi_params(self, flat_params):
         """
         set actor trainable parameters
-        :param v_ph: inputs
+        :param flat_params: inputs
         :return: None
         """
-        pi_params = self.actor.model.trainable_weights
-        assign_params_from_flat(v_ph, pi_params)
+        pi_params = self.actor.trainable_weights
+        flat_size = lambda p: int(np.prod(p.shape.as_list()))  # the 'int' is important for scalars
+        splits = tf.split(flat_params, [flat_size(p) for p in pi_params])
+        new_params = [tf.reshape(p_new, p.shape) for p, p_new in zip(pi_params, splits)]
+        return tf.group([p.assign(p_new) for p, p_new in zip(pi_params, new_params)])
 
-    def save_ckpt(self):
+    def save(self):
         """
         save trained weights
         :return: None
         """
-        if not os.path.exists('model'):
-            os.makedirs('model')
-
-        tl.files.save_weights_to_hdf5('model/trpo_actor.hdf5', self.actor.model)
-        tl.files.save_weights_to_hdf5('model/trpo_critic.hdf5', self.critic.model)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'critic.hdf5'), self.critic)
 
-    def load_ckpt(self):
+    def load(self):
         """
         load trained weights
         :return: None
         """
-        tl.files.load_hdf5_to_weights_in_order('model/trpo_actor.hdf5', self.actor.model)
-        tl.files.load_hdf5_to_weights_in_order('model/trpo_critic.hdf5', self.critic.model)
+        path = os.path.join('model', '_'.join([ALG_NAME, ENV_ID]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'actor.hdf5'), self.actor)
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'critic.hdf5'), self.critic)
 
     def cg(self, Ax, b):
         """
@@ -631,117 +367,146 @@ def cg(self, Ax, b):
             r_dot_old = r_dot_new
         return x
 
+    def hvp(self, states, old_mean, old_log_std, x):
+        """
+        calculate Hessian-vector product
+        :param states: state batch
+        :param old_mean: mean batch of the old pi
+        :param old_log_std: log std batch of the old pi
+        :return: hvp
+        """
+        pi_params = self.actor.trainable_weights
+        with tf.GradientTape() as tape1:
+            with tf.GradientTape() as tape0:
+                d_kl = self.kl(states, old_mean, old_log_std)
+            g = self._flat_concat(tape0.gradient(d_kl, pi_params))
+            l = tf.reduce_sum(g * x)
+        hvp = self._flat_concat(tape1.gradient(l, pi_params))
+
+        if DAMPING_COEFF > 0:
+            hvp += DAMPING_COEFF * x
+        return hvp
+
     def update(self):
         """
         update trpo
-        :return:
+        :return: None
         """
-        # Prepare hessian func, gradient eval
-        inputs = self.buf.get()
-        Hx = lambda x: self.hvp(inputs, x)
-        g, pi_l_old, v_l_old = self.gradient(inputs), self.pi_loss(inputs), self.v_loss(inputs)
+        states, actions, adv, rewards_to_go, logp_old_ph, old_mu, old_log_std = self.buf.get()
+        g, pi_l_old = self.gradient(states, actions, adv, logp_old_ph)
 
-        # Core calculations for TRPO or NPG
+        Hx = lambda x: self.hvp(states, old_mu, old_log_std, x)
         x = self.cg(Hx, g)
+
         alpha = np.sqrt(2 * DELTA / (np.dot(x, Hx(x)) + EPS))
         old_params = self.get_pi_params()
 
         def set_and_eval(step):
-            aa = alpha * x * step
-            par = old_params - aa
-            self.set_pi_params(par)
-            x_ph, a_ph, adv_ph, ret_ph, logp_old_ph, *info_values = inputs
-            pi, logp, logp_pi, info, info_phs, d_kl = self.actor.cal_outputs_1(x_ph, a_ph, *info_values)
-            loss = self.pi_loss(inputs)
+            params = old_params - alpha * x * step
+            self.set_pi_params(params)
+            d_kl = self.kl(states, old_mu, old_log_std)
+            loss = self.pi_loss(states, actions, adv, logp_old_ph)
             return [d_kl, loss]
 
-        # trpo augments npg with backtracking line search, hard kl
+        # trpo with backtracking line search, hard kl
         for j in range(BACKTRACK_ITERS):
             kl, pi_l_new = set_and_eval(step=BACKTRACK_COEFF**j)
             if kl <= DELTA and pi_l_new <= pi_l_old:
                 # Accepting new params at step of line search
                 break
-
-            if j == BACKTRACK_ITERS - 1:
-                # Line search failed! Keeping old params.
-                kl, pi_l_new = set_and_eval(step=0.)
+        else:
+            # Line search failed! Keeping old params.
+            set_and_eval(step=0.)
 
         # Value function updates
-        for _ in range(TRAIN_V_ITERS):
-            self.train_vf(inputs)
+        for _ in range(TRAIN_VF_ITERS):
+            self.train_vf(states, rewards_to_go)
+
+    def finish_path(self, done, next_state):
+        """
+        finish a trajectory
+        :param done: whether the epoch is done
+        :param next_state: next state
+        :return: None
+        """
+        if not done:
+            next_state = np.array([next_state], np.float32)
+            last_val = self.critic(next_state)
+        else:
+            last_val = 0
+        self.buf.finish_path(last_val)
 
 
 if __name__ == '__main__':
-
-    tf.random.set_seed(SEED)
-    np.random.seed(SEED)
-
-    env = gym.make(ENV_NAME)
-    env.seed(SEED)
-
-    agent = TRPO(env.observation_space, env.action_space)
-
-    if args.train:
-        start_time = time.time()
-        o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
-
-        reward_list = []
-        # Main loop: collect experience in env and update/log each epoch
-        for epoch in range(EPOCHS):
-            t0 = time.time()
-            rew = 0
-            for t in range(STEPS_PER_EPOCH):
-                agent_outs = agent.get_action_ops(o.reshape(1, -1))
-                a, v_t, logp_t, info_t = np.array(agent_outs[0][0], np.float32), \
-                                         np.array(agent_outs[1], np.float32), \
-                                         np.array(agent_outs[2], np.float32), \
-                                         np.array(agent_outs[3:], np.float32)
-
-                # save and log
-                agent.buf.store(o, a, r, v_t, logp_t, info_t)
-
-                o, r, d, _ = env.step(a)
-                ep_ret += r
-                ep_len += 1
-
-                terminal = d or (ep_len == MAX_EP_LEN)
-                if terminal or (t == STEPS_PER_EPOCH - 1):
-                    if not (terminal):
-                        print('Warning: trajectory cut off by epoch at %d steps.' % ep_len)
-                    # if trajectory didn't reach terminal state, bootstrap value target
-                    last_val = r if d else agent.critic.critic_cal_func(o.reshape(1, -1))
-                    agent.buf.finish_path(last_val)
-                    rew = ep_ret
-                    o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
-
-            # Save model
-            if (epoch % SAVE_FREQ == 0) or (epoch == EPOCHS - 1):
-                agent.save_ckpt()
-
-            # Perform TRPO or NPG update!
-            agent.update()
-            print('epoch [{}/{}] ep_ret: {} time: {}'.format(epoch, EPOCHS, rew, time.time() - t0))
-
-            reward_list.append(rew)
-            plt.clf()
-            plt.ion()
-            plt.plot(reward_list)
-            plt.title('TRPO ' + str(DELTA))
-            plt.ylim(-2000, 0)
-            plt.show()
-            plt.pause(0.1)
-        agent.save_ckpt()
-        plt.ioff()
-        plt.show()
-
-    # test
-    agent.load_ckpt()
-    while True:
-        o = env.reset()
-        for i in range(STEPS_PER_EPOCH):
-            env.render()
-            agent_outs = agent.get_action_ops(o.reshape(1, -1))
-            a, v_t, logp_t, info_t = agent_outs[0][0], agent_outs[1], agent_outs[2], agent_outs[3:]
-            o, r, d, _ = env.step(a)
-            if d:
-                break
+    env = gym.make(ENV_ID).unwrapped
+
+    # reproducible
+    np.random.seed(RANDOM_SEED)
+    tf.random.set_seed(RANDOM_SEED)
+    env.seed(RANDOM_SEED)
+
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    action_bound = env.action_space.high
+
+    agent = TRPO(state_dim, action_dim, action_bound)
+
+    t0 = time.time()
+    if args.train:  # train
+        all_episode_reward = []
+        for episode in range(TRAIN_EPISODES):
+            state = env.reset()
+            state = np.array(state, np.float32)
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                if RENDER:
+                    env.render()
+                action, value, logp, mean, log_std = agent.get_action(state)
+                next_state, reward, done, _ = env.step(action)
+                next_state = np.array(next_state, np.float32)
+                agent.buf.store(state, action, reward, value, logp, mean, log_std)
+                episode_reward += reward
+                state = next_state
+                if agent.buf.is_full():
+                    agent.finish_path(done, next_state)
+                    agent.update()
+                if done:
+                    break
+            agent.finish_path(done, next_state)
+            if episode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            print(
+                'Training  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TRAIN_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
+            if episode % SAVE_FREQ == 0:
+                agent.save()
+        agent.save()
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([ALG_NAME, ENV_ID])))
+
+    if args.test:
+        # test
+        agent.load()
+        for episode in range(TEST_EPISODES):
+            state = env.reset()
+            episode_reward = 0
+            for step in range(MAX_STEPS):
+                env.render()
+                action, *_ = agent.get_action(state, greedy=True)
+                state, reward, done, info = env.step(action)
+                episode_reward += reward
+                if done:
+                    break
+            print(
+                'Testing  | Episode: {}/{}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    episode + 1, TEST_EPISODES, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_atari_pong.py b/examples/reinforcement_learning/tutorial_atari_pong.py
index 0ffee9174..e28f70bec 100644
--- a/examples/reinforcement_learning/tutorial_atari_pong.py
+++ b/examples/reinforcement_learning/tutorial_atari_pong.py
@@ -1,21 +1,16 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 """Monte-Carlo Policy Network π(a|s)  (REINFORCE).
-
 To understand Reinforcement Learning, we let computer to learn how to play
 Pong game from the original screen inputs. Before we start, we highly recommend
 you to go through a famous blog called “Deep Reinforcement Learning: Pong from
 Pixels” which is a minimalistic implementation of deep reinforcement learning by
 using python-numpy and OpenAI gym environment.
-
 The code here is the reimplementation of Karpathy's Blog by using TensorLayer.
-
-Compare with Karpathy's code, we store observation for a batch, he store
-observation for a episode only, they store gradients instead. (so we will use
+Compare with Karpathy's code, we store observation for a batch, but he store
+observation for only one episode and gradients. (so we will use
 more memory if the observation is very large.)
 
-FEEL FREE TO JOIN US !
-
 TODO
 -----
 - update grads every step rather than storing all observation!
@@ -24,14 +19,13 @@
 References
 ------------
 - http://karpathy.github.io/2016/05/31/rl/
-
 """
 import time
 
-import numpy as np
-
 import gym
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tl.logging.set_verbosity(tl.logging.DEBUG)
diff --git a/examples/reinforcement_learning/tutorial_format.py b/examples/reinforcement_learning/tutorial_format.py
index f3e9a7e50..cd27ef2c4 100644
--- a/examples/reinforcement_learning/tutorial_format.py
+++ b/examples/reinforcement_learning/tutorial_format.py
@@ -31,7 +31,6 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
 
 # import 'other package name'
diff --git a/examples/reinforcement_learning/tutorial_prioritized_replay.py b/examples/reinforcement_learning/tutorial_prioritized_replay.py
index 8f5f60404..f2c5745fd 100644
--- a/examples/reinforcement_learning/tutorial_prioritized_replay.py
+++ b/examples/reinforcement_learning/tutorial_prioritized_replay.py
@@ -1,494 +1,527 @@
-"""
-Prioritized Experience Replay
-------------------------
-Prioritized experience replay is an efficient replay method that replay
-important transitions more frequently. Segment tree data structure is used to
-speed up indexing.
-
-
-Reference:
-------------------------
-Schaul T, Quan J, Antonoglou I, et al. Prioritized experience replay[J]. arXiv
-preprint arXiv:1511.05952, 2015.
-
-Dhariwal P, Hesse C, Klimov O, et al. Openai baselines (2017)[J]. URL
-https://github. com/opfenai/baselines.
-
-
-Environment:
-------------------------
-Cartpole and Pong in OpenAI Gym
-
-
-Requirements:
-------------------------
-tensorflow>=2.0.0a0
-tensorlayer>=2.0.0
-
-
-To run:
-------------------------
-python tutorial_prioritized_replay.py --mode=train
-python tutorial_prioritized_replay.py --mode=test --save_path=per/8000.npz
-"""
-import argparse
-import operator
-import os
-import random
-import time
-
-import numpy as np
-
-import tensorflow as tf
-import tensorlayer as tl
-from tutorial_wrappers import build_env
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--mode', help='train or test', default='train')
-parser.add_argument(
-    '--save_path', default='per', help='folder to save if mode == train else model path,'
-    'qnet will be saved once target net update'
-)
-parser.add_argument('--seed', help='random seed', type=int, default=0)
-parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
-args = parser.parse_args()
-
-if args.mode == 'train':
-    os.makedirs(args.save_path, exist_ok=True)
-random.seed(args.seed)
-np.random.seed(args.seed)
-tf.random.set_seed(args.seed)  # reproducible
-env_id = args.env_id
-env = build_env(env_id, seed=args.seed)
-
-# ####################  hyper parameters  ####################
-if env_id == 'CartPole-v0':
-    qnet_type = 'MLP'
-    number_timesteps = 10000  # total number of time steps to train on
-    explore_timesteps = 100
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 5e-3  # learning rate
-    buffer_size = 1000  # replay buffer size
-    target_q_update_freq = 50  # how frequency target q net update
-    ob_scale = 1.0  # scale observations
-else:
-    # reward will increase obviously after 1e5 time steps
-    qnet_type = 'CNN'
-    number_timesteps = int(1e6)  # total number of time steps to train on
-    explore_timesteps = 1e5
-    # epsilon-greedy schedule, final exploit prob is 0.99
-    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
-    lr = 1e-4  # learning rate
-    buffer_size = 10000  # replay buffer size
-    target_q_update_freq = 200  # how frequency target q net update
-    ob_scale = 1.0 / 255  # scale observations
-
-in_dim = env.observation_space.shape
-out_dim = env.action_space.n
-reward_gamma = 0.99  # reward discount
-batch_size = 32  # batch size for sampling from replay buffer
-warm_start = buffer_size / 10  # sample times befor learning
-prioritized_replay_alpha = 0.6  # alpha in PER
-prioritized_replay_beta0 = 0.4  # initial beta in PER
-
-
-# ##############################  PER  ####################################
-class MLP(tl.models.Model):
-
-    def __init__(self, name):
-        super(MLP, self).__init__(name=name)
-        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
-
-    def forward(self, ni):
-        return self.qvalue(self.h1(ni))
-
-
-class CNN(tl.models.Model):
-
-    def __init__(self, name):
-        super(CNN, self).__init__(name=name)
-        h, w, in_channels = in_dim
-        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
-        self.conv1 = tl.layers.Conv2d(
-            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv2 = tl.layers.Conv2d(
-            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.conv3 = tl.layers.Conv2d(
-            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
-            W_init=tf.initializers.GlorotUniform()
-        )
-        self.flatten = tl.layers.Flatten(name='flatten')
-        self.preq = tl.layers.Dense(
-            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
-        )
-        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
-
-    def forward(self, ni):
-        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
-        return self.qvalue(self.preq(feature))
-
-
-class SegmentTree(object):
-
-    def __init__(self, capacity, operation, neutral_element):
-        """Build a Segment Tree data structure.
-
-        https://en.wikipedia.org/wiki/Segment_tree
-
-        Can be used as regular array, but with two
-        important differences:
-
-            a) setting item's value is slightly slower.
-               It is O(lg capacity) instead of O(1).
-            b) user has access to an efficient ( O(log segment size) )
-               `reduce` operation which reduces `operation` over
-               a contiguous subsequence of items in the array.
-
-        Paramters
-        ---------
-        capacity: int
-            Total size of the array - must be a power of two.
-        operation: lambda obj, obj -> obj
-            and operation for combining elements (eg. sum, max)
-            must form a mathematical group together with the set of
-            possible values for array elements (i.e. be associative)
-        neutral_element: obj
-            neutral element for the operation above. eg. float('-inf')
-            for max and 0 for sum.
-        """
-        assert capacity > 0 and capacity & (capacity - 1) == 0, \
-            "capacity must be positive and a power of 2."
-        self._capacity = capacity
-        self._value = [neutral_element for _ in range(2 * capacity)]
-        self._operation = operation
-
-    def _reduce_helper(self, start, end, node, node_start, node_end):
-        if start == node_start and end == node_end:
-            return self._value[node]
-        mid = (node_start + node_end) // 2
-        if end <= mid:
-            return self._reduce_helper(start, end, 2 * node, node_start, mid)
-        else:
-            if mid + 1 <= start:
-                return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end)
-            else:
-                return self._operation(
-                    self._reduce_helper(start, mid, 2 * node, node_start, mid),
-                    self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end)
-                )
-
-    def reduce(self, start=0, end=None):
-        """Returns result of applying `self.operation`
-        to a contiguous subsequence of the array.
-
-        Parameters
-        ----------
-        start: int
-            beginning of the subsequence
-        end: int
-            end of the subsequences
-
-        Returns
-        -------
-        reduced: obj
-            result of reducing self.operation over the specified range of array.
-        """
-        if end is None:
-            end = self._capacity
-        if end < 0:
-            end += self._capacity
-        end -= 1
-        return self._reduce_helper(start, end, 1, 0, self._capacity - 1)
-
-    def __setitem__(self, idx, val):
-        # index of the leaf
-        idx += self._capacity
-        self._value[idx] = val
-        idx //= 2
-        while idx >= 1:
-            self._value[idx] = self._operation(self._value[2 * idx], self._value[2 * idx + 1])
-            idx //= 2
-
-    def __getitem__(self, idx):
-        assert 0 <= idx < self._capacity
-        return self._value[self._capacity + idx]
-
-
-class SumSegmentTree(SegmentTree):
-
-    def __init__(self, capacity):
-        super(SumSegmentTree, self).__init__(capacity=capacity, operation=operator.add, neutral_element=0.0)
-
-    def sum(self, start=0, end=None):
-        """Returns arr[start] + ... + arr[end]"""
-        return super(SumSegmentTree, self).reduce(start, end)
-
-    def find_prefixsum_idx(self, prefixsum):
-        """Find the highest index `i` in the array such that
-            sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
-
-        if array values are probabilities, this function
-        allows to sample indexes according to the discrete
-        probability efficiently.
-
-        Parameters
-        ----------
-        perfixsum: float
-            upperbound on the sum of array prefix
-
-        Returns
-        -------
-        idx: int
-            highest index satisfying the prefixsum constraint
-        """
-        assert 0 <= prefixsum <= self.sum() + 1e-5
-        idx = 1
-        while idx < self._capacity:  # while non-leaf
-            if self._value[2 * idx] > prefixsum:
-                idx = 2 * idx
-            else:
-                prefixsum -= self._value[2 * idx]
-                idx = 2 * idx + 1
-        return idx - self._capacity
-
-
-class MinSegmentTree(SegmentTree):
-
-    def __init__(self, capacity):
-        super(MinSegmentTree, self).__init__(capacity=capacity, operation=min, neutral_element=float('inf'))
-
-    def min(self, start=0, end=None):
-        """Returns min(arr[start], ...,  arr[end])"""
-
-        return super(MinSegmentTree, self).reduce(start, end)
-
-
-class ReplayBuffer(object):
-
-    def __init__(self, size):
-        self._storage = []
-        self._maxsize = size
-        self._next_idx = 0
-
-    def __len__(self):
-        return len(self._storage)
-
-    def add(self, *args):
-        if self._next_idx >= len(self._storage):
-            self._storage.append(args)
-        else:
-            self._storage[self._next_idx] = args
-        self._next_idx = (self._next_idx + 1) % self._maxsize
-
-    def _encode_sample(self, idxes):
-        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
-        for i in idxes:
-            o, a, r, o_, d = self._storage[i]
-            b_o.append(o)
-            b_a.append(a)
-            b_r.append(r)
-            b_o_.append(o_)
-            b_d.append(d)
-        return (
-            np.stack(b_o).astype('float32') * ob_scale,
-            np.stack(b_a).astype('int32'),
-            np.stack(b_r).astype('float32'),
-            np.stack(b_o_).astype('float32') * ob_scale,
-            np.stack(b_d).astype('float32'),
-        )
-
-    def sample(self, batch_size):
-        indexes = range(len(self._storage))
-        idxes = [random.choice(indexes) for _ in range(batch_size)]
-        return self._encode_sample(idxes)
-
-
-class PrioritizedReplayBuffer(ReplayBuffer):
-
-    def __init__(self, size, alpha, beta):
-        """Create Prioritized Replay buffer.
-
-        Parameters
-        ----------
-        size: int
-            Max number of transitions to store in the buffer. When the buffer
-            overflows the old memories are dropped.
-        alpha: float
-            how much prioritization is used
-            (0 - no prioritization, 1 - full prioritization)
-
-        See Also
-        --------
-        ReplayBuffer.__init__
-        """
-        super(PrioritizedReplayBuffer, self).__init__(size)
-        assert alpha >= 0
-        self._alpha = alpha
-
-        it_capacity = 1
-        while it_capacity < size:
-            it_capacity *= 2
-
-        self._it_sum = SumSegmentTree(it_capacity)
-        self._it_min = MinSegmentTree(it_capacity)
-        self._max_priority = 1.0
-        self.beta = beta
-
-    def add(self, *args):
-        """See ReplayBuffer.store_effect"""
-        idx = self._next_idx
-        super().add(*args)
-        self._it_sum[idx] = self._max_priority**self._alpha
-        self._it_min[idx] = self._max_priority**self._alpha
-
-    def _sample_proportional(self, batch_size):
-        res = []
-        p_total = self._it_sum.sum(0, len(self._storage) - 1)
-        every_range_len = p_total / batch_size
-        for i in range(batch_size):
-            mass = random.random() * every_range_len + i * every_range_len
-            idx = self._it_sum.find_prefixsum_idx(mass)
-            res.append(idx)
-        return res
-
-    def sample(self, batch_size):
-        """Sample a batch of experiences"""
-        idxes = self._sample_proportional(batch_size)
-
-        it_sum = self._it_sum.sum()
-        p_min = self._it_min.min() / it_sum
-        max_weight = (p_min * len(self._storage))**(-self.beta)
-
-        p_samples = np.asarray([self._it_sum[idx] for idx in idxes]) / it_sum
-        weights = (p_samples * len(self._storage))**(-self.beta) / max_weight
-        encoded_sample = self._encode_sample(idxes)
-        return encoded_sample + (weights, idxes)
-
-    def update_priorities(self, idxes, priorities):
-        """Update priorities of sampled transitions"""
-        assert len(idxes) == len(priorities)
-        for idx, priority in zip(idxes, priorities):
-            assert priority > 0
-            assert 0 <= idx < len(self._storage)
-            self._it_sum[idx] = priority**self._alpha
-            self._it_min[idx] = priority**self._alpha
-
-            self._max_priority = max(self._max_priority, priority)
-
-
-def huber_loss(x):
-    """Loss function for value"""
-    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
-
-
-def sync(net, net_tar):
-    """Copy q network to target q network"""
-    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
-        var_tar.assign(var)
-
-
-if __name__ == '__main__':
-    if args.mode == 'train':
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        qnet.train()
-        trainabel_weights = qnet.trainable_weights
-        targetqnet = MLP('targetq') if qnet_type == 'MLP' else CNN('targetq')
-        targetqnet.infer()
-        sync(qnet, targetqnet)
-        optimizer = tf.optimizers.Adam(learning_rate=lr)
-        buffer = PrioritizedReplayBuffer(buffer_size, prioritized_replay_alpha, prioritized_replay_beta0)
-
-        o = env.reset()
-        nepisode = 0
-        t = time.time()
-        for i in range(1, number_timesteps + 1):
-            eps = epsilon(i)
-            buffer.beta += (1 - prioritized_replay_beta0) / number_timesteps
-
-            # select action
-            if random.random() < eps:
-                a = int(random.random() * out_dim)
-            else:
-                obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-                a = qnet(obv).numpy().argmax(1)[0]
-
-            # execute action and feed to replay buffer
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-            buffer.add(o, a, r, o_, done)
-
-            if i >= warm_start:
-                # sync q net and target q net
-                if i % target_q_update_freq == 0:
-                    sync(qnet, targetqnet)
-                    path = os.path.join(args.save_path, '{}.npz'.format(i))
-                    tl.files.save_npz(qnet.trainable_weights, name=path)
-
-                # sample from replay buffer
-                b_o, b_a, b_r, b_o_, b_d, weights, idxs \
-                    = buffer.sample(batch_size)
-
-                # q estimation
-                b_q_ = (1 - b_d) * tf.reduce_max(targetqnet(b_o_), 1)
-
-                # calculate loss
-                with tf.GradientTape() as q_tape:
-                    b_q = tf.reduce_sum(qnet(b_o) * tf.one_hot(b_a, out_dim), 1)
-                    abs_td_error = tf.abs(b_q - (b_r + reward_gamma * b_q_))
-                    priorities = np.clip(abs_td_error.numpy(), 1e-6, None)
-                    buffer.update_priorities(idxs, priorities)
-                    loss = tf.reduce_mean(weights * huber_loss(abs_td_error))
-
-                # backward gradients
-                q_grad = q_tape.gradient(loss, trainabel_weights)
-                optimizer.apply_gradients(zip(q_grad, trainabel_weights))
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                fps = int(length / (time.time() - t))
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}, FPS: {}'.format(i, nepisode, reward, length, fps)
-                )
-                t = time.time()
-    else:
-        qnet = MLP('q') if qnet_type == 'MLP' else CNN('q')
-        tl.files.load_and_assign_npz(name=args.save_path, network=qnet)
-        qnet.eval()
-
-        nepisode = 0
-        o = env.reset()
-        for i in range(1, number_timesteps + 1):
-            obv = np.expand_dims(o, 0).astype('float32') * ob_scale
-            a = qnet(obv).numpy().argmax(1)[0]
-
-            # execute action
-            # note that `_` tail in var name means next
-            o_, r, done, info = env.step(a)
-
-            if done:
-                o = env.reset()
-            else:
-                o = o_
-
-            # episode in info is real (unwrapped) message
-            if info.get('episode'):
-                nepisode += 1
-                reward, length = info['episode']['r'], info['episode']['l']
-                print(
-                    'Time steps so far: {}, episode so far: {}, '
-                    'episode reward: {:.4f}, episode length: {}'.format(i, nepisode, reward, length)
-                )
+"""
+Prioritized Experience Replay
+------------------------
+Prioritized experience replay is an efficient replay method that replay
+important transitions more frequently. Segment tree data structure is used to
+speed up indexing.
+Reference:
+------------------------
+Schaul T, Quan J, Antonoglou I, et al. Prioritized experience replay[J]. arXiv
+preprint arXiv:1511.05952, 2015.
+Dhariwal P, Hesse C, Klimov O, et al. Openai baselines (2017)[J]. URL
+https://github. com/opfenai/baselines.
+Environment:
+------------------------
+Cartpole and Pong in OpenAI Gym
+Requirements:
+------------------------
+tensorflow>=2.0.0a0
+tensorlayer>=2.0.0
+To run:
+------------------------
+python tutorial_prioritized_replay.py --mode=train
+python tutorial_prioritized_replay.py --mode=test --save_path=per/8000.npz
+"""
+import argparse
+import operator
+import os
+import random
+import time
+
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+import tensorlayer as tl
+
+parser = argparse.ArgumentParser()
+# add arguments in command  --train/test
+parser.add_argument('--train', dest='train', action='store_true', default=True)
+parser.add_argument('--test', dest='test', action='store_true', default=True)
+parser.add_argument(
+    '--save_path', default=None, help='folder to save if mode == train else model path,'
+    'qnet will be saved once target net update'
+)
+parser.add_argument('--seed', help='random seed', type=int, default=0)
+parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
+args = parser.parse_args()
+
+random.seed(args.seed)
+np.random.seed(args.seed)
+tf.random.set_seed(args.seed)  # reproducible
+env_id = args.env_id
+env = gym.make(env_id)
+env.seed(args.seed)
+alg_name = 'prioritized_replay'
+
+# ####################  hyper parameters  ####################
+if env_id == 'CartPole-v0':
+    qnet_type = 'MLP'
+    number_timesteps = 10000  # total number of time steps to train on
+    explore_timesteps = 100
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 5e-3  # learning rate
+    buffer_size = 1000  # replay buffer size
+    target_q_update_freq = 50  # how frequency target q net update
+    ob_scale = 1.0  # scale observations
+    clipnorm = None
+else:
+    # reward will increase obviously after 1e5 time steps
+    qnet_type = 'CNN'
+    number_timesteps = int(1e6)  # total number of time steps to train on
+    explore_timesteps = 1e5
+    # epsilon-greedy schedule, final exploit prob is 0.99
+    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
+    lr = 1e-4  # learning rate
+    buffer_size = 10000  # replay buffer size
+    target_q_update_freq = 200  # how frequency target q net update
+    ob_scale = 1.0 / 255  # scale observations
+    clipnorm = 10
+
+in_dim = env.observation_space.shape
+out_dim = env.action_space.n
+reward_gamma = 0.99  # reward discount
+batch_size = 32  # batch size for sampling from replay buffer
+warm_start = buffer_size / 10  # sample times befor learning
+prioritized_replay_alpha = 0.6  # alpha in PER
+prioritized_replay_beta0 = 0.4  # initial beta in PER
+
+
+# ##############################  Network  ####################################
+class MLP(tl.models.Model):
+
+    def __init__(self, name):
+        super(MLP, self).__init__(name=name)
+        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
+        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
+
+    def forward(self, ni):
+        return self.qvalue(self.h1(ni))
+
+
+class CNN(tl.models.Model):
+
+    def __init__(self, name):
+        super(CNN, self).__init__(name=name)
+        h, w, in_channels = in_dim
+        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
+        self.conv1 = tl.layers.Conv2d(
+            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv2 = tl.layers.Conv2d(
+            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.conv3 = tl.layers.Conv2d(
+            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
+            W_init=tf.initializers.GlorotUniform()
+        )
+        self.flatten = tl.layers.Flatten(name='flatten')
+        self.preq = tl.layers.Dense(
+            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
+        )
+        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
+
+    def forward(self, ni):
+        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))
+        return self.qvalue(self.preq(feature))
+
+
+# ##############################  Replay  ####################################
+class SegmentTree(object):
+
+    def __init__(self, capacity, operation, neutral_element):
+        """Build a Segment Tree data structure.
+        https://en.wikipedia.org/wiki/Segment_tree
+        Can be used as regular array, but with two
+        important differences:
+            a) setting item's value is slightly slower.
+               It is O(lg capacity) instead of O(1).
+            b) user has access to an efficient ( O(log segment size) )
+               `reduce` operation which reduces `operation` over
+               a contiguous subsequence of items in the array.
+        Paramters
+        ---------
+        capacity: int
+            Total size of the array - must be a power of two.
+        operation: lambda obj, obj -> obj
+            and operation for combining elements (eg. sum, max)
+            must form a mathematical group together with the set of
+            possible values for array elements (i.e. be associative)
+        neutral_element: obj
+            neutral element for the operation above. eg. float('-inf')
+            for max and 0 for sum.
+        """
+        assert capacity > 0 and capacity & (capacity - 1) == 0, \
+            "capacity must be positive and a power of 2."
+        self._capacity = capacity
+        self._value = [neutral_element for _ in range(2 * capacity)]
+        self._operation = operation
+
+    def _reduce_helper(self, start, end, node, node_start, node_end):
+        if start == node_start and end == node_end:
+            return self._value[node]
+        mid = (node_start + node_end) // 2
+        if end <= mid:
+            return self._reduce_helper(start, end, 2 * node, node_start, mid)
+        else:
+            if mid + 1 <= start:
+                return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end)
+            else:
+                return self._operation(
+                    self._reduce_helper(start, mid, 2 * node, node_start, mid),
+                    self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end)
+                )
+
+    def reduce(self, start=0, end=None):
+        """Returns result of applying `self.operation`
+        to a contiguous subsequence of the array.
+        Parameters
+        ----------
+        start: int
+            beginning of the subsequence
+        end: int
+            end of the subsequences
+        Returns
+        -------
+        reduced: obj
+            result of reducing self.operation over the specified range of array.
+        """
+        if end is None:
+            end = self._capacity
+        if end < 0:
+            end += self._capacity
+        end -= 1
+        return self._reduce_helper(start, end, 1, 0, self._capacity - 1)
+
+    def __setitem__(self, idx, val):
+        # index of the leaf
+        idx += self._capacity
+        self._value[idx] = val
+        idx //= 2
+        while idx >= 1:
+            self._value[idx] = self._operation(self._value[2 * idx], self._value[2 * idx + 1])
+            idx //= 2
+
+    def __getitem__(self, idx):
+        assert 0 <= idx < self._capacity
+        return self._value[self._capacity + idx]
+
+
+class SumSegmentTree(SegmentTree):
+
+    def __init__(self, capacity):
+        super(SumSegmentTree, self).__init__(capacity=capacity, operation=operator.add, neutral_element=0.0)
+
+    def sum(self, start=0, end=None):
+        """Returns arr[start] + ... + arr[end]"""
+        return super(SumSegmentTree, self).reduce(start, end)
+
+    def find_prefixsum_idx(self, prefixsum):
+        """Find the highest index `i` in the array such that
+            sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
+        if array values are probabilities, this function
+        allows to sample indexes according to the discrete
+        probability efficiently.
+        Parameters
+        ----------
+        perfixsum: float
+            upperbound on the sum of array prefix
+        Returns
+        -------
+        idx: int
+            highest index satisfying the prefixsum constraint
+        """
+        assert 0 <= prefixsum <= self.sum() + 1e-5
+        idx = 1
+        while idx < self._capacity:  # while non-leaf
+            if self._value[2 * idx] > prefixsum:
+                idx = 2 * idx
+            else:
+                prefixsum -= self._value[2 * idx]
+                idx = 2 * idx + 1
+        return idx - self._capacity
+
+
+class MinSegmentTree(SegmentTree):
+
+    def __init__(self, capacity):
+        super(MinSegmentTree, self).__init__(capacity=capacity, operation=min, neutral_element=float('inf'))
+
+    def min(self, start=0, end=None):
+        """Returns min(arr[start], ...,  arr[end])"""
+
+        return super(MinSegmentTree, self).reduce(start, end)
+
+
+class ReplayBuffer(object):
+
+    def __init__(self, size):
+        self._storage = []
+        self._maxsize = size
+        self._next_idx = 0
+
+    def __len__(self):
+        return len(self._storage)
+
+    def add(self, *args):
+        if self._next_idx >= len(self._storage):
+            self._storage.append(args)
+        else:
+            self._storage[self._next_idx] = args
+        self._next_idx = (self._next_idx + 1) % self._maxsize
+
+    def _encode_sample(self, idxes):
+        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
+        for i in idxes:
+            o, a, r, o_, d = self._storage[i]
+            b_o.append(o)
+            b_a.append(a)
+            b_r.append(r)
+            b_o_.append(o_)
+            b_d.append(d)
+        return (
+            np.stack(b_o).astype('float32') * ob_scale,
+            np.stack(b_a).astype('int32'),
+            np.stack(b_r).astype('float32'),
+            np.stack(b_o_).astype('float32') * ob_scale,
+            np.stack(b_d).astype('float32'),
+        )
+
+    def sample(self, batch_size):
+        indexes = range(len(self._storage))
+        idxes = [random.choice(indexes) for _ in range(batch_size)]
+        return self._encode_sample(idxes)
+
+
+class PrioritizedReplayBuffer(ReplayBuffer):
+
+    def __init__(self, size, alpha, beta):
+        """Create Prioritized Replay buffer.
+        Parameters
+        ----------
+        size: int
+            Max number of transitions to store in the buffer. When the buffer
+            overflows the old memories are dropped.
+        alpha: float
+            how much prioritization is used
+            (0 - no prioritization, 1 - full prioritization)
+        See Also
+        --------
+        ReplayBuffer.__init__
+        """
+        super(PrioritizedReplayBuffer, self).__init__(size)
+        assert alpha >= 0
+        self._alpha = alpha
+
+        it_capacity = 1
+        while it_capacity < size:
+            it_capacity *= 2
+
+        self._it_sum = SumSegmentTree(it_capacity)
+        self._it_min = MinSegmentTree(it_capacity)
+        self._max_priority = 1.0
+        self.beta = beta
+
+    def add(self, *args):
+        """See ReplayBuffer.store_effect"""
+        idx = self._next_idx
+        super().add(*args)
+        self._it_sum[idx] = self._max_priority**self._alpha
+        self._it_min[idx] = self._max_priority**self._alpha
+
+    def _sample_proportional(self, batch_size):
+        res = []
+        p_total = self._it_sum.sum(0, len(self._storage) - 1)
+        every_range_len = p_total / batch_size
+        for i in range(batch_size):
+            mass = random.random() * every_range_len + i * every_range_len
+            idx = self._it_sum.find_prefixsum_idx(mass)
+            res.append(idx)
+        return res
+
+    def sample(self, batch_size):
+        """Sample a batch of experiences"""
+        idxes = self._sample_proportional(batch_size)
+
+        it_sum = self._it_sum.sum()
+        p_min = self._it_min.min() / it_sum
+        max_weight = (p_min * len(self._storage))**(-self.beta)
+
+        p_samples = np.asarray([self._it_sum[idx] for idx in idxes]) / it_sum
+        weights = (p_samples * len(self._storage))**(-self.beta) / max_weight
+        encoded_sample = self._encode_sample(idxes)
+        return encoded_sample + (weights.astype('float32'), idxes)
+
+    def update_priorities(self, idxes, priorities):
+        """Update priorities of sampled transitions"""
+        assert len(idxes) == len(priorities)
+        for idx, priority in zip(idxes, priorities):
+            assert priority > 0
+            assert 0 <= idx < len(self._storage)
+            self._it_sum[idx] = priority**self._alpha
+            self._it_min[idx] = priority**self._alpha
+
+            self._max_priority = max(self._max_priority, priority)
+
+
+# #############################  Functions  ###################################
+def huber_loss(x):
+    """Loss function for value"""
+    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)
+
+
+def sync(net, net_tar):
+    """Copy q network to target q network"""
+    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
+        var_tar.assign(var)
+
+
+# ###############################  DQN  #####################################
+class DQN(object):
+
+    def __init__(self):
+        model = MLP if qnet_type == 'MLP' else CNN
+        self.qnet = model('q')
+        if args.train:
+            self.qnet.train()
+            self.targetqnet = model('targetq')
+            self.targetqnet.infer()
+            sync(self.qnet, self.targetqnet)
+        else:
+            self.qnet.infer()
+            self.load(args.save_path)
+        self.niter = 0
+        if clipnorm is not None:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr, clipnorm=clipnorm)
+        else:
+            self.optimizer = tf.optimizers.Adam(learning_rate=lr)
+
+    def get_action(self, obv):
+        eps = epsilon(self.niter)
+        if args.train and random.random() < eps:
+            return int(random.random() * out_dim)
+        else:
+            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
+            return self._qvalues_func(obv).numpy().argmax(1)[0]
+
+    @tf.function
+    def _qvalues_func(self, obv):
+        return self.qnet(obv)
+
+    def train(self, b_o, b_a, b_r, b_o_, b_d, weights=None):
+        if weights is None:
+            weights = np.ones_like(b_r)
+        td_errors = self._train_func(b_o, b_a, b_r, b_o_, b_d, weights)
+
+        self.niter += 1
+        if self.niter % target_q_update_freq == 0:
+            sync(self.qnet, self.targetqnet)
+            self.save(args.save_path)
+        return td_errors.numpy()
+
+    def save(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        if not os.path.exists(path):
+            os.makedirs(path)
+        tl.files.save_weights_to_hdf5(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+    def load(self, path):
+        if path is None:
+            path = os.path.join('model', '_'.join([alg_name, env_id]))
+        tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'q_net.hdf5'), self.qnet)
+
+    @tf.function
+    def _train_func(self, b_o, b_a, b_r, b_o_, b_d, weights):
+        with tf.GradientTape() as tape:
+            td_errors = self._tderror_func(b_o, b_a, b_r, b_o_, b_d)
+            loss = tf.reduce_mean(huber_loss(td_errors) * weights)
+
+        grad = tape.gradient(loss, self.qnet.trainable_weights)
+        self.optimizer.apply_gradients(zip(grad, self.qnet.trainable_weights))
+
+        return td_errors
+
+    @tf.function
+    def _tderror_func(self, b_o, b_a, b_r, b_o_, b_d):
+        b_q_ = (1 - b_d) * tf.reduce_max(self.targetqnet(b_o_), 1)
+        b_q = tf.reduce_sum(self.qnet(b_o) * tf.one_hot(b_a, out_dim), 1)
+        return b_q - (b_r + reward_gamma * b_q_)
+
+
+# #############################  Trainer  ###################################
+if __name__ == '__main__':
+    dqn = DQN()
+    t0 = time.time()
+    if args.train:
+        buffer = PrioritizedReplayBuffer(buffer_size, prioritized_replay_alpha, prioritized_replay_beta0)
+        nepisode = 0
+        all_episode_reward = []
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                buffer.beta += (1 - prioritized_replay_beta0) / number_timesteps
+
+                a = dqn.get_action(o)
+
+                # execute action and feed to replay buffer
+                # note that `_` tail in var name means next
+                o_, r, done, info = env.step(a)
+                buffer.add(o, a, r, o_, done)
+                episode_reward += r
+
+                if i >= warm_start:
+                    *transitions, idxs = buffer.sample(batch_size)
+                    priorities = dqn.train(*transitions)
+                    priorities = np.clip(np.abs(priorities), 1e-6, None)
+                    buffer.update_priorities(idxs, priorities)
+
+                if done:
+                    break
+                else:
+                    o = o_
+
+            if nepisode == 0:
+                all_episode_reward.append(episode_reward)
+            else:
+                all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)
+            nepisode += 1
+            print(
+                'Training  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )  # episode num starts from 1 in print
+
+        dqn.save(args.save_path)
+        plt.plot(all_episode_reward)
+        if not os.path.exists('image'):
+            os.makedirs('image')
+        plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
+
+    if args.test:
+        nepisode = 0
+        for i in range(1, number_timesteps + 1):
+            o = env.reset()
+            episode_reward = 0
+            while True:
+                env.render()
+                a = dqn.get_action(o)
+                o_, r, done, info = env.step(a)
+                episode_reward += r
+                if done:
+                    break
+                else:
+                    o = o_
+            nepisode += 1
+            print(
+                'Testing  | Episode: {}  | Episode Reward: {:.4f}  | Running Time: {:.4f}'.format(
+                    nepisode, episode_reward,
+                    time.time() - t0
+                )
+            )
diff --git a/examples/reinforcement_learning/tutorial_wrappers.py b/examples/reinforcement_learning/tutorial_wrappers.py
index a53e5102d..c7395f063 100644
--- a/examples/reinforcement_learning/tutorial_wrappers.py
+++ b/examples/reinforcement_learning/tutorial_wrappers.py
@@ -7,10 +7,9 @@
 from multiprocessing import Pipe, Process, cpu_count
 from sys import platform
 
-import numpy as np
-
 import cv2
 import gym
+import numpy as np
 from gym import spaces
 
 __all__ = (
diff --git a/examples/spatial_transformer_network/tutorial_spatial_transformer_network_dynamic.py b/examples/spatial_transformer_network/tutorial_spatial_transformer_network_dynamic.py
index bc0bae141..40695339f 100644
--- a/examples/spatial_transformer_network/tutorial_spatial_transformer_network_dynamic.py
+++ b/examples/spatial_transformer_network/tutorial_spatial_transformer_network_dynamic.py
@@ -3,8 +3,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import Model
diff --git a/examples/spatial_transformer_network/tutorial_spatial_transformer_network_static.py b/examples/spatial_transformer_network/tutorial_spatial_transformer_network_static.py
index 51c2114ca..450284d91 100644
--- a/examples/spatial_transformer_network/tutorial_spatial_transformer_network_static.py
+++ b/examples/spatial_transformer_network/tutorial_spatial_transformer_network_static.py
@@ -3,8 +3,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import Model
diff --git a/examples/text_classification/tutorial_imdb_fasttext.py b/examples/text_classification/tutorial_imdb_fasttext.py
index 94de9a66f..53b0fdce7 100644
--- a/examples/text_classification/tutorial_imdb_fasttext.py
+++ b/examples/text_classification/tutorial_imdb_fasttext.py
@@ -31,8 +31,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
diff --git a/examples/text_generation/tutorial_generate_text.py b/examples/text_generation/tutorial_generate_text.py
index f3a9ebef0..f17440b62 100644
--- a/examples/text_generation/tutorial_generate_text.py
+++ b/examples/text_generation/tutorial_generate_text.py
@@ -28,8 +28,8 @@
 
 import nltk
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import Model
diff --git a/examples/text_ptb/tutorial_ptb_lstm.py b/examples/text_ptb/tutorial_ptb_lstm.py
index 2fa1f331e..6f215abba 100644
--- a/examples/text_ptb/tutorial_ptb_lstm.py
+++ b/examples/text_ptb/tutorial_ptb_lstm.py
@@ -104,8 +104,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models import Model
 
diff --git a/examples/text_ptb/tutorial_ptb_lstm_state_is_tuple.py b/examples/text_ptb/tutorial_ptb_lstm_state_is_tuple.py
index 9fccca66a..0021a7bfc 100644
--- a/examples/text_ptb/tutorial_ptb_lstm_state_is_tuple.py
+++ b/examples/text_ptb/tutorial_ptb_lstm_state_is_tuple.py
@@ -105,8 +105,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 
 tf.logging.set_verbosity(tf.logging.DEBUG)
diff --git a/examples/text_word_embedding/tutorial_word2vec_basic.py b/examples/text_word_embedding/tutorial_word2vec_basic.py
index 074bcb1fa..d7bc63fbc 100644
--- a/examples/text_word_embedding/tutorial_word2vec_basic.py
+++ b/examples/text_word_embedding/tutorial_word2vec_basic.py
@@ -39,13 +39,12 @@
 
 import argparse
 import os
-import sys
 import time
 
 import numpy as np
+import tensorflow as tf
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
-import tensorflow as tf
 import tensorlayer as tl
 import wget
 
diff --git a/examples/tutorial_work_with_onnx.py b/examples/tutorial_work_with_onnx.py
index 728438520..4d9de2cf8 100644
--- a/examples/tutorial_work_with_onnx.py
+++ b/examples/tutorial_work_with_onnx.py
@@ -117,13 +117,13 @@
 import time
 
 import numpy as np
+import tensorflow as tf
+from tensorflow.python.tools.freeze_graph import freeze_graph as _freeze_graph
 
 import onnx
-import tensorflow as tf
 import tensorlayer as tl
 from onnx_tf.backend import prepare
 from onnx_tf.frontend import tensorflow_graph_to_onnx_model
-from tensorflow.python.tools.freeze_graph import freeze_graph as _freeze_graph
 
 tf.logging.set_verbosity(tf.logging.DEBUG)
 tl.logging.set_verbosity(tl.logging.DEBUG)
diff --git a/img/medium/Readme.md b/img/medium/Readme.md
index 35ea25a33..0fc8cdc6b 100644
--- a/img/medium/Readme.md
+++ b/img/medium/Readme.md
@@ -15,5 +15,4 @@ It is 72px tall and can have a maximum width of 600px.
 
 ## Publication homepage images
 
-Under Homepage and settings >  Layout, you can select a header size, upload a logo and add a background image (large header size only).
-
+Under Homepage and settings >  Layout, you can select a header size, upload a logo and add a background image (large header size only).
\ No newline at end of file
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2f1ac7f15..f251c65bf 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,10 +1,10 @@
-imageio==2.5.0
-numpy>=1.16,<1.17
-progressbar2==3.39.3
-requests==2.21.0
-scikit-learn==0.21.0
-scikit-image==0.15.0
-scipy==1.2.1
-wrapt==1.11.1
+imageio>=2.5.0
+numpy>=1.16
+progressbar2>=3.39.3
+requests>=2.21.0
+scikit-learn>=0.21.0
+scikit-image>=0.15.0
+scipy>=1.2.1
+wrapt>=1.11.1
 h5py>=2.9
 cloudpickle>=0.8.1
diff --git a/requirements/requirements_db.txt b/requirements/requirements_db.txt
index 76796f646..f9cb647b4 100644
--- a/requirements/requirements_db.txt
+++ b/requirements/requirements_db.txt
@@ -1 +1 @@
-pymongo==3.8.0
+pymongo>=3.8.0
diff --git a/requirements/requirements_doc.txt b/requirements/requirements_doc.txt
index ab6b176f4..02d813ada 100644
--- a/requirements/requirements_doc.txt
+++ b/requirements/requirements_doc.txt
@@ -1,8 +1,8 @@
 flake8-docstrings>=1.3,<1.4
-pycodestyle==2.5.0
+pycodestyle>=2.5.0
 pydocstyle>=2.1,<3.1
 sphinx==2.0.1
 sphinx_rtd_theme>=0.4,<0.5
-wrapt==1.11.1
+wrapt>=1.11.1
 h5py>=2.9
 cloudpickle>=0.8.1
diff --git a/requirements/requirements_extra.txt b/requirements/requirements_extra.txt
index f1ab51761..3a818c650 100644
--- a/requirements/requirements_extra.txt
+++ b/requirements/requirements_extra.txt
@@ -1,6 +1,6 @@
-opencv-python==4.1.0.25
+opencv-python>=4.1.0.25
 nltk>=3.3,<3.5
 matplotlib>=2.2,<3.1
-requests==2.21.0
-tqdm==4.31.1
-lxml==4.3.3
+requests>=2.21.0
+tqdm>=4.31.1
+lxml>=4.3.3
diff --git a/requirements/requirements_test.txt b/requirements/requirements_test.txt
index a6b027a03..9642a41a4 100644
--- a/requirements/requirements_test.txt
+++ b/requirements/requirements_test.txt
@@ -1,9 +1,11 @@
 keras>=2.2,<2.3
-pycodestyle==2.5.0
+pycodestyle>=2.5.0
 pydocstyle>=2.1,<3.1
-pytest==4.5.0
+pytest>=4.5.0
 pytest-cache>=1.0,<1.1
-pytest-cov==2.7.1
-pytest-xdist==1.28.0
+pytest-cov>=2.7.1
+pytest-xdist>=1.28.0
 sphinx==2.0.1
-yapf==0.27.0
+yapf==0.29.0
+autoflake==1.3.1
+isort==4.3.21
diff --git a/requirements/requirements_tf_gpu.txt b/requirements/requirements_tf_gpu.txt
index 25247e0d1..b4199823d 100644
--- a/requirements/requirements_tf_gpu.txt
+++ b/requirements/requirements_tf_gpu.txt
@@ -1 +1 @@
-tensorflow-gpu>=2.0.0-alpha0
+tensorflow-gpu>=2.0.0-rc1
diff --git a/scripts/download_and_install_openmpi3_ubuntu.sh b/scripts/download_and_install_openmpi3_ubuntu.sh
index 8aa233a38..223abb1c2 100755
--- a/scripts/download_and_install_openmpi3_ubuntu.sh
+++ b/scripts/download_and_install_openmpi3_ubuntu.sh
@@ -23,7 +23,7 @@ URL=https://download.open-mpi.org/release/open-mpi/v${MPI_MAJOR}.${MPI_MINOR}/${
 tar -xf ${FILENAME}
 cd ${FOLDER}
 
-# will take about 8 min or longer depends on your machine
+# will take about 8 min or longer depends on your machine.
 ./configure --prefix=$HOME/local/openmpi
 make -j ${NPROC} all
 make install
diff --git a/scripts/install-requirements-for-rtd.sh b/scripts/install-requirements-for-rtd.sh
index fd0693c7a..69b1f3b76 100755
--- a/scripts/install-requirements-for-rtd.sh
+++ b/scripts/install-requirements-for-rtd.sh
@@ -1,4 +1,5 @@
 # This script is for installing horovod on readthedocs only!
+
 set -e
 
 pwd
diff --git a/setup.cfg b/setup.cfg
index 67f86fedd..284ddeb6e 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -23,28 +23,13 @@ based_on_style=google
 # The number of columns to use for indentation.
 indent_width = 4
 
-# The column limit.
+# The column limit. (larger than usual)
 column_limit=120
 
 # Place each dictionary entry onto its own line.
 each_dict_entry_on_separate_line = True
 
 # Put closing brackets on a separate line, dedented, if the bracketed
-# expression can't fit in a single line. Applies to all kinds of brackets,
-# including function definitions and calls. For example:
-#
-#   config = {
-#       'key1': 'value1',
-#       'key2': 'value2',
-#   }        # <--- this bracket is dedented and on a separate line
-#
-#   time_series = self.remote_client.query_entity_counters(
-#       entity='dev3246.region1',
-#       key='dns.query_latency_tcp',
-#       transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
-#       start_ts=now()-timedelta(days=3),
-#       end_ts=now(),
-#   )        # <--- this bracket is dedented and on a separate line
 dedent_closing_brackets=True
 
 # Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS is set
@@ -76,4 +61,4 @@ no_spaces_around_selected_binary_operators = True
 allow_multiline_lambdas = True
 
 SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT = 10
-SPLIT_PENALTY_AFTER_OPENING_BRACKET = 500
\ No newline at end of file
+SPLIT_PENALTY_AFTER_OPENING_BRACKET = 500
diff --git a/setup.py b/setup.py
index 76ac68291..8723fe774 100755
--- a/setup.py
+++ b/setup.py
@@ -5,39 +5,24 @@
 
 os.environ['TENSORLAYER_PACKAGE_BUILDING'] = 'True'
 
-
 try:
     from setuptools import find_packages, setup, Extension
     from setuptools.command.build_ext import build_ext
 
 except ImportError:
-    from distutils.core import (
-        setup,
-        find_packages
-    )
-
+    from distutils.core import (setup, find_packages)
 
 from tensorlayer import (
-    __contact_emails__,
-    __contact_names__,
-    __description__,
-    __download_url__,
-    __homepage__,
-    __keywords__,
-    __license__,
-    __package_name__,
-    __repository_url__,
-    __version__
+    __contact_emails__, __contact_names__, __description__, __download_url__, __homepage__, __keywords__, __license__,
+    __package_name__, __repository_url__, __version__
 )
 
-
 # =================== Reading Readme file as TXT files ===================
 
 if os.path.exists('README.rst'):
     # codec is used for consistent encoding
     long_description = codecs.open(
-        os.path.join(os.path.abspath(os.path.dirname(__file__)), 'README.rst'),
-        'r', 'utf-8'
+        os.path.join(os.path.abspath(os.path.dirname(__file__)), 'README.rst'), 'r', 'utf-8'
     ).read()
 
 else:
@@ -53,8 +38,8 @@ def req_file(filename, folder="requirements"):
     # Example: `\n` at the end of each line
     return [x.strip() for x in content]
 
-# ======================= Defining the requirements var =======================
 
+# ======================= Defining the requirements var =======================
 
 install_requires = req_file("requirements.txt")
 
@@ -83,11 +68,9 @@ def req_file(filename, folder="requirements"):
 extras_require['all_cpu_dev'] = sum([extras_require.get(key) for key in ['all_dev', 'tf_cpu']], list())
 extras_require['all_gpu_dev'] = sum([extras_require.get(key) for key in ['all_dev', 'tf_gpu']], list())
 
-
 cmdclass = dict()
 ext_modules = []
 
-
 # Readthedocs requires TF 1.5.0 to build properly
 if 'READTHEDOCS' in os.environ:
     ext_modules = [
@@ -95,16 +78,14 @@ def req_file(filename, folder="requirements"):
     ]
 
     class custom_build_ext(build_ext):
+
         def build_extensions(self):
-            os.system('./scripts/install-requirements-for-rtd.sh %s' %
-                      os.path.dirname(sys.executable))
+            os.system('./scripts/install-requirements-for-rtd.sh %s' % os.path.dirname(sys.executable))
 
     cmdclass = {'build_ext': custom_build_ext}
 
-
 # ======================= Define the package setup =======================
 
-
 setup(
     name=__package_name__,
 
@@ -112,7 +93,6 @@ def build_extensions(self):
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
     version=__version__,
-
     description=__description__,
     long_description=long_description,
 
@@ -130,16 +110,7 @@ def build_extensions(self):
 
     # The licence under which the project is released
     license=__license__,
-
     classifiers=[
-        # How mature is this project? Common values are
-        #  1 - Planning
-        #  2 - Pre-Alpha
-        #  3 - Alpha
-        #  4 - Beta
-        #  5 - Production/Stable
-        #  6 - Mature
-        #  7 - Inactive
         'Development Status :: 5 - Production/Stable',
 
         # Indicate who your project is intended for
@@ -170,7 +141,6 @@ def build_extensions(self):
         'Natural Language :: English',
         'Operating System :: OS Independent',
     ],
-
     keywords=__keywords__,
     packages=find_packages(),
 
@@ -179,7 +149,6 @@ def build_extensions(self):
     # requirements files see:
     # https://packaging.python.org/en/latest/requirements.html
     install_requires=install_requires,
-
     cmdclass=cmdclass,
 
     # List additional groups of dependencies here (e.g. development
@@ -187,7 +156,6 @@ def build_extensions(self):
     # $ pip install -e .[test]
     extras_require=extras_require,
     ext_modules=ext_modules,
-
     scripts=[
         'tl',
     ],
diff --git a/setup.travis.cfg b/setup.travis.cfg
index b786d1bd9..15b01240b 100644
--- a/setup.travis.cfg
+++ b/setup.travis.cfg
@@ -62,21 +62,6 @@ column_limit=120
 each_dict_entry_on_separate_line = True
 
 # Put closing brackets on a separate line, dedented, if the bracketed
-# expression can't fit in a single line. Applies to all kinds of brackets,
-# including function definitions and calls. For example:
-#
-#   config = {
-#       'key1': 'value1',
-#       'key2': 'value2',
-#   }        # <--- this bracket is dedented and on a separate line
-#
-#   time_series = self.remote_client.query_entity_counters(
-#       entity='dev3246.region1',
-#       key='dns.query_latency_tcp',
-#       transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
-#       start_ts=now()-timedelta(days=3),
-#       end_ts=now(),
-#   )        # <--- this bracket is dedented and on a separate line
 dedent_closing_brackets=True
 
 # Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS is set
diff --git a/setup.travis_doc.cfg b/setup.travis_doc.cfg
index 55267cf29..41ee76043 100644
--- a/setup.travis_doc.cfg
+++ b/setup.travis_doc.cfg
@@ -32,21 +32,6 @@ column_limit=120
 each_dict_entry_on_separate_line = True
 
 # Put closing brackets on a separate line, dedented, if the bracketed
-# expression can't fit in a single line. Applies to all kinds of brackets,
-# including function definitions and calls. For example:
-#
-#   config = {
-#       'key1': 'value1',
-#       'key2': 'value2',
-#   }        # <--- this bracket is dedented and on a separate line
-#
-#   time_series = self.remote_client.query_entity_counters(
-#       entity='dev3246.region1',
-#       key='dns.query_latency_tcp',
-#       transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
-#       start_ts=now()-timedelta(days=3),
-#       end_ts=now(),
-#   )        # <--- this bracket is dedented and on a separate line
 dedent_closing_brackets=True
 
 # Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS is set
diff --git a/tensorlayer/__init__.py b/tensorlayer/__init__.py
index f89eebfff..c00b75f28 100644
--- a/tensorlayer/__init__.py
+++ b/tensorlayer/__init__.py
@@ -44,6 +44,7 @@
     from tensorlayer import optimizers
     from tensorlayer import rein
     from tensorlayer import utils
+    from tensorlayer import app
 
     from tensorlayer.lazy_imports import LazyImport
 
diff --git a/tensorlayer/activation.py b/tensorlayer/activation.py
index 4aef4a429..e2d3ac3b9 100644
--- a/tensorlayer/activation.py
+++ b/tensorlayer/activation.py
@@ -3,6 +3,7 @@
 """A file containing various activation functions."""
 
 import tensorflow as tf
+
 from tensorlayer.decorators import deprecated
 
 __all__ = [
@@ -18,6 +19,7 @@
     'htanh',
     'hard_tanh',
     'pixel_wise_softmax',
+    'mish',
 ]
 
 
@@ -338,6 +340,25 @@ def pixel_wise_softmax(x, name='pixel_wise_softmax'):
         return tf.nn.softmax(x)
 
 
+def mish(x):
+    """Mish activation function.
+
+    Reference: [Mish: A Self Regularized Non-Monotonic Neural Activation Function .Diganta Misra, 2019]<https://arxiv.org/abs/1908.08681>
+
+    Parameters
+    ----------
+    x : Tensor
+        input.
+
+    Returns
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
+
+    """
+    return x * tf.math.tanh(tf.math.softplus(x))
+
+
 # Alias
 lrelu = leaky_relu
 lrelu6 = leaky_relu6
diff --git a/tensorlayer/app/__init__.py b/tensorlayer/app/__init__.py
new file mode 100644
index 000000000..9b6a5139d
--- /dev/null
+++ b/tensorlayer/app/__init__.py
@@ -0,0 +1,6 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .computer_vision_object_detection import *
+from .human_pose_estimation import *
+from .computer_vision import *
diff --git a/tensorlayer/app/computer_vision.py b/tensorlayer/app/computer_vision.py
new file mode 100644
index 000000000..496854d88
--- /dev/null
+++ b/tensorlayer/app/computer_vision.py
@@ -0,0 +1,118 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app import YOLOv4
+from tensorlayer.app import CGCNN
+from tensorlayer import logging
+from tensorlayer.app import yolo4_input_processing, yolo4_output_processing, result_to_json
+
+
+class object_detection(object):
+    """Model encapsulation.
+
+    Parameters
+    ----------
+    model_name : str
+        Choose the model to inference.
+
+    Methods
+    ---------
+    __init__()
+        Initializing the model.
+    __call__()
+        (1)Formatted input and output. (2)Inference model.
+    list()
+        Abstract method. Return available a list of model_name.
+
+    Examples
+    ---------
+    Object Detection detection MSCOCO with YOLOv4, see `tutorial_object_detection_yolov4.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_object_detection_yolov4.py>`__
+    With TensorLayer
+
+    >>> # get the whole model
+    >>> net = tl.app.computer_vision.object_detection('yolo4-mscoco')
+    >>> # use for inferencing
+    >>> output = net(img)
+    """
+
+    def __init__(self, model_name='yolo4-mscoco'):
+        self.model_name = model_name
+        if self.model_name == 'yolo4-mscoco':
+            self.model = YOLOv4(NUM_CLASS=80, pretrained=True)
+        else:
+            raise ("The model does not support.")
+
+    def __call__(self, input_data):
+        if self.model_name == 'yolo4-mscoco':
+            batch_data = yolo4_input_processing(input_data)
+            feature_maps = self.model(batch_data, is_train=False)
+            pred_bbox = yolo4_output_processing(feature_maps)
+            output = result_to_json(input_data, pred_bbox)
+        else:
+            raise NotImplementedError
+
+        return output
+
+    def __repr__(self):
+        s = ('(model_name={model_name}, model_structure={model}')
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    @property
+    def list(self):
+        logging.info("The model name list: 'yolov4-mscoco', 'lcn'")
+
+
+class human_pose_estimation(object):
+    """Model encapsulation.
+
+    Parameters
+    ----------
+    model_name : str
+        Choose the model to inference.
+
+    Methods
+    ---------
+    __init__()
+        Initializing the model.
+    __call__()
+        (1)Formatted input and output. (2)Inference model.
+    list()
+        Abstract method. Return available a list of model_name.
+
+    Examples
+    ---------
+    LCN to estimate 3D human poses from 2D poses, see `tutorial_human_3dpose_estimation_LCN.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_human_3dpose_estimation_LCN.py>`__
+    With TensorLayer
+
+    >>> # get the whole model
+    >>> net = tl.app.computer_vision.human_pose_estimation('3D-pose')
+    >>> # use for inferencing
+    >>> output = net(img)
+    """
+
+    def __init__(self, model_name='3D-pose'):
+        self.model_name = model_name
+        if self.model_name == '3D-pose':
+            self.model = CGCNN(pretrained=True)
+        else:
+            raise ("The model does not support.")
+
+    def __call__(self, input_data):
+        if self.model_name == '3D-pose':
+            output = self.model(input_data, is_train=False)
+        else:
+            raise NotImplementedError
+
+        return output
+
+    def __repr__(self):
+        s = ('(model_name={model_name}, model_structure={model}')
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    @property
+    def list(self):
+        logging.info("The model name list: '3D-pose'")
diff --git a/tensorlayer/app/computer_vision_object_detection/__init__.py b/tensorlayer/app/computer_vision_object_detection/__init__.py
new file mode 100644
index 000000000..940e8a1f8
--- /dev/null
+++ b/tensorlayer/app/computer_vision_object_detection/__init__.py
@@ -0,0 +1,5 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .yolov4 import YOLOv4
+from .common import *
diff --git a/tensorlayer/app/computer_vision_object_detection/common.py b/tensorlayer/app/computer_vision_object_detection/common.py
new file mode 100644
index 000000000..b5ea1cc75
--- /dev/null
+++ b/tensorlayer/app/computer_vision_object_detection/common.py
@@ -0,0 +1,224 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import colorsys, random, cv2
+import numpy as np
+
+
+def decode_tf(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=0, XYSCALE=[1, 1, 1]):
+    batch_size = tf.shape(conv_output)[0]
+    conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
+
+    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
+
+    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
+    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]
+    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [batch_size, 1, 1, 3, 1])
+
+    xy_grid = tf.cast(xy_grid, tf.float32)
+
+    pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * \
+              STRIDES[i]
+    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i])
+    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
+
+    pred_conf = tf.sigmoid(conv_raw_conf)
+    pred_prob = tf.sigmoid(conv_raw_prob)
+
+    pred_prob = pred_conf * pred_prob
+    pred_prob = tf.reshape(pred_prob, (batch_size, -1, NUM_CLASS))
+    pred_xywh = tf.reshape(pred_xywh, (batch_size, -1, 4))
+
+    return pred_xywh, pred_prob
+
+
+def decode(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE=[1, 1, 1]):
+    return decode_tf(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=i, XYSCALE=XYSCALE)
+
+
+def filter_boxes(box_xywh, scores, score_threshold=0.4, input_shape=tf.constant([416, 416])):
+    scores_max = tf.math.reduce_max(scores, axis=-1)
+
+    mask = scores_max >= score_threshold
+    class_boxes = tf.boolean_mask(box_xywh, mask)
+    pred_conf = tf.boolean_mask(scores, mask)
+    class_boxes = tf.reshape(class_boxes, [tf.shape(scores)[0], -1, tf.shape(class_boxes)[-1]])
+    pred_conf = tf.reshape(pred_conf, [tf.shape(scores)[0], -1, tf.shape(pred_conf)[-1]])
+
+    box_xy, box_wh = tf.split(class_boxes, (2, 2), axis=-1)
+
+    input_shape = tf.cast(input_shape, dtype=tf.float32)
+    box_yx = box_xy[..., ::-1]
+    box_hw = box_wh[..., ::-1]
+
+    box_mins = (box_yx - (box_hw / 2.)) / input_shape
+    box_maxes = (box_yx + (box_hw / 2.)) / input_shape
+    boxes = tf.concat(
+        [
+            box_mins[..., 0:1],  # y_min
+            box_mins[..., 1:2],  # x_min
+            box_maxes[..., 0:1],  # y_max
+            box_maxes[..., 1:2]  # x_max
+        ],
+        axis=-1
+    )
+    # return tf.concat([boxes, pred_conf], axis=-1)
+    return (boxes, pred_conf)
+
+
+def read_class_names(class_file_name):
+    names = {}
+    with open(class_file_name, 'r') as data:
+        for ID, name in enumerate(data):
+            names[ID] = name.strip('\n')
+    return names
+
+
+def draw_bbox(image, bboxes, show_label=True):
+    classes = read_class_names('model/coco.names')
+    num_classes = len(classes)
+    image_h, image_w, _ = image.shape
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+
+    out_boxes, out_scores, out_classes, num_boxes = bboxes
+    for i in range(num_boxes[0]):
+        if int(out_classes[0][i]) < 0 or int(out_classes[0][i]) > num_classes: continue
+        coor = out_boxes[0][i]
+        coor[0] = int(coor[0] * image_h)
+        coor[2] = int(coor[2] * image_h)
+        coor[1] = int(coor[1] * image_w)
+        coor[3] = int(coor[3] * image_w)
+
+        fontScale = 0.5
+        score = out_scores[0][i]
+        class_ind = int(out_classes[0][i])
+        bbox_color = colors[class_ind]
+        bbox_thick = int(0.6 * (image_h + image_w) / 600)
+        c1, c2 = (coor[1], coor[0]), (coor[3], coor[2])
+        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
+
+        if show_label:
+            bbox_mess = '%s: %.2f' % (classes[class_ind], score)
+            t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
+            c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
+            cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)  #filled
+
+            cv2.putText(
+                image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
+                bbox_thick // 2, lineType=cv2.LINE_AA
+            )
+    return image
+
+
+def get_anchors(anchors_path, tiny=False):
+    anchors = np.array(anchors_path)
+    if tiny:
+        return anchors.reshape(2, 3, 2)
+    else:
+        return anchors.reshape(3, 3, 2)
+
+
+def decode_train(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=0, XYSCALE=[1, 1, 1]):
+    conv_output = tf.reshape(conv_output, (tf.shape(conv_output)[0], output_size, output_size, 3, 5 + NUM_CLASS))
+
+    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
+
+    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
+    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]
+    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [tf.shape(conv_output)[0], 1, 1, 3, 1])
+
+    xy_grid = tf.cast(xy_grid, tf.float32)
+
+    pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * \
+              STRIDES[i]
+    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i])
+    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
+
+    pred_conf = tf.sigmoid(conv_raw_conf)
+    pred_prob = tf.sigmoid(conv_raw_prob)
+
+    return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
+
+
+def yolo4_input_processing(original_image):
+    image_data = cv2.resize(original_image, (416, 416))
+    image_data = image_data / 255.
+    images_data = []
+    for i in range(1):
+        images_data.append(image_data)
+    images_data = np.asarray(images_data).astype(np.float32)
+    batch_data = tf.constant(images_data)
+    return batch_data
+
+
+def yolo4_output_processing(feature_maps):
+    STRIDES = [8, 16, 32]
+    ANCHORS = get_anchors([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401])
+    NUM_CLASS = 80
+    XYSCALE = [1.2, 1.1, 1.05]
+    iou_threshold = 0.45
+    score_threshold = 0.25
+
+    bbox_tensors = []
+    prob_tensors = []
+    score_thres = 0.2
+    for i, fm in enumerate(feature_maps):
+        if i == 0:
+            output_tensors = decode(fm, 416 // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        elif i == 1:
+            output_tensors = decode(fm, 416 // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        else:
+            output_tensors = decode(fm, 416 // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        bbox_tensors.append(output_tensors[0])
+        prob_tensors.append(output_tensors[1])
+    pred_bbox = tf.concat(bbox_tensors, axis=1)
+    pred_prob = tf.concat(prob_tensors, axis=1)
+    boxes, pred_conf = filter_boxes(
+        pred_bbox, pred_prob, score_threshold=score_thres, input_shape=tf.constant([416, 416])
+    )
+    pred = {'concat': tf.concat([boxes, pred_conf], axis=-1)}
+
+    for key, value in pred.items():
+        boxes = value[:, :, 0:4]
+        pred_conf = value[:, :, 4:]
+
+    boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
+        boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
+        scores=tf.reshape(pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
+        max_output_size_per_class=50, max_total_size=50, iou_threshold=iou_threshold, score_threshold=score_threshold
+    )
+    output = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
+    return output
+
+
+def result_to_json(image, pred_bbox):
+    image_h, image_w, _ = image.shape
+    out_boxes, out_scores, out_classes, num_boxes = pred_bbox
+    class_names = {}
+    json_result = []
+    with open('model/coco.names', 'r') as data:
+        for ID, name in enumerate(data):
+            class_names[ID] = name.strip('\n')
+    nums_class = len(class_names)
+
+    for i in range(num_boxes[0]):
+        if int(out_classes[0][i]) < 0 or int(out_classes[0][i]) > nums_class: continue
+        coor = out_boxes[0][i]
+        coor[0] = int(coor[0] * image_h)
+        coor[2] = int(coor[2] * image_h)
+        coor[1] = int(coor[1] * image_w)
+        coor[3] = int(coor[3] * image_w)
+
+        score = float(out_scores[0][i])
+        class_ind = int(out_classes[0][i])
+        bbox = np.array([coor[1], coor[0], coor[3], coor[2]]).tolist()  # [x1,y1,x2,y2]
+        json_result.append({'image': None, 'category_id': class_ind, 'bbox': bbox, 'score': score})
+
+    return json_result
diff --git a/tensorlayer/app/computer_vision_object_detection/yolov4.py b/tensorlayer/app/computer_vision_object_detection/yolov4.py
new file mode 100644
index 000000000..c4569e33f
--- /dev/null
+++ b/tensorlayer/app/computer_vision_object_detection/yolov4.py
@@ -0,0 +1,244 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""YOLOv4 for MS-COCO.
+
+# Reference:
+- [tensorflow-yolov4-tflite](
+    https://github.com/hunglc007/tensorflow-yolov4-tflite)
+
+"""
+
+import tensorflow as tf
+import numpy as np
+import tensorlayer as tl
+from tensorlayer.activation import mish
+from tensorlayer.layers import Conv2d, MaxPool2d, BatchNorm2d, ZeroPad2d, UpSampling2d, Concat, Input, Elementwise
+from tensorlayer.models import Model
+from tensorlayer import logging
+
+INPUT_SIZE = 416
+weights_url = {'link': 'https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ', 'password': 'idsz'}
+
+
+def upsample(input_layer):
+    return UpSampling2d(scale=2)(input_layer)
+
+
+def convolutional(
+    input_layer, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky', name=None
+):
+    if downsample:
+        input_layer = ZeroPad2d(((1, 0), (1, 0)))(input_layer)
+        padding = 'VALID'
+        strides = 2
+    else:
+        strides = 1
+        padding = 'SAME'
+
+    if bn:
+        b_init = None
+    else:
+        b_init = tl.initializers.constant(value=0.0)
+
+    conv = Conv2d(
+        n_filter=filters_shape[-1], filter_size=(filters_shape[0], filters_shape[1]), strides=(strides, strides),
+        padding=padding, b_init=b_init, name=name
+    )(input_layer)
+
+    if bn:
+        if activate ==True:
+            if activate_type == 'leaky':
+                conv = BatchNorm2d(act='lrelu0.1')(conv)
+            elif activate_type == 'mish':
+                conv = BatchNorm2d(act=mish)(conv)
+        else:
+            conv = BatchNorm2d()(conv)
+    return conv
+
+
+def residual_block(input_layer, input_channel, filter_num1, filter_num2, activate_type='leaky'):
+    short_cut = input_layer
+    conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
+    conv = convolutional(conv, filters_shape=(3, 3, filter_num1, filter_num2), activate_type=activate_type)
+
+    residual_output = Elementwise(tf.add)([short_cut, conv])
+    return residual_output
+
+
+def cspdarknet53(input_data=None):
+
+    input_data = convolutional(input_data, (3, 3, 3, 32), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True, activate_type='mish')
+
+    route = input_data
+    route = convolutional(route, (1, 1, 64, 64), activate_type='mish', name='conv_rote_block_1')
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+
+    for i in range(1):
+        input_data = residual_block(input_data, 64, 32, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+
+    input_data = Concat()([input_data, route])
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 128, 64), activate_type='mish', name='conv_rote_block_2')
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type='mish')
+    for i in range(2):
+        input_data = residual_block(input_data, 64, 64, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 256, 128), activate_type='mish', name='conv_rote_block_3')
+    input_data = convolutional(input_data, (1, 1, 256, 128), activate_type='mish')
+    for i in range(8):
+        input_data = residual_block(input_data, 128, 128, 128, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type='mish')
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 512, 256), activate_type='mish', name='conv_rote_block_4')
+    input_data = convolutional(input_data, (1, 1, 512, 256), activate_type='mish')
+    for i in range(8):
+        input_data = residual_block(input_data, 256, 256, 256, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type='mish')
+    route_2 = input_data
+    input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 1024, 512), activate_type='mish', name='conv_rote_block_5')
+    input_data = convolutional(input_data, (1, 1, 1024, 512), activate_type='mish')
+    for i in range(4):
+        input_data = residual_block(input_data, 512, 512, 512, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 1024, 1024), activate_type='mish')
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    maxpool1 = MaxPool2d(filter_size=(13, 13), strides=(1, 1))(input_data)
+    maxpool2 = MaxPool2d(filter_size=(9, 9), strides=(1, 1))(input_data)
+    maxpool3 = MaxPool2d(filter_size=(5, 5), strides=(1, 1))(input_data)
+    input_data = Concat()([maxpool1, maxpool2, maxpool3, input_data])
+
+    input_data = convolutional(input_data, (1, 1, 2048, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    return route_1, route_2, input_data
+
+
+def YOLOv4(NUM_CLASS, pretrained=False):
+    """Pre-trained YOLOv4 model.
+
+    Parameters
+    ------------
+    NUM_CLASS : int
+        Number of classes in final prediction.
+    pretrained : boolean
+        Whether to load pretrained weights. Default False.
+
+    Examples
+    ---------
+    Object Detection with YOLOv4, see `computer_vision.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/app/computer_vision.py>`__
+    With TensorLayer
+
+    >>> # get the whole model, without pre-trained YOLOv4 parameters
+    >>> yolov4 = tl.app.YOLOv4(NUM_CLASS=80, pretrained=False)
+    >>> # get the whole model, restore pre-trained YOLOv4 parameters
+    >>> yolov4 = tl.app.YOLOv4(NUM_CLASS=80, pretrained=True)
+    >>> # use for inferencing
+    >>> output = yolov4(img, is_train=False)
+
+    """
+
+    input_layer = Input([None, INPUT_SIZE, INPUT_SIZE, 3])
+    route_1, route_2, conv = cspdarknet53(input_layer)
+
+    route = conv
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = upsample(conv)
+    route_2 = convolutional(route_2, (1, 1, 512, 256), name='conv_yolo_1')
+    conv = Concat()([route_2, conv])
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = upsample(conv)
+    route_1 = convolutional(route_1, (1, 1, 256, 128), name='conv_yolo_2')
+    conv = Concat()([route_1, conv])
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+
+    route_1 = conv
+    conv = convolutional(conv, (3, 3, 128, 256), name='conv_route_1')
+    conv_sbbox = convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_1, (3, 3, 128, 256), downsample=True, name='conv_route_2')
+    conv = Concat()([conv, route_2])
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (3, 3, 256, 512), name='conv_route_3')
+    conv_mbbox = convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_2, (3, 3, 256, 512), downsample=True, name='conv_route_4')
+    conv = Concat()([conv, route])
+
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv_lbbox = convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    network = Model(input_layer, [conv_sbbox, conv_mbbox, conv_lbbox])
+
+    if pretrained:
+        restore_params(network, model_path='model/yolov4_model.npz')
+
+    return network
+
+
+def restore_params(network, model_path='models.npz'):
+    logging.info("Restore pre-trained weights")
+
+    try:
+        npz = np.load(model_path, allow_pickle=True)
+    except:
+        print("Download the model file, placed in the /model ")
+        print("Weights download: ", weights_url['link'], "password:", weights_url['password'])
+
+    txt_path = 'model/yolov4_weights_config.txt'
+    f = open(txt_path, "r")
+    line = f.readlines()
+    for i in range(len(line)):
+        network.all_weights[i].assign(npz[line[i].strip()])
+        logging.info("  Loading weights %s in %s" % (network.all_weights[i].shape, network.all_weights[i].name))
diff --git a/tensorlayer/app/human_pose_estimation/LCN.py b/tensorlayer/app/human_pose_estimation/LCN.py
new file mode 100644
index 000000000..a88c5bc3e
--- /dev/null
+++ b/tensorlayer/app/human_pose_estimation/LCN.py
@@ -0,0 +1,331 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+""" LCN to estimate 3D human poses from 2D poses.
+
+# Reference:
+- [pose_lcn](
+    https://github.com/rujiewu/pose_lcn)
+
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
+from tensorlayer.models import Model
+from tensorlayer import logging
+from .common import mask_weight, neighbour_matrix
+
+BATCH_SIZE = 200
+M_0 = 17
+IN_F = 2
+
+IN_JOINTS = 17
+OUT_JOINTS = 17
+F = 64
+NUM_LAYERS = 3
+weights_url = {'link': 'https://pan.baidu.com/s/1HBHWsAfyAlNaavw0iyUmUQ', 'password': 'ec07'}
+
+
+class Base_layer(Layer):
+
+    def __init__(
+        self, F=F, in_joints=IN_JOINTS, out_joints=OUT_JOINTS, regularization=0.0, max_norm=True, residual=True,
+        mask_type='locally_connected', neighbour_matrix=neighbour_matrix, init_type='ones', in_F=IN_F
+    ):
+        super().__init__()
+        self.F = F
+        self.in_joints = in_joints
+        self.regularizers = []
+        self.regularization = regularization
+        self.max_norm = max_norm
+        self.out_joints = out_joints
+        self.residual = residual
+        self.mask_type = mask_type
+
+        self.init_type = init_type
+        self.in_F = in_F
+
+        assert neighbour_matrix.shape[0] == neighbour_matrix.shape[1]
+        assert neighbour_matrix.shape[0] == in_joints
+        self.neighbour_matrix = neighbour_matrix
+
+        self._initialize_mask()
+
+    def _initialize_mask(self):
+        """
+        Parameter
+            mask_type
+                locally_connected
+                locally_connected_learnable
+            init_type
+                same: use L to init learnable part in mask
+                ones: use 1 to init learnable part in mask
+                random: use random to init learnable part in mask
+        """
+        if 'locally_connected' in self.mask_type:
+            assert self.neighbour_matrix is not None
+            L = self.neighbour_matrix.T
+            assert L.shape == (self.in_joints, self.in_joints)
+            if 'learnable' not in self.mask_type:
+                self.mask = tf.constant(L)
+            else:
+                if self.init_type == 'same':
+                    initializer = L
+                elif self.init_type == 'ones':
+                    initializer = tf.initializers.ones
+                elif self.init_type == 'random':
+                    initializer = tf.random.uniform
+                var_mask = tf.Variable(
+                    name='mask', shape=[self.in_joints, self.out_joints] if self.init_type != 'same' else None,
+                    dtype=tf.float32, initial_value=initializer
+                )
+                var_mask = tf.nn.softmax(var_mask, axis=0)
+                self.mask = var_mask * tf.constant(L != 0, dtype=tf.float32)
+
+    def _get_weights(self, name, initializer, shape, regularization=True, trainable=True):
+        var = tf.Variable(initial_value=initializer(shape=shape, dtype=tf.float32), name=name, trainable=True)
+        if regularization:
+            self.regularizers.append(tf.nn.l2_loss(var))
+        if trainable is True:
+            if self._trainable_weights is None:
+                self._trainable_weights = list()
+            self._trainable_weights.append(var)
+        else:
+            if self._nontrainable_weights is None:
+                self._nontrainable_weights = list()
+            self._nontrainable_weights.append(var)
+        return var
+
+    def kaiming(self, shape, dtype):
+        """Kaiming initialization as described in https://arxiv.org/pdf/1502.01852.pdf
+
+        Args
+            shape: dimensions of the tf array to initialize
+            dtype: data type of the array
+            partition_info: (Optional) info about how the variable is partitioned.
+                See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/init_ops.py#L26
+                Needed to be used as an initializer.
+        Returns
+            Tensorflow array with initial weights
+        """
+        return (tf.random.truncated_normal(shape, dtype=dtype) * tf.sqrt(2 / float(shape[0])))
+
+    def mask_weights(self, weights):
+        return mask_weight(weights)
+
+
+class Mask_layer(Base_layer):
+
+    def __init__(self, in_channels=17, out_channels=None, name=None):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.w_name, self.b_name = name
+
+        if self.in_channels:
+            self.build(None)
+            self._built = True
+
+    def build(self, inputs_shape):
+        if self.in_channels is None:
+            self.in_channels = inputs_shape[1]
+
+        self.weight = self._get_weights(
+            self.w_name, self.kaiming, [self.in_channels, self.out_channels], regularization=self.regularization != 0
+        )
+        self.bias = self._get_weights(
+            self.b_name, self.kaiming, [self.out_channels], regularization=self.regularization != 0
+        )  # equal to b2leaky_relu
+        self.weight = tf.clip_by_norm(self.weight, 1) if self.max_norm else self.weight
+
+        self.weight = self.mask_weights(self.weight)
+
+    def forward(self, x):
+        outputs = tf.matmul(x, self.weight) + self.bias
+        return outputs
+
+
+class End_layer(Base_layer):
+
+    def __init__(self):
+        super().__init__()
+
+    def build(self, inputs_shape):
+        pass
+
+    def forward(self, inputs):
+        x, y = inputs
+        x = tf.reshape(x, [-1, self.in_joints, self.in_F])  # [N, J, 3]
+        y = tf.reshape(y, [-1, self.out_joints, 3])  # [N, J, 3]
+        y = tf.concat([x[:, :, :2] + y[:, :, :2], tf.expand_dims(y[:, :, 2], axis=-1)], axis=2)  # [N, J, 3]
+        y = tf.reshape(y, [-1, self.out_joints * 3])
+        return y
+
+
+def batch_normalization_warp(y):
+    _, output_size = y.get_shape()
+    output_size = int(output_size)
+    out_F = int(output_size / IN_JOINTS)
+    y = Reshape([-1, IN_JOINTS, out_F])(y)
+    y = BatchNorm(act='lrelu', epsilon=1e-3)(y)
+    y = Reshape([-1, output_size])(y)
+    return y
+
+
+def two_linear_train(inputs, idx):
+    """
+    Make a bi-linear block with optional residual connection
+
+    Args
+        xin: the batch that enters the block
+        idx: integer. Number of layer (for naming/scoping)
+        Returns
+    y: the batch after it leaves the block
+    """
+
+    output_size = IN_JOINTS * F
+
+    # Linear 1
+    input_size1 = int(inputs.get_shape()[1])
+    output = Mask_layer(in_channels=input_size1, out_channels=output_size, name=["w2" + str(idx),
+                                                                                 "b2" + str(idx)])(inputs)
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # Linear 2
+    input_size2 = int(output.get_shape()[1])
+    output = Mask_layer(in_channels=input_size2, out_channels=output_size, name=["w3_" + str(idx),
+                                                                                 "b3_" + str(idx)])(output)
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # Residual every 2 blocks
+    output = Elementwise(combine_fn=tf.add)([inputs, output])
+
+    return output
+
+
+def cgcnn_train():
+    input_layer = Input(shape=(BATCH_SIZE, M_0 * IN_F))
+
+    # === First layer===
+    output = Mask_layer(in_channels=IN_JOINTS * IN_F, out_channels=IN_JOINTS * F, name=["w1", "b1"])(input_layer)
+
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # === Create multiple bi-linear layers ===
+    for idx in range(NUM_LAYERS):
+        output = two_linear_train(output, idx)
+
+    # === Last layer ===
+    input_size4 = int(output.get_shape()[1])
+    output = Mask_layer(in_channels=input_size4, out_channels=OUT_JOINTS * 3, name=["w4", "b4"])(output)
+
+    # === End linear model ===
+    output = End_layer()([input_layer, output])
+
+    network = Model(inputs=input_layer, outputs=output)
+
+    return network
+
+
+# inference
+def two_linear_inference(xin):
+    """
+    Make a bi-linear block with optional residual connection
+
+    Args
+        xin: the batch that enters the block
+    y: the batch after it leaves the block
+    """
+
+    output_size = IN_JOINTS * F
+
+    # Linear 1
+    output = Dense(n_units=output_size, act=None)(xin)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # Linear 2
+    output = Dense(n_units=output_size, act=None)(output)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # Residual every 2 blocks
+    y = Elementwise(tf.add)([xin, output])
+
+    return y
+
+
+def cgcnn_inference():
+    input_layer = Input(shape=(BATCH_SIZE, M_0 * IN_F))
+
+    # === First layer===
+    output = Dense(n_units=IN_JOINTS * F, act=None)(input_layer)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # === Create multiple bi-linear layers ===
+    for i in range(3):
+        output = two_linear_inference(output)
+
+    # === Last layer ===
+    output = Dense(n_units=OUT_JOINTS * 3, act=None)(output)
+
+    output = End_layer()([input_layer, output])
+
+    network = Model(inputs=input_layer, outputs=output)
+    return network
+
+
+def restore_params(network, model_path='model.npz'):
+    logging.info("Restore pre-trained weights")
+
+    try:
+        npz = np.load(model_path, allow_pickle=True)
+    except:
+        print("Download the model file, placed in the /model ")
+        print("Weights download: ", weights_url['link'], "password:", weights_url['password'])
+
+    txt_path = 'model/pose_weights_config.txt'
+    f = open(txt_path, "r")
+    line = f.readlines()
+    for i in range(len(line)):
+        # mask weights
+        if len(npz[line[i].strip()].shape) == 2:
+            _weight = mask_weight(npz[line[i].strip()])
+        else:
+            _weight = npz[line[i].strip()]
+        network.all_weights[i].assign(_weight)
+        logging.info("  Loading weights %s in %s" % (network.all_weights[i].shape, network.all_weights[i].name))
+
+
+def CGCNN(pretrained=True):
+    """Pre-trained LCN model.
+
+    Parameters
+    ------------
+    pretrained : boolean
+        Whether to load pretrained weights. Default False.
+
+    Examples
+    ---------
+    LCN to estimate 3D human poses from 2D poses, see `computer_vision.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/app/computer_vision.py>`__
+    With TensorLayer
+
+    >>> # get the whole model, without pre-trained LCN parameters
+    >>> lcn = tl.app.CGCNN(pretrained=False)
+    >>> # get the whole model, restore pre-trained LCN parameters
+    >>> lcn = tl.app.CGCNN(pretrained=True)
+    >>> # use for inferencing
+    >>> output = lcn(img, is_train=False)
+
+    """
+    if pretrained:
+        network = cgcnn_inference()
+        restore_params(network, model_path='model/lcn_model.npz')
+    else:
+        network = cgcnn_train()
+    return network
diff --git a/tensorlayer/app/human_pose_estimation/__init__.py b/tensorlayer/app/human_pose_estimation/__init__.py
new file mode 100644
index 000000000..f9ca7f6b7
--- /dev/null
+++ b/tensorlayer/app/human_pose_estimation/__init__.py
@@ -0,0 +1,5 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .common import *
+from .LCN import CGCNN
diff --git a/tensorlayer/app/human_pose_estimation/common.py b/tensorlayer/app/human_pose_estimation/common.py
new file mode 100644
index 000000000..66df417e7
--- /dev/null
+++ b/tensorlayer/app/human_pose_estimation/common.py
@@ -0,0 +1,424 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+
+# Reference:
+- [pose_lcn](
+    https://github.com/rujiewu/pose_lcn)
+
+- [3d-pose-baseline](
+    https://github.com/una-dinosauria/3d-pose-baseline)
+
+"""
+
+import tensorflow as tf
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+import os
+import matplotlib.gridspec as gridspec
+
+H36M_NAMES = [''] * 17
+H36M_NAMES[0] = 'Hip'
+H36M_NAMES[1] = 'RHip'
+H36M_NAMES[2] = 'RKnee'
+H36M_NAMES[3] = 'RFoot'
+H36M_NAMES[4] = 'LHip'
+H36M_NAMES[5] = 'LKnee'
+H36M_NAMES[6] = 'LFoot'
+H36M_NAMES[7] = 'Belly'
+H36M_NAMES[8] = 'Neck'
+H36M_NAMES[9] = 'Nose'
+H36M_NAMES[10] = 'Head'
+H36M_NAMES[11] = 'LShoulder'
+H36M_NAMES[12] = 'LElbow'
+H36M_NAMES[13] = 'LHand'
+H36M_NAMES[14] = 'RShoulder'
+H36M_NAMES[15] = 'RElbow'
+H36M_NAMES[16] = 'RHand'
+IN_F = 2
+IN_JOINTS = 17
+OUT_JOINTS = 17
+neighbour_matrix = np.array(
+    [
+        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0.],
+        [1., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0.],
+        [1., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1.]
+    ]
+)
+
+ROOT_PATH = '../../examples/app_tutorials/data/'
+
+
+def mask_weight(weight):
+    weights = tf.clip_by_norm(weight, 1)
+    L = neighbour_matrix.T
+    mask = tf.constant(L)
+    input_size, output_size = weights.get_shape()
+    input_size, output_size = int(input_size), int(output_size)
+    assert input_size % IN_JOINTS == 0 and output_size % IN_JOINTS == 0
+    in_F = int(input_size / IN_JOINTS)
+    out_F = int(output_size / IN_JOINTS)
+    weights = tf.reshape(weights, [IN_JOINTS, in_F, IN_JOINTS, out_F])
+    mask = tf.reshape(mask, [IN_JOINTS, 1, IN_JOINTS, 1])
+
+    weights = tf.cast(weights, dtype=tf.float32)
+    mask = tf.cast(mask, dtype=tf.float32)
+
+    masked_weights = weights * mask
+    masked_weights = tf.reshape(masked_weights, [input_size, output_size])
+    return masked_weights
+
+
+def flip_data(data):
+    """
+    horizontal flip
+        data: [N, 17*k] or [N, 17, k], i.e. [x, y], [x, y, confidence] or [x, y, z]
+    Return
+        result: [2N, 17*k] or [2N, 17, k]
+    """
+    left_joints = [4, 5, 6, 11, 12, 13]
+    right_joints = [1, 2, 3, 14, 15, 16]
+
+    flipped_data = data.copy().reshape((len(data), 17, -1))
+    flipped_data[:, :, 0] *= -1  # flip x of all joints
+    flipped_data[:, left_joints + right_joints] = flipped_data[:, right_joints + left_joints]
+    flipped_data = flipped_data.reshape(data.shape)
+
+    result = np.concatenate((data, flipped_data), axis=0)
+
+    return result
+
+
+def unflip_data(data):
+    """
+    Average original data and flipped data
+        data: [2N, 17*3]
+    Return
+        result: [N, 17*3]
+    """
+    left_joints = [4, 5, 6, 11, 12, 13]
+    right_joints = [1, 2, 3, 14, 15, 16]
+
+    data = data.copy().reshape((2, -1, 17, 3))
+    data[1, :, :, 0] *= -1  # flip x of all joints
+    data[1, :, left_joints + right_joints] = data[1, :, right_joints + left_joints]
+    data = np.mean(data, axis=0)
+    data = data.reshape((-1, 17 * 3))
+
+    return data
+
+
+class DataReader(object):
+
+    def __init__(self):
+        self.gt_trainset = None
+        self.gt_testset = None
+        self.dt_dataset = None
+
+    def real_read(self, subset):
+        file_name = 'h36m_%s.pkl' % subset
+        print('loading %s' % file_name)
+        file_path = os.path.join(ROOT_PATH, file_name)
+        with open(file_path, 'rb') as f:
+            gt = pickle.load(f)
+        return gt
+
+    def read_2d(self, which='scale', mode='dt_ft', read_confidence=True):
+        if self.gt_trainset is None:
+            self.gt_trainset = self.real_read('train')
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if mode == 'gt':
+            trainset = np.empty((len(self.gt_trainset), 17, 2))  # [N, 17, 2]
+            testset = np.empty((len(self.gt_testset), 17, 2))  # [N, 17, 2]
+            for idx, item in enumerate(self.gt_trainset):
+                trainset[idx] = item['joint_3d_image'][:, :2]
+            for idx, item in enumerate(self.gt_testset):
+                testset[idx] = item['joint_3d_image'][:, :2]
+            if read_confidence:
+                train_confidence = np.ones((len(self.gt_trainset), 17, 1))  # [N, 17, 1]
+                test_confidence = np.ones((len(self.gt_testset), 17, 1))  # [N, 17, 1]
+        elif mode == 'dt_ft':
+            file_name = 'h36m_sh_dt_ft.pkl'
+            file_path = os.path.join(ROOT_PATH, 'dataset', file_name)
+            print('loading %s' % file_name)
+            with open(file_path, 'rb') as f:
+                self.dt_dataset = pickle.load(f)
+
+            trainset = self.dt_dataset['train']['joint3d_image'][:, :, :2].copy()  # [N, 17, 2]
+            testset = self.dt_dataset['test']['joint3d_image'][:, :, :2].copy()  # [N, 17, 2]
+            if read_confidence:
+                train_confidence = self.dt_dataset['train']['confidence'].copy()  # [N, 17, 1]
+                test_confidence = self.dt_dataset['test']['confidence'].copy()  # [N, 17, 1]
+        else:
+            assert 0, 'not supported type %s' % mode
+
+        # normalize
+        if which == 'scale':
+            # map to [-1, 1]
+            for idx, item in enumerate(self.gt_trainset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                trainset[idx, :, :] = trainset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                testset[idx, :, :] = testset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+        else:
+            assert 0, 'not support normalize type %s' % which
+
+        if read_confidence:
+            trainset = np.concatenate((trainset, train_confidence), axis=2)  # [N, 17, 3]
+            testset = np.concatenate((testset, test_confidence), axis=2)  # [N, 17, 3]
+
+        # reshape
+        trainset, testset = trainset.reshape((len(trainset), -1)).astype(np.float32), testset.reshape(
+            (len(testset), -1)
+        ).astype(np.float32)
+
+        return trainset, testset
+
+    def read_3d(self, which='scale', mode='dt_ft'):
+        if self.gt_trainset is None:
+            self.gt_trainset = self.real_read('train')
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        # normalize
+        train_labels = np.empty((len(self.gt_trainset), 17, 3))
+        test_labels = np.empty((len(self.gt_testset), 17, 3))
+        if which == 'scale':
+            # map to [-1, 1]
+            for idx, item in enumerate(self.gt_trainset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                train_labels[idx, :, :2] = item['joint_3d_image'][:, :2] / res_w * 2 - [1, res_h / res_w]
+                train_labels[idx, :, 2:] = item['joint_3d_image'][:, 2:] / res_w * 2
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                test_labels[idx, :, :2] = item['joint_3d_image'][:, :2] / res_w * 2 - [1, res_h / res_w]
+                test_labels[idx, :, 2:] = item['joint_3d_image'][:, 2:] / res_w * 2
+        else:
+            assert 0, 'not support normalize type %s' % which
+
+        # reshape
+        train_labels, test_labels = train_labels.reshape((-1, 17 * 3)).astype(np.float32), test_labels.reshape(
+            (-1, 17 * 3)
+        ).astype(np.float32)
+
+        return train_labels, test_labels
+
+    def denormalize3D(self, data, which='scale'):
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if which == 'scale':
+            data = data.reshape((-1, 17, 3)).copy()
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                if idx < len(data):
+                    data[idx, :, :2] = (data[idx, :, :2] + [1, res_h / res_w]) * res_w / 2
+                    data[idx, :, 2:] = data[idx, :, 2:] * res_w / 2
+                else:
+                    break
+        else:
+            assert 0
+        return data
+
+    def denormalize2D(self, data, which='scale'):
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if which == 'scale':
+            data = data.reshape((-1, 17, 2)).copy()
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                if idx < len(data):
+                    data[idx, :, :] = (data[idx, :, :] + [1, res_h / res_w]) * res_w / 2
+                else:
+                    break
+        else:
+            assert 0
+        return data
+
+
+def show3Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):  # blue, orange
+    """
+  Visualize a 3d skeleton
+
+  Args
+    channels: 54x1 vector. The pose to plot.
+    ax: matplotlib 3d axis to draw on
+    lcolor: color for left part of the body
+    rcolor: color for right part of the body
+    add_labels: whether to add coordinate labels
+  Returns
+    Nothing. Draws on ax.
+  """
+
+    assert channels.size == len(H36M_NAMES) * 3, "channels should have 96 entries, it has %d instead" % channels.size
+    vals = np.reshape(channels, (len(H36M_NAMES), -1))
+
+    I = np.array([0, 1, 2, 0, 4, 5, 0, 7, 8, 8, 14, 15, 8, 11, 12])  # start points
+    J = np.array([1, 2, 3, 4, 5, 6, 7, 8, 10, 14, 15, 16, 11, 12, 13])  # end points
+    LR = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
+
+    # Make connection matrix
+    for i in np.arange(len(I)):
+        x, y, z = [np.array([vals[I[i], j], vals[J[i], j]]) for j in range(3)]
+        ax.plot(x, y, z, lw=2, c=lcolor if LR[i] else rcolor)
+
+    RADIUS = 750  # space around the subject
+    xroot, yroot, zroot = vals[0, 0], vals[0, 1], vals[0, 2]
+    ax.set_xlim3d([-RADIUS + xroot, RADIUS + xroot])
+    ax.set_zlim3d([-RADIUS + zroot, RADIUS + zroot])
+    ax.set_ylim3d([-RADIUS + yroot, RADIUS + yroot])
+
+    if add_labels:
+        ax.set_xlabel("x")
+        ax.set_ylabel("y")
+        ax.set_zlabel("z")
+
+    # Get rid of the ticks and tick labels
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.set_zticks([])
+
+    ax.get_xaxis().set_ticklabels([])
+    ax.get_yaxis().set_ticklabels([])
+    ax.set_zticklabels([])
+
+    # Get rid of the panes (actually, make them white)
+    white = (1.0, 1.0, 1.0, 0.0)
+    ax.w_xaxis.set_pane_color(white)
+    ax.w_yaxis.set_pane_color(white)
+    # Keep z pane
+
+    # Get rid of the lines in 3d
+    ax.w_xaxis.line.set_color(white)
+    ax.w_yaxis.line.set_color(white)
+    ax.w_zaxis.line.set_color(white)
+
+
+def show2Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):
+    """Visualize a 2d skeleton
+
+  Args
+    channels: 34x1 vector. The pose to plot.
+    ax: matplotlib axis to draw on
+    lcolor: color for left part of the body
+    rcolor: color for right part of the body
+    add_labels: whether to add coordinate labels
+  Returns
+    Nothing. Draws on ax.
+  """
+
+    assert channels.size == len(H36M_NAMES) * 2, "channels should have 64 entries, it has %d instead" % channels.size
+    vals = np.reshape(channels, (len(H36M_NAMES), -1))
+
+    I = np.array([0, 1, 2, 0, 4, 5, 0, 7, 8, 8, 14, 15, 8, 11, 12])  # start points
+    J = np.array([1, 2, 3, 4, 5, 6, 7, 8, 10, 14, 15, 16, 11, 12, 13])  # end points
+    LR = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
+
+    # Make connection matrix
+    for i in np.arange(len(I)):
+        x, y = [np.array([vals[I[i], j], vals[J[i], j]]) for j in range(2)]
+        ax.plot(x, y, lw=2, c=lcolor if LR[i] else rcolor)
+
+    # Get rid of the ticks
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    # Get rid of tick labels
+    ax.get_xaxis().set_ticklabels([])
+    ax.get_yaxis().set_ticklabels([])
+
+    RADIUS = 350  # space around the subject
+    xroot, yroot = vals[0, 0], vals[0, 1]
+    ax.set_xlim([-RADIUS + xroot, RADIUS + xroot])
+    ax.set_ylim([-RADIUS + yroot, RADIUS + yroot])
+    if add_labels:
+        ax.set_xlabel("x")
+        ax.set_ylabel("z")
+
+    ax.set_aspect('equal')
+
+
+def visualize_3D_pose(test_data, label, result):
+    fig = plt.figure(figsize=(19.2, 10.8))
+    gs1 = gridspec.GridSpec(2, 6)  # 5 rows, 9 columns
+    gs1.update(wspace=-0.00, hspace=0.05)  # set the spacing between axes.
+    plt.axis('off')
+
+    subplot_idx, exidx = 1, 1
+    nsamples = 4
+    for i in np.arange(nsamples):
+        # Plot 2d pose
+        ax1 = plt.subplot(gs1[subplot_idx - 1])
+        p2d = test_data[exidx, :]
+        show2Dpose(p2d, ax1)
+        ax1.invert_yaxis()
+
+        # Plot 3d gt
+        ax2 = plt.subplot(gs1[subplot_idx], projection='3d')
+        p3d = label[exidx, :]
+        show3Dpose(p3d, ax2)
+
+        # Plot 3d predictions
+        ax3 = plt.subplot(gs1[subplot_idx + 1], projection='3d')
+        p3d = result[exidx, :]
+        show3Dpose(p3d, ax3, lcolor="#9b59b6", rcolor="#2ecc71")
+
+        exidx = exidx + 1
+        subplot_idx = subplot_idx + 3
+
+    plt.show()
diff --git a/tensorlayer/cost.py b/tensorlayer/cost.py
index 8ae36920d..9ccf5eeca 100644
--- a/tensorlayer/cost.py
+++ b/tensorlayer/cost.py
@@ -6,6 +6,7 @@
 import tensorflow as tf
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops, math_ops, nn_ops, standard_ops
+
 from tensorlayer import logging
 
 __all__ = [
@@ -373,7 +374,7 @@ def iou_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
 
 
 def sequence_loss_by_example(
-        logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None
+    logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None
 ):
     """Weighted cross-entropy loss for a sequence of logits (per example). see original tensorflow code :
     <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py#L1057>
@@ -781,7 +782,7 @@ def mn_i(weights, name='maxnorm_i_regularizer'):
 
 
 def huber_loss(
-        output, target, is_mean=True, delta=1.0, dynamichuber=False, reverse=False, axis=-1, epsilon=0.00001, name=None
+    output, target, is_mean=True, delta=1.0, dynamichuber=False, reverse=False, axis=-1, epsilon=0.00001, name=None
 ):
     """Huber Loss operation, see ``https://en.wikipedia.org/wiki/Huber_loss`` .
     Reverse Huber Loss operation, see  ''https://statweb.stanford.edu/~owen/reports/hhu.pdf''.
diff --git a/tensorlayer/db.py b/tensorlayer/db.py
index 0fac9f803..129e251e5 100644
--- a/tensorlayer/db.py
+++ b/tensorlayer/db.py
@@ -8,15 +8,14 @@
 from datetime import datetime
 
 import numpy as np
+import tensorflow as tf
 
 import gridfs
 import pymongo
-import tensorflow as tf
 from tensorlayer import logging
-
-from tensorlayer.files import static_graph2net, assign_weights
-from tensorlayer.files import save_weights_to_hdf5, load_hdf5_to_weights
-from tensorlayer.files import del_folder, exists_or_mkdir
+from tensorlayer.files import (
+    assign_weights, del_folder, exists_or_mkdir, load_hdf5_to_weights, save_weights_to_hdf5, static_graph2net
+)
 
 
 class TensorHub(object):
@@ -49,7 +48,7 @@ class TensorHub(object):
 
     # @deprecated_alias(db_name='dbname', user_name='username', end_support_version=2.1)
     def __init__(
-            self, ip='localhost', port=27017, dbname='dbname', username='None', password='password', project_name=None
+        self, ip='localhost', port=27017, dbname='dbname', username='None', password='password', project_name=None
     ):
         self.ip = ip
         self.port = port
diff --git a/tensorlayer/distributed.py b/tensorlayer/distributed.py
index d3fbdd38f..3b426f8f5 100644
--- a/tensorlayer/distributed.py
+++ b/tensorlayer/distributed.py
@@ -6,6 +6,7 @@
 
 import tensorflow as tf
 from tensorflow.python.training import session_run_hook
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated
 from tensorlayer.lazy_imports import LazyImport
@@ -93,9 +94,9 @@ class Trainer(object):
     """
 
     def __init__(
-            self, training_dataset, build_training_func, optimizer, optimizer_args, batch_size=32, prefetch_size=None,
-            checkpoint_dir=None, scaling_learning_rate=True, log_step_size=1, validation_dataset=None,
-            build_validation_func=None, max_iteration=float('inf')
+        self, training_dataset, build_training_func, optimizer, optimizer_args, batch_size=32, prefetch_size=None,
+        checkpoint_dir=None, scaling_learning_rate=True, log_step_size=1, validation_dataset=None,
+        build_validation_func=None, max_iteration=float('inf')
     ):
         # Initialize Horovod.
         hvd.init()
@@ -394,9 +395,9 @@ def create_task_spec_def():
 
 @deprecated(date="2018-10-30", instructions="Using the TensorLayer distributed trainer.")
 def create_distributed_session(
-        task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600,
-        save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120,
-        log_step_count_steps=100
+    task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600,
+    save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120,
+    log_step_count_steps=100
 ):
     """Creates a distributed session.
 
diff --git a/tensorlayer/files/__init__.py b/tensorlayer/files/__init__.py
index 4d88fa35d..0de8a9737 100644
--- a/tensorlayer/files/__init__.py
+++ b/tensorlayer/files/__init__.py
@@ -72,4 +72,6 @@
     #'load_graph',
     #'save_graph_and_params',
     #'load_graph_and_params',
+    'load_and_assign_ckpt',
+    'ckpt_to_npz_dict'
 ]
diff --git a/tensorlayer/files/dataset_loaders/imdb_dataset.py b/tensorlayer/files/dataset_loaders/imdb_dataset.py
index 34b4dffe0..2967e7ee6 100644
--- a/tensorlayer/files/dataset_loaders/imdb_dataset.py
+++ b/tensorlayer/files/dataset_loaders/imdb_dataset.py
@@ -13,8 +13,8 @@
 
 
 def load_imdb_dataset(
-        path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
-        index_from=3
+    path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
+    index_from=3
 ):
     """Load IMDB dataset.
 
diff --git a/tensorlayer/files/dataset_loaders/voc_dataset.py b/tensorlayer/files/dataset_loaders/voc_dataset.py
index e5124b4df..5584864ae 100644
--- a/tensorlayer/files/dataset_loaders/voc_dataset.py
+++ b/tensorlayer/files/dataset_loaders/voc_dataset.py
@@ -4,6 +4,7 @@
 import os
 
 import tensorflow as tf
+
 from tensorlayer import logging, utils
 from tensorlayer.files.utils import (del_file, del_folder, folder_exists, load_file_list, maybe_download_and_extract)
 
diff --git a/tensorlayer/files/dataset_loaders/wmt_en_fr_dataset.py b/tensorlayer/files/dataset_loaders/wmt_en_fr_dataset.py
index 77c1f93f9..0261a8581 100644
--- a/tensorlayer/files/dataset_loaders/wmt_en_fr_dataset.py
+++ b/tensorlayer/files/dataset_loaders/wmt_en_fr_dataset.py
@@ -6,6 +6,7 @@
 import tarfile
 
 from tensorflow.python.platform import gfile
+
 from tensorlayer import logging
 from tensorlayer.files.utils import maybe_download_and_extract
 
diff --git a/tensorlayer/files/utils.py b/tensorlayer/files/utils.py
index 242590c04..d0b30bc30 100644
--- a/tensorlayer/files/utils.py
+++ b/tensorlayer/files/utils.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import base64
+import datetime
 import gzip
 import json
 import math
@@ -19,24 +20,17 @@
 import h5py
 import numpy as np
 import scipy.io as sio
-from six.moves import cPickle
-
-import progressbar
 import tensorflow as tf
-import tensorlayer as tl
+from six.moves import cPickle
 from tensorflow.python.keras.saving import model_config as model_config_lib
 from tensorflow.python.platform import gfile
 from tensorflow.python.util import serialization
 from tensorflow.python.util.tf_export import keras_export
-from tensorlayer import logging, nlp, utils, visualize
+from tensorflow.python import pywrap_tensorflow
 
-import cloudpickle
-import base64
-from tensorflow.python.keras.saving import model_config as model_config_lib
-from tensorflow.python.util.tf_export import keras_export
-from tensorflow.python.util import serialization
-import json
-import datetime
+import progressbar
+import tensorlayer as tl
+from tensorlayer import logging, nlp, utils, visualize
 
 # from six.moves import zip
 
@@ -83,6 +77,8 @@
     'static_graph2net',
     # 'save_pkl_graph',
     # 'load_pkl_graph',
+    'load_and_assign_ckpt',
+    'ckpt_to_npz_dict',
 ]
 
 
@@ -846,8 +842,8 @@ def load_matt_mahoney_text8_dataset(path='data'):
 
 
 def load_imdb_dataset(
-        path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
-        index_from=3
+    path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
+    index_from=3
 ):
     """Load IMDB dataset.
 
@@ -2666,6 +2662,10 @@ def _load_weights_from_hdf5_group(f, layers, skip=False):
             elif isinstance(layer, tl.layers.Layer):
                 weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
                 for iid, w_name in enumerate(weight_names):
+                    # FIXME : this is only for compatibility
+                    if isinstance(layer, tl.layers.BatchNorm) and np.asarray(g[w_name]).ndim > 1:
+                        assign_tf_variable(layer.all_weights[iid], np.asarray(g[w_name]).squeeze())
+                        continue
                     assign_tf_variable(layer.all_weights[iid], np.asarray(g[w_name]))
             else:
                 raise Exception("Only layer or model can be saved into hdf5.")
@@ -2713,7 +2713,7 @@ def load_hdf5_to_weights_in_order(filepath, network):
     """
     f = h5py.File(filepath, 'r')
     try:
-        layer_names = [n.decode('utf8') for n in f.attrs["layer_names"]]
+        layer_names = [n if isinstance(n, str) else n.decode('utf8') for n in f.attrs["layer_names"]]
     except Exception:
         raise NameError(
             "The loaded hdf5 file needs to have 'layer_names' as attributes. "
@@ -2778,3 +2778,119 @@ def load_hdf5_to_weights(filepath, network, skip=False):
 
     f.close()
     logging.info("[*] Load %s SUCCESS!" % filepath)
+
+
+def check_ckpt_file(model_dir):
+    model_dir = model_dir
+    model_path = None
+    count_extension = 0
+    for root, dirs, files in os.walk(model_dir):
+        for file in files:
+            filename, extension = os.path.splitext(file)
+            if extension in ['.data-00000-of-00001', '.index']:
+                count_extension += 1
+        if count_extension == 2:
+            model_path = model_dir + '/' + filename
+        else:
+            raise Exception("Check the file extension for missing .data-00000-of-00001, .index")
+        if model_path is None:
+            raise Exception('The ckpt file is not found')
+    return model_path, filename
+
+
+def rename_weight_or_biases(variable_name):
+    if variable_name is None:
+        return variable_name
+    split_var = variable_name.split('/')
+
+    str_temp = ''
+    for i in range(len(split_var)):
+        if 'w' in split_var[i]:
+            split_var[i] = 'filters:0'
+        elif 'b' in split_var[i]:
+            split_var[i] = 'biases:0'
+        else:
+            pass
+
+        if i < len(split_var) - 1:
+            str_temp = str_temp + split_var[i] + '/'
+        else:
+            str_temp = str_temp + split_var[i]
+
+    return str_temp
+
+
+def load_and_assign_ckpt(model_dir, network=None, skip=True):
+    """Load weights by name from a given file of ckpt format
+
+    Parameters
+    ----------
+    model_dir : str
+        Filename to which the weights will be loaded, should be of ckpt format.
+        Examples: model_dir = /root/cnn_model/
+    network : Model
+        TL model.
+    skip : bool
+        If 'skip' == True, loaded weights whose name is not found in 'weights' will be skipped. If 'skip' is False,
+        error will be raised when mismatch is found. Default False.
+
+    Returns
+    -------
+
+    """
+    model_path, filename = check_ckpt_file(model_dir)
+
+    reader = pywrap_tensorflow.NewCheckpointReader(model_path)
+    var_to_shape_map = reader.get_variable_to_shape_map()
+
+    net_weights_name = [w.name for w in network.all_weights]
+
+    for key in var_to_shape_map:
+        if key not in net_weights_name:
+            if skip:
+                logging.warning("Weights named '%s' not found in network. Skip it." % key)
+            else:
+                raise RuntimeError(
+                    "Weights named '%s' not found in network. Hint: set argument skip=Ture "
+                    "if you want to skip redundant or mismatch weights." % key
+                )
+        else:
+            assign_tf_variable(network.all_weights[net_weights_name.index(key)], reader.get_tensor(key))
+    logging.info("[*] Model restored from ckpt %s" % filename)
+
+
+def ckpt_to_npz_dict(model_dir, save_name='model.npz', rename_key=False):
+    """ Save ckpt weights to npz file
+
+    Parameters
+    ----------
+    model_dir : str
+        Filename to which the weights will be loaded, should be of ckpt format.
+        Examples: model_dir = /root/cnn_model/
+    save_name : str
+        The save_name of the `.npz` file.
+    rename_key : bool
+        Modify parameter naming,  used to match TL naming rule.
+        Examples: conv1_1/b_b --> conv1_1/biases:0 ; conv1_1/w_w --> conv1_1/filters:0
+
+    Returns
+    -------
+
+    """
+    model_path, _ = check_ckpt_file(model_dir)
+
+    reader = pywrap_tensorflow.NewCheckpointReader(model_path)
+    var_to_shape_map = reader.get_variable_to_shape_map()
+
+    parameters_dict = {}
+    if rename_key is False:
+        for key in sorted(var_to_shape_map):
+            parameters_dict[key] = reader.get_tensor(key)
+    elif rename_key is True:
+        for key in sorted(var_to_shape_map):
+            parameters_dict[rename_weight_or_biases(key)] = reader.get_tensor(key)
+
+    np.savez(save_name, **parameters_dict)
+    parameters_dict = None
+    del parameters_dict
+    logging.info("[*] Ckpt weights saved in npz_dict %s" % save_name)
diff --git a/tensorlayer/initializers.py b/tensorlayer/initializers.py
index 7db82f839..aaf4f37ac 100644
--- a/tensorlayer/initializers.py
+++ b/tensorlayer/initializers.py
@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
 
 __all__ = [
diff --git a/tensorlayer/layers/activation.py b/tensorlayer/layers/activation.py
index 07815cb48..31abaeaba 100644
--- a/tensorlayer/layers/activation.py
+++ b/tensorlayer/layers/activation.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.activation import leaky_relu6, leaky_twice_relu6
 from tensorlayer.decorators import deprecated_alias
@@ -53,11 +54,11 @@ class PRelu(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "prelu"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "prelu"
     ):
 
         super(PRelu, self).__init__(name)
@@ -140,11 +141,11 @@ class PRelu6(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "prelu6"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "prelu6"
     ):
 
         super(PRelu6, self).__init__(name)
@@ -228,11 +229,11 @@ class PTRelu6(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "ptrelu6"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "ptrelu6"
     ):
 
         super(PTRelu6, self).__init__(name)
diff --git a/tensorlayer/layers/convolution/binary_conv.py b/tensorlayer/layers/convolution/binary_conv.py
index b54dbc762..92929ae92 100644
--- a/tensorlayer/layers/convolution/binary_conv.py
+++ b/tensorlayer/layers/convolution/binary_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -60,19 +61,19 @@ class BinaryConv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'binary_cnn2d',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'binary_cnn2d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
diff --git a/tensorlayer/layers/convolution/deformable_conv.py b/tensorlayer/layers/convolution/deformable_conv.py
index 16e3a0840..3a8038c39 100644
--- a/tensorlayer/layers/convolution/deformable_conv.py
+++ b/tensorlayer/layers/convolution/deformable_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias, private_method
@@ -70,17 +71,17 @@ class DeformableConv2d(Layer):
 
     # @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            offset_layer=None,
-            # shape=(3, 3, 1, 100),
-            n_filter=32,
-            filter_size=(3, 3),
-            act=None,
-            padding='SAME',
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'deformable_conv_2d',
+        self,
+        offset_layer=None,
+        # shape=(3, 3, 1, 100),
+        n_filter=32,
+        filter_size=(3, 3),
+        act=None,
+        padding='SAME',
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'deformable_conv_2d',
     ):
         super().__init__(name, act=act)
 
diff --git a/tensorlayer/layers/convolution/depthwise_conv.py b/tensorlayer/layers/convolution/depthwise_conv.py
index c46e60cd4..4f963d317 100644
--- a/tensorlayer/layers/convolution/depthwise_conv.py
+++ b/tensorlayer/layers/convolution/depthwise_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -68,18 +69,18 @@ class DepthwiseConv2d(Layer):
 
     # https://zhuanlan.zhihu.com/p/31551004  https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/MobileNet.py
     def __init__(
-            self,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=(1, 1),
-            depth_multiplier=1,
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'depthwise_conv2d'
+        self,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=(1, 1),
+        depth_multiplier=1,
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'depthwise_conv2d'
     ):
         super().__init__(name, act=act)
         self.filter_size = filter_size
diff --git a/tensorlayer/layers/convolution/dorefa_conv.py b/tensorlayer/layers/convolution/dorefa_conv.py
index ece1bcaef..bc80f5e3a 100644
--- a/tensorlayer/layers/convolution/dorefa_conv.py
+++ b/tensorlayer/layers/convolution/dorefa_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -64,21 +65,21 @@ class DorefaConv2d(Layer):
     """
 
     def __init__(
-            self,
-            bitW=1,
-            bitA=3,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'dorefa_cnn2d',
+        self,
+        bitW=1,
+        bitA=3,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'dorefa_cnn2d',
     ):
         super().__init__(name, act=act)
         self.bitW = bitW
diff --git a/tensorlayer/layers/convolution/expert_conv.py b/tensorlayer/layers/convolution/expert_conv.py
index eb3539eb3..062a2738c 100644
--- a/tensorlayer/layers/convolution/expert_conv.py
+++ b/tensorlayer/layers/convolution/expert_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -59,16 +60,16 @@ class Conv1dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(5, 1, 5),
-            stride=1,
-            padding='SAME',
-            data_format='NWC',
-            dilation_rate=1,
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'cnn1d_layer',
+        self,
+        act=None,
+        shape=(5, 1, 5),
+        stride=1,
+        padding='SAME',
+        data_format='NWC',
+        dilation_rate=1,
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'cnn1d_layer',
     ):
         super().__init__(name, act=act)
         self.n_filter = shape[-1]
@@ -178,16 +179,16 @@ class Conv2dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(5, 5, 1, 100),
-            strides=(1, 1, 1, 1),
-            padding='SAME',
-            data_format='NHWC',
-            dilation_rate=(1, 1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'cnn2d_layer',
+        self,
+        act=None,
+        shape=(5, 5, 1, 100),
+        strides=(1, 1, 1, 1),
+        padding='SAME',
+        data_format='NHWC',
+        dilation_rate=(1, 1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'cnn2d_layer',
     ):
         super().__init__(name, act=act)
         self.n_filter = shape[-1]
@@ -296,16 +297,16 @@ class Conv3dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(2, 2, 2, 3, 32),
-            strides=(1, 2, 2, 2, 1),
-            padding='SAME',
-            data_format='NDHWC',
-            dilation_rate=(1, 1, 1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'cnn3d_layer'
+        self,
+        act=None,
+        shape=(2, 2, 2, 3, 32),
+        strides=(1, 2, 2, 2, 1),
+        padding='SAME',
+        data_format='NDHWC',
+        dilation_rate=(1, 1, 1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'cnn3d_layer'
     ):
         super().__init__(name, act=act)
         self.n_filter = shape[-1]
diff --git a/tensorlayer/layers/convolution/expert_deconv.py b/tensorlayer/layers/convolution/expert_deconv.py
index a541b8a14..ace1f221b 100644
--- a/tensorlayer/layers/convolution/expert_deconv.py
+++ b/tensorlayer/layers/convolution/expert_deconv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -67,17 +68,17 @@ class DeConv1dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(3, 128, 256),
-            outputs_shape=(1, 256, 128),
-            strides=(1, 2, 1),
-            padding='SAME',
-            data_format='NWC',
-            dilation_rate=(1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'decnn1d_layer',
+        self,
+        act=None,
+        shape=(3, 128, 256),
+        outputs_shape=(1, 256, 128),
+        strides=(1, 2, 1),
+        padding='SAME',
+        data_format='NWC',
+        dilation_rate=(1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'decnn1d_layer',
     ):
         super().__init__(name, act=act)
         self.shape = shape
@@ -201,17 +202,17 @@ class DeConv2dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(3, 3, 128, 256),
-            outputs_shape=(1, 256, 256, 128),
-            strides=(1, 2, 2, 1),
-            padding='SAME',
-            data_format='NHWC',
-            dilation_rate=(1, 1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'decnn2d_layer',
+        self,
+        act=None,
+        shape=(3, 3, 128, 256),
+        outputs_shape=(1, 256, 256, 128),
+        strides=(1, 2, 2, 1),
+        padding='SAME',
+        data_format='NHWC',
+        dilation_rate=(1, 1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'decnn2d_layer',
     ):
         super().__init__(name, act=act)
         self.shape = shape
@@ -327,17 +328,17 @@ class DeConv3dLayer(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            shape=(2, 2, 2, 128, 256),
-            outputs_shape=(1, 12, 32, 32, 128),
-            strides=(1, 2, 2, 2, 1),
-            padding='SAME',
-            data_format='NDHWC',
-            dilation_rate=(1, 1, 1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            name=None  # 'decnn3d_layer',
+        self,
+        act=None,
+        shape=(2, 2, 2, 128, 256),
+        outputs_shape=(1, 12, 32, 32, 128),
+        strides=(1, 2, 2, 2, 1),
+        padding='SAME',
+        data_format='NDHWC',
+        dilation_rate=(1, 1, 1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        name=None  # 'decnn3d_layer',
     ):
         super().__init__(name, act=act)
         self.shape = shape
diff --git a/tensorlayer/layers/convolution/group_conv.py b/tensorlayer/layers/convolution/group_conv.py
index 262056ff9..78b7b17fa 100644
--- a/tensorlayer/layers/convolution/group_conv.py
+++ b/tensorlayer/layers/convolution/group_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -58,19 +59,19 @@ class GroupConv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(2, 2),
-            n_group=2,
-            act=None,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'groupconv',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(2, 2),
+        n_group=2,
+        act=None,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'groupconv',
     ):  # Windaway
         super().__init__(name, act=act)
         self.n_filter = n_filter
diff --git a/tensorlayer/layers/convolution/quan_conv.py b/tensorlayer/layers/convolution/quan_conv.py
index 55112993e..6d17376c8 100644
--- a/tensorlayer/layers/convolution/quan_conv.py
+++ b/tensorlayer/layers/convolution/quan_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -65,21 +66,21 @@ class QuanConv2d(Layer):
     """
 
     def __init__(
-            self,
-            bitW=8,
-            bitA=8,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'quan_cnn2d',
+        self,
+        bitW=8,
+        bitA=8,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'quan_cnn2d',
     ):
         super().__init__(name, act=act)
         self.bitW = bitW
diff --git a/tensorlayer/layers/convolution/quan_conv_bn.py b/tensorlayer/layers/convolution/quan_conv_bn.py
index bc2aec938..df20a6835 100644
--- a/tensorlayer/layers/convolution/quan_conv_bn.py
+++ b/tensorlayer/layers/convolution/quan_conv_bn.py
@@ -1,10 +1,12 @@
-# /usr/bin/python
+#! /usr/bin/python
 # -*- coding: utf-8 -*-
 
+import numpy as np
 import tensorflow as tf
 from tensorflow.python.training import moving_averages
+
+import tensorlayer as tl
 from tensorlayer import logging
-from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
 from tensorlayer.layers.utils import (quantize_active_overflow, quantize_weight_overflow)
 
@@ -21,8 +23,6 @@ class QuanConv2dWithBN(Layer):
 
     Parameters
     ----------
-    prev_layer : :class:`Layer`
-        Previous layer.
     n_filter : int
         The number of filters.
     filter_size : tuple of int
@@ -50,70 +50,71 @@ class QuanConv2dWithBN(Layer):
         The bits of this layer's parameter
     bitA : int
         The bits of the output of previous layer
-    decay : float
-        A decay factor for `ExponentialMovingAverage`.
-        Suggest to use a large value for large dataset.
-    epsilon : float
-        Eplison.
-    is_train : boolean
-        Is being used for training or inference.
-    beta_init : initializer or None
-        The initializer for initializing beta, if None, skip beta.
-        Usually you should not skip beta unless you know what happened.
-    gamma_init : initializer or None
-        The initializer for initializing gamma, if None, skip gamma.
     use_gemm : boolean
         If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO).
     W_init : initializer
         The initializer for the the weight matrix.
     W_init_args : dictionary
         The arguments for the weight matrix initializer.
-    use_cudnn_on_gpu : bool
-        Default is False.
     data_format : str
         "NHWC" or "NCHW", default is "NHWC".
+    dilation_rate : tuple of int
+        Specifying the dilation rate to use for dilated convolution.
+    in_channels : int
+        The number of in channels.
     name : str
         A unique layer name.
 
     Examples
     ---------
-    >>> import tensorflow as tf
     >>> import tensorlayer as tl
-    >>> x = tf.placeholder(tf.float32, [None, 256, 256, 3])
-    >>> net = tl.layers.InputLayer(x, name='input')
-    >>> net = tl.layers.QuanConv2dWithBN(net, 64, (5, 5), (1, 1),  act=tf.nn.relu, padding='SAME', is_train=is_train, bitW=bitW, bitA=bitA, name='qcnnbn1')
-    >>> net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-    ...
-    >>> net = tl.layers.QuanConv2dWithBN(net, 64, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, is_train=is_train,  bitW=bitW, bitA=bitA, name='qcnnbn2')
-    >>> net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
-    ...
+    >>> net = tl.layers.Input([50, 256, 256, 3])
+    >>> layer = tl.layers.QuanConv2dWithBN(n_filter=64, filter_size=(5,5),strides=(1,1),padding='SAME',name='qcnnbn1')
+    >>> print(layer)
+    >>> net = tl.layers.QuanConv2dWithBN(n_filter=64, filter_size=(5,5),strides=(1,1),padding='SAME',name='qcnnbn1')(net)
+    >>> print(net)
     """
 
-    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            prev_layer,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            padding='SAME',
-            act=None,
-            decay=0.9,
-            epsilon=1e-5,
-            is_train=False,
-            gamma_init=tf.compat.v1.initializers.ones,
-            beta_init=tf.compat.v1.initializers.zeros,
-            bitW=8,
-            bitA=8,
-            use_gemm=False,
-            W_init=tf.compat.v1.initializers.truncated_normal(stddev=0.02),
-            W_init_args=None,
-            use_cudnn_on_gpu=None,
-            data_format=None,
-            name='quan_cnn2d_bn',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        padding='SAME',
+        act=None,
+        decay=0.9,
+        epsilon=1e-5,
+        is_train=False,
+        gamma_init=tl.initializers.truncated_normal(stddev=0.02),
+        beta_init=tl.initializers.truncated_normal(stddev=0.02),
+        bitW=8,
+        bitA=8,
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        W_init_args=None,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        in_channels=None,
+        name='quan_cnn2d_bn',
     ):
-        super(QuanConv2dWithBN, self).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, name=name)
-
+        super(QuanConv2dWithBN, self).__init__(act=act, name=name)
+        self.n_filter = n_filter
+        self.filter_size = filter_size
+        self.strides = strides
+        self.padding = padding
+        self.decay = decay
+        self.epsilon = epsilon
+        self.is_train = is_train
+        self.gamma_init = gamma_init
+        self.beta_init = beta_init
+        self.bitW = bitW
+        self.bitA = bitA
+        self.use_gemm = use_gemm
+        self.W_init = W_init
+        self.W_init_args = W_init_args
+        self.data_format = data_format
+        self.dilation_rate = dilation_rate
+        self.in_channels = in_channels
         logging.info(
             "QuanConv2dWithBN %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s " % (
                 self.name, n_filter, filter_size, str(strides), padding,
@@ -121,8 +122,9 @@ def __init__(
             )
         )
 
-        x = self.inputs
-        self.inputs = quantize_active_overflow(self.inputs, bitA)  # Do not remove
+        if self.in_channels:
+            self.build(None)
+            self._built = True
 
         if use_gemm:
             raise Exception("TODO. The current version use tf.matmul for inferencing.")
@@ -130,96 +132,103 @@ def __init__(
         if len(strides) != 2:
             raise ValueError("len(strides) should be 2.")
 
-        try:
-            pre_channel = int(prev_layer.outputs.get_shape()[-1])
-        except Exception:  # if pre_channel is ?, it happens when using Spatial Transformer Net
-            pre_channel = 1
-            logging.warning("[warnings] unknow input channels, set to 1")
-
-        shape = (filter_size[0], filter_size[1], pre_channel, n_filter)
-        strides = (1, strides[0], strides[1], 1)
-
-        with tf.compat.v1.variable_scope(name):
-            W = tf.compat.v1.get_variable(
-                name='W_conv2d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
-            )
-
-            conv = tf.nn.conv2d(
-                x, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format
-            )
-
-            para_bn_shape = conv.get_shape()[-1:]
-
-            if gamma_init:
-                scale_para = tf.compat.v1.get_variable(
-                    name='scale_para', shape=para_bn_shape, initializer=gamma_init, dtype=LayersConfig.tf_dtype,
-                    trainable=is_train
-                )
-            else:
-                scale_para = None
-
-            if beta_init:
-                offset_para = tf.compat.v1.get_variable(
-                    name='offset_para', shape=para_bn_shape, initializer=beta_init, dtype=LayersConfig.tf_dtype,
-                    trainable=is_train
-                )
-            else:
-                offset_para = None
-
-            moving_mean = tf.compat.v1.get_variable(
-                'moving_mean', para_bn_shape, initializer=tf.compat.v1.initializers.constant(1.),
-                dtype=LayersConfig.tf_dtype, trainable=False
+    def __repr__(self):
+        actstr = self.act.__name__ if self.act is not None else 'No Activation'
+        s = (
+            '{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
+            ', strides={strides}, padding={padding}' + actstr
+        )
+        if self.dilation_rate != (1, ) * len(self.dilation_rate):
+            s += ', dilation={dilation_rate}'
+        if self.name is not None:
+            s += ', name=\'{name}\''
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    def build(self, inputs_shape):
+        if self.data_format == 'channels_last':
+            self.data_format = 'NHWC'
+            if self.in_channels is None:
+                self.in_channels = inputs_shape[-1]
+            self._strides = [1, self.strides[0], self.strides[1], 1]
+            self._dilation_rate = [1, self.dilation_rate[0], self.dilation_rate[1], 1]
+        elif self.data_format == 'channels_first':
+            self.data_format = 'NCHW'
+            if self.in_channels is None:
+                self.in_channels = inputs_shape[1]
+            self._strides = [1, 1, self.strides[0], self.strides[1]]
+            self._dilation_rate = [1, 1, self.dilation_rate[0], self.dilation_rate[1]]
+        else:
+            raise Exception("data_format should be either channels_last or channels_first")
+
+        self.filter_shape = (self.filter_size[0], self.filter_size[1], self.in_channels, self.n_filter)
+        self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
+
+        para_bn_shape = (self.n_filter, )
+        if self.gamma_init:
+            self.scale_para = self._get_weights(
+                "scale_para", shape=para_bn_shape, init=self.gamma_init, trainable=self.is_train
             )
+        else:
+            self.scale_para = None
 
-            moving_variance = tf.compat.v1.get_variable(
-                'moving_variance',
-                para_bn_shape,
-                initializer=tf.compat.v1.initializers.constant(1.),
-                dtype=LayersConfig.tf_dtype,
-                trainable=False,
+        if self.beta_init:
+            self.offset_para = self._get_weights(
+                "offset_para", shape=para_bn_shape, init=self.beta_init, trainable=self.is_train
             )
+        else:
+            self.offset_para = None
 
-            mean, variance = tf.nn.moments(x=conv, axes=list(range(len(conv.get_shape()) - 1)))
-
-            update_moving_mean = moving_averages.assign_moving_average(
-                moving_mean, mean, decay, zero_debias=False
-            )  # if zero_debias=True, has bias
-
-            update_moving_variance = moving_averages.assign_moving_average(
-                moving_variance, variance, decay, zero_debias=False
-            )  # if zero_debias=True, has bias
+        self.moving_mean = self._get_weights(
+            "moving_mean", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
+        )
+        self.moving_variance = self._get_weights(
+            "moving_variance", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
+        )
 
-            def mean_var_with_update():
-                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
-                    return tf.identity(mean), tf.identity(variance)
+    def forward(self, inputs):
+        x = inputs
+        inputs = quantize_active_overflow(inputs, self.bitA)  # Do not remove
+        outputs = tf.nn.conv2d(
+            input=x, filters=self.W, strides=self._strides, padding=self.padding, data_format=self.data_format,
+            dilations=self._dilation_rate, name=self.name
+        )
 
-            if is_train:
-                mean, var = mean_var_with_update()
-            else:
-                mean, var = moving_mean, moving_variance
+        mean, variance = tf.nn.moments(outputs, axes=list(range(len(outputs.get_shape()) - 1)))
 
-            w_fold = _w_fold(W, scale_para, var, epsilon)
-            bias_fold = _bias_fold(offset_para, scale_para, mean, var, epsilon)
+        update_moving_mean = moving_averages.assign_moving_average(
+            self.moving_mean, mean, self.decay, zero_debias=False
+        )  # if zero_debias=True, has bias
+        update_moving_variance = moving_averages.assign_moving_average(
+            self.moving_variance, mean, self.decay, zero_debias=False
+        )  # if zero_debias=True, has bias
 
-            W = quantize_weight_overflow(w_fold, bitW)
+        if self.is_train:
+            mean, var = self.mean_var_with_update(update_moving_mean, update_moving_variance, mean, variance)
+        else:
+            mean, var = self.moving_mean, self.moving_variance
 
-            conv_fold = tf.nn.conv2d(
-                self.inputs, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
-                data_format=data_format
-            )
+        w_fold = self._w_fold(self.W, self.scale_para, var, self.epsilon)
 
-            self.outputs = tf.nn.bias_add(conv_fold, bias_fold, name='bn_bias_add')
+        W_ = quantize_weight_overflow(w_fold, self.bitW)
 
-            self.outputs = self._apply_activation(self.outputs)
+        conv_fold = tf.nn.conv2d(inputs, W_, strides=self.strides, padding=self.padding, data_format=self.data_format)
 
-        self._add_layers(self.outputs)
+        if self.beta_init:
+            bias_fold = self._bias_fold(self.offset_para, self.scale_para, mean, var, self.epsilon)
+            conv_fold = tf.nn.bias_add(conv_fold, bias_fold, name='bn_bias_add')
 
-        self._add_params([W, scale_para, offset_para, moving_mean, moving_variance])
+        if self.act:
+            conv_fold = self.act(conv_fold)
 
+        return conv_fold
 
-def _w_fold(w, gama, var, epsilon):
-    return tf.compat.v1.div(tf.multiply(gama, w), tf.sqrt(var + epsilon))
+    def mean_var_with_update(self, update_moving_mean, update_moving_variance, mean, variance):
+        with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+            return tf.identity(mean), tf.identity(variance)
 
+    def _w_fold(self, w, gama, var, epsilon):
+        return tf.compat.v1.div(tf.multiply(gama, w), tf.sqrt(var + epsilon))
 
-def _bias_fold(beta, gama, mean, var, epsilon):
-    return tf.subtract(beta, tf.compat.v1.div(tf.multiply(gama, mean), tf.sqrt(var + epsilon)))
+    def _bias_fold(self, beta, gama, mean, var, epsilon):
+        return tf.subtract(beta, tf.compat.v1.div(tf.multiply(gama, mean), tf.sqrt(var + epsilon)))
diff --git a/tensorlayer/layers/convolution/separable_conv.py b/tensorlayer/layers/convolution/separable_conv.py
index 462b639f5..156a5f80d 100644
--- a/tensorlayer/layers/convolution/separable_conv.py
+++ b/tensorlayer/layers/convolution/separable_conv.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -61,28 +61,28 @@ class SeparableConv1d(Layer):
 
     # @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            n_filter=100,
-            filter_size=3,
-            strides=1,
-            act=None,
-            padding='valid',
-            data_format='channels_last',
-            dilation_rate=1,
-            depth_multiplier=1,
-            depthwise_init=None,
-            pointwise_init=None,
-            b_init=tl.initializers.constant(value=0.0),
-            # depthwise_regularizer=None,
-            # pointwise_regularizer=None,
-            # bias_regularizer=None,
-            # activity_regularizer=None,
-            # depthwise_constraint=None,
-            # pointwise_constraint=None,
-            # W_init=tf.truncated_normal_initializer(stddev=0.1),
-            # b_init=tf.constant_initializer(value=0.0),
-            in_channels=None,
-            name=None  # 'seperable1d',
+        self,
+        n_filter=100,
+        filter_size=3,
+        strides=1,
+        act=None,
+        padding='valid',
+        data_format='channels_last',
+        dilation_rate=1,
+        depth_multiplier=1,
+        depthwise_init=None,
+        pointwise_init=None,
+        b_init=tl.initializers.constant(value=0.0),
+        # depthwise_regularizer=None,
+        # pointwise_regularizer=None,
+        # bias_regularizer=None,
+        # activity_regularizer=None,
+        # depthwise_constraint=None,
+        # pointwise_constraint=None,
+        # W_init=tf.truncated_normal_initializer(stddev=0.1),
+        # b_init=tf.constant_initializer(value=0.0),
+        in_channels=None,
+        name=None  # 'seperable1d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
@@ -208,28 +208,28 @@ class SeparableConv2d(Layer):
 
     # @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            n_filter=100,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='valid',
-            data_format='channels_last',
-            dilation_rate=(1, 1),
-            depth_multiplier=1,
-            depthwise_init=None,
-            pointwise_init=None,
-            b_init=tl.initializers.constant(value=0.0),
-            # depthwise_regularizer=None,
-            # pointwise_regularizer=None,
-            # bias_regularizer=None,
-            # activity_regularizer=None,
-            # depthwise_constraint=None,
-            # pointwise_constraint=None,
-            # W_init=tf.truncated_normal_initializer(stddev=0.1),
-            # b_init=tf.constant_initializer(value=0.0),
-            in_channels=None,
-            name=None  # 'seperable2d',
+        self,
+        n_filter=100,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='valid',
+        data_format='channels_last',
+        dilation_rate=(1, 1),
+        depth_multiplier=1,
+        depthwise_init=None,
+        pointwise_init=None,
+        b_init=tl.initializers.constant(value=0.0),
+        # depthwise_regularizer=None,
+        # pointwise_regularizer=None,
+        # bias_regularizer=None,
+        # activity_regularizer=None,
+        # depthwise_constraint=None,
+        # pointwise_constraint=None,
+        # W_init=tf.truncated_normal_initializer(stddev=0.1),
+        # b_init=tf.constant_initializer(value=0.0),
+        in_channels=None,
+        name=None  # 'seperable2d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
diff --git a/tensorlayer/layers/convolution/simplified_conv.py b/tensorlayer/layers/convolution/simplified_conv.py
index d33bb7593..fab3d5817 100644
--- a/tensorlayer/layers/convolution/simplified_conv.py
+++ b/tensorlayer/layers/convolution/simplified_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -56,18 +57,18 @@ class Conv1d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=5,
-            stride=1,
-            act=None,
-            padding='SAME',
-            data_format="channels_last",
-            dilation_rate=1,
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'conv1d'
+        self,
+        n_filter=32,
+        filter_size=5,
+        stride=1,
+        act=None,
+        padding='SAME',
+        data_format="channels_last",
+        dilation_rate=1,
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'conv1d'
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
@@ -186,18 +187,18 @@ class Conv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'conv2d',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'conv2d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
@@ -319,18 +320,18 @@ class Conv3d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3, 3),
-            strides=(1, 1, 1),
-            act=None,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=(1, 1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'conv3d',
+        self,
+        n_filter=32,
+        filter_size=(3, 3, 3),
+        strides=(1, 1, 1),
+        act=None,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=(1, 1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'conv3d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
diff --git a/tensorlayer/layers/convolution/simplified_deconv.py b/tensorlayer/layers/convolution/simplified_deconv.py
index 13431b7bd..8e967c114 100644
--- a/tensorlayer/layers/convolution/simplified_deconv.py
+++ b/tensorlayer/layers/convolution/simplified_deconv.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -58,18 +58,18 @@ class DeConv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(2, 2),
-            act=None,
-            padding='SAME',
-            dilation_rate=(1, 1),
-            data_format='channels_last',
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'decnn2d'
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(2, 2),
+        act=None,
+        padding='SAME',
+        dilation_rate=(1, 1),
+        data_format='channels_last',
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'decnn2d'
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
@@ -82,10 +82,10 @@ def __init__(
         self.b_init = b_init
         self.in_channels = in_channels
 
-        # Attention: To build, we need not only the in_channels!
-        # if self.in_channels:
-        #     self.build(None)
-        #     self._built = True
+        # Attention: To build, we need not only the in_channels! Solved.
+        if self.in_channels is not None:
+            self.build(None)
+            self._built = True
 
         logging.info(
             "DeConv2d {}: n_filters: {} strides: {} padding: {} act: {} dilation: {}".format(
@@ -132,10 +132,13 @@ def build(self, inputs_shape):
             # dtype=tf.float32,
             name=self.name,
         )
-        if self.data_format == "channels_first":
-            self.in_channels = inputs_shape[1]
+        if inputs_shape is not None:
+            self.in_channels = inputs_shape[1 if self.data_format == "channels_first" else -1]
+        elif self.in_channels is not None:
+            inputs_shape = [1, self.in_channels, 1, 1
+                           ] if self.data_format == "channels_first" else [1, 1, 1, self.in_channels]
         else:
-            self.in_channels = inputs_shape[-1]
+            raise ValueError("Either inputs_shape or in_channels must be specified for build.")
         _out = self.layer(
             tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
         )  #np.random.uniform([1] + list(inputs_shape)))  # initialize weights
@@ -186,17 +189,17 @@ class DeConv3d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3, 3),
-            strides=(2, 2, 2),
-            padding='SAME',
-            act=None,
-            data_format='channels_last',
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'decnn3d'
+        self,
+        n_filter=32,
+        filter_size=(3, 3, 3),
+        strides=(2, 2, 2),
+        padding='SAME',
+        act=None,
+        data_format='channels_last',
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'decnn3d'
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
@@ -206,12 +209,12 @@ def __init__(
         self.data_format = data_format
         self.W_init = W_init
         self.b_init = b_init
-        self.in_channels = in_channels,
+        self.in_channels = in_channels
 
-        # Attention: To build, we need not only the in_channels!
-        # if self.in_channels:
-        #     self.build(None)
-        #     self._built = True
+        # Attention: To build, we need not only the in_channels! Solved.
+        if self.in_channels is not None:
+            self.build(None)
+            self._built = True
 
         logging.info(
             "DeConv3d %s: n_filters: %s strides: %s pad: %s act: %s" % (
@@ -252,16 +255,17 @@ def build(self, inputs_shape):
             bias_initializer=self.b_init,
             name=self.name,
         )
-        if self.data_format == "channels_first":
-            self.in_channels = inputs_shape[1]
+        if inputs_shape is not None:
+            self.in_channels = inputs_shape[1 if self.data_format == "channels_first" else -1]
+        elif self.in_channels is not None:
+            inputs_shape = [1, self.in_channels, 1, 1, 1
+                           ] if self.data_format == "channels_first" else [1, 1, 1, 1, self.in_channels]
         else:
-            self.in_channels = inputs_shape[-1]
-
+            raise ValueError("Either inputs_shape or in_channels must be specified for build.")
         _out = self.layer(
             tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
         )  #self.layer(np.random.uniform([1] + list(inputs_shape)))  # initialize weights
         outputs_shape = _out.shape
-        # self._add_weights(self.layer.weights)
         self._trainable_weights = self.layer.weights
 
     def forward(self, inputs):
diff --git a/tensorlayer/layers/convolution/super_resolution.py b/tensorlayer/layers/convolution/super_resolution.py
index c29daa3c1..5bdbd24c7 100644
--- a/tensorlayer/layers/convolution/super_resolution.py
+++ b/tensorlayer/layers/convolution/super_resolution.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias, private_method
@@ -46,11 +47,11 @@ class SubpixelConv1d(Layer):
     """
 
     def __init__(
-            self,
-            scale=2,
-            act=None,
-            in_channels=None,
-            name=None  # 'subpixel_conv1d'
+        self,
+        scale=2,
+        act=None,
+        in_channels=None,
+        name=None  # 'subpixel_conv1d'
     ):
         super().__init__(name, act=act)
         self.scale = scale
@@ -79,7 +80,6 @@ def build(self, inputs_shape):
         if inputs_shape is not None:
             self.in_channels = inputs_shape[-1]
         self.out_channels = int(self.in_channels / self.scale)
-        pass
 
     def forward(self, inputs):
         outputs = self._PS(inputs, r=self.scale)
@@ -141,12 +141,12 @@ class SubpixelConv2d(Layer):
 
     # github/Tetrachrome/subpixel  https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py
     def __init__(
-            self,
-            scale=2,
-            n_out_channels=None,
-            act=None,
-            in_channels=None,
-            name=None  # 'subpixel_conv2d'
+        self,
+        scale=2,
+        n_out_channels=None,
+        act=None,
+        in_channels=None,
+        name=None  # 'subpixel_conv2d'
     ):
         super().__init__(name, act=act)
         self.scale = scale
diff --git a/tensorlayer/layers/convolution/ternary_conv.py b/tensorlayer/layers/convolution/ternary_conv.py
index 421b46ff2..a75630a9f 100644
--- a/tensorlayer/layers/convolution/ternary_conv.py
+++ b/tensorlayer/layers/convolution/ternary_conv.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -60,19 +61,19 @@ class TernaryConv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'ternary_cnn2d',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'ternary_cnn2d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter
diff --git a/tensorlayer/layers/core.py b/tensorlayer/layers/core.py
index 118ed0dc8..023d510a2 100644
--- a/tensorlayer/layers/core.py
+++ b/tensorlayer/layers/core.py
@@ -5,6 +5,7 @@
 from abc import abstractmethod
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import (deprecated_alias, private_method, protected_method)
@@ -344,7 +345,7 @@ def __delitem__(self, key):
 
     def __setattr__(self, key, value):
         if isinstance(value, Layer):
-            value._nodes_fixed = True
+            value._fix_nodes_for_layers()
             if self._layers is None:
                 self._layers = []
             self._layers.append(value)
@@ -386,15 +387,11 @@ def _get_init_args(self, skip=3):
 
                 # change function (e.g. act) into dictionary of module path and function name
                 if inspect.isfunction(val):
-                    if ("__module__" in dir(val)) and (len(val.__module__) >
-                                                       10) and (val.__module__[0:10] == "tensorflow"):
+                    if ("__module__" in dir(val)) and (len(val.__module__) > 10) and (val.__module__[0:10]
+                                                                                      == "tensorflow"):
                         params[arg] = val.__name__
                     else:
                         params[arg] = ('is_Func', utils.func2str(val))
-                    # if val.__name__ == "<lambda>":
-                    #     params[arg] = utils.lambda2str(val)
-                    # else:
-                    #     params[arg] = {"module_path": val.__module__, "func_name": val.__name__}
                 # ignore more args e.g. TL initializer
                 elif arg.endswith('init'):
                     continue
diff --git a/tensorlayer/layers/dense/__init__.py b/tensorlayer/layers/dense/__init__.py
index 87b064f0c..557fbd070 100644
--- a/tensorlayer/layers/dense/__init__.py
+++ b/tensorlayer/layers/dense/__init__.py
@@ -24,5 +24,5 @@
     'DropconnectDense',
     'TernaryDense',
     'QuanDense',
-    'QuanDenseLayerWithBN',
+    'QuanDenseWithBN',
 ]
diff --git a/tensorlayer/layers/dense/base_dense.py b/tensorlayer/layers/dense/base_dense.py
index 96b807fd5..c24080432 100644
--- a/tensorlayer/layers/dense/base_dense.py
+++ b/tensorlayer/layers/dense/base_dense.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -54,13 +54,13 @@ class Dense(Layer):
     """
 
     def __init__(
-            self,
-            n_units,
-            act=None,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  # 'dense',
+        self,
+        n_units,
+        act=None,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  # 'dense',
     ):
 
         super(Dense, self).__init__(name, act=act)
diff --git a/tensorlayer/layers/dense/binary_dense.py b/tensorlayer/layers/dense/binary_dense.py
index 3919f30d0..d4d152ac0 100644
--- a/tensorlayer/layers/dense/binary_dense.py
+++ b/tensorlayer/layers/dense/binary_dense.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -39,14 +40,14 @@ class BinaryDense(Layer):
     """
 
     def __init__(
-            self,
-            n_units=100,
-            act=None,
-            use_gemm=False,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  #'binary_dense',
+        self,
+        n_units=100,
+        act=None,
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  #'binary_dense',
     ):
         super().__init__(name, act=act)
         self.n_units = n_units
diff --git a/tensorlayer/layers/dense/dorefa_dense.py b/tensorlayer/layers/dense/dorefa_dense.py
index a8a56143d..4bc4f40df 100644
--- a/tensorlayer/layers/dense/dorefa_dense.py
+++ b/tensorlayer/layers/dense/dorefa_dense.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -44,16 +45,16 @@ class DorefaDense(Layer):
     """
 
     def __init__(
-            self,
-            bitW=1,
-            bitA=3,
-            n_units=100,
-            act=None,
-            use_gemm=False,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  #'dorefa_dense',
+        self,
+        bitW=1,
+        bitA=3,
+        n_units=100,
+        act=None,
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  #'dorefa_dense',
     ):
         super().__init__(name, act=act)
         self.bitW = bitW
diff --git a/tensorlayer/layers/dense/dropconnect.py b/tensorlayer/layers/dense/dropconnect.py
index 486a57799..43c3a144a 100644
--- a/tensorlayer/layers/dense/dropconnect.py
+++ b/tensorlayer/layers/dense/dropconnect.py
@@ -4,6 +4,7 @@
 import numbers
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -56,14 +57,14 @@ class DropconnectDense(Layer):
     """
 
     def __init__(
-            self,
-            keep=0.5,
-            n_units=100,
-            act=None,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  # 'dropconnect',
+        self,
+        keep=0.5,
+        n_units=100,
+        act=None,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  # 'dropconnect',
     ):
         super().__init__(name, act=act)
 
diff --git a/tensorlayer/layers/dense/quan_dense.py b/tensorlayer/layers/dense/quan_dense.py
index 6817ed8b2..67ca73074 100644
--- a/tensorlayer/layers/dense/quan_dense.py
+++ b/tensorlayer/layers/dense/quan_dense.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -42,16 +43,16 @@ class QuanDense(Layer):
     """
 
     def __init__(
-            self,
-            n_units=100,
-            act=None,
-            bitW=8,
-            bitA=8,
-            use_gemm=False,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  #'quan_dense',
+        self,
+        n_units=100,
+        act=None,
+        bitW=8,
+        bitA=8,
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  #'quan_dense',
     ):
         super().__init__(name, act=act)
         self.n_units = n_units
diff --git a/tensorlayer/layers/dense/quan_dense_bn.py b/tensorlayer/layers/dense/quan_dense_bn.py
index c60464baf..9270f548d 100644
--- a/tensorlayer/layers/dense/quan_dense_bn.py
+++ b/tensorlayer/layers/dense/quan_dense_bn.py
@@ -4,24 +4,24 @@
 import tensorflow as tf
 # from tensorlayer.layers.core import LayersConfig
 from tensorflow.python.training import moving_averages
+
+import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
 from tensorlayer.layers.utils import (quantize_active_overflow, quantize_weight_overflow)
 
 __all__ = [
-    'QuanDenseLayerWithBN',
+    'QuanDenseWithBN',
 ]
 
 
-class QuanDenseLayerWithBN(Layer):
-    """The :class:`QuanDenseLayerWithBN` class is a quantized fully connected layer with BN, which weights are 'bitW' bits and the output of the previous layer
+class QuanDenseWithBN(Layer):
+    """The :class:`QuanDenseWithBN` class is a quantized fully connected layer with BN, which weights are 'bitW' bits and the output of the previous layer
     are 'bitA' bits while inferencing.
 
     Parameters
     ----------
-    prev_layer : :class:`Layer`
-        Previous layer.
     n_units : int
         The number of units of this layer.
     act : activation function
@@ -42,146 +42,153 @@ class QuanDenseLayerWithBN(Layer):
         The bits of this layer's parameter
     bitA : int
         The bits of the output of previous layer
-    decay : float
-        A decay factor for `ExponentialMovingAverage`.
-        Suggest to use a large value for large dataset.
-    epsilon : float
-        Eplison.
-    is_train : boolean
-        Is being used for training or inference.
-    beta_init : initializer or None
-        The initializer for initializing beta, if None, skip beta.
-        Usually you should not skip beta unless you know what happened.
-    gamma_init : initializer or None
-        The initializer for initializing gamma, if None, skip gamma.
     use_gemm : boolean
         If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO).
     W_init : initializer
         The initializer for the the weight matrix.
     W_init_args : dictionary
         The arguments for the weight matrix initializer.
+    in_channels: int
+        The number of channels of the previous layer.
+        If None, it will be automatically detected when the layer is forwarded for the first time.
     name : a str
         A unique layer name.
 
+    Examples
+    ---------
+    >>> import tensorlayer as tl
+    >>> net = tl.layers.Input([50, 256])
+    >>> layer = tl.layers.QuanDenseWithBN(128, act='relu', name='qdbn1')(net)
+    >>> print(layer)
+    >>> net = tl.layers.QuanDenseWithBN(256, act='relu', name='qdbn2')(net)
+    >>> print(net)
     """
 
-    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            prev_layer,
-            n_units=100,
-            act=None,
-            decay=0.9,
-            epsilon=1e-5,
-            is_train=False,
-            bitW=8,
-            bitA=8,
-            gamma_init=tf.compat.v1.initializers.ones,
-            beta_init=tf.compat.v1.initializers.zeros,
-            use_gemm=False,
-            W_init=tf.compat.v1.initializers.truncated_normal(stddev=0.05),
-            W_init_args=None,
-            name=None,  #'quan_dense_with_bn',
+        self,
+        n_units=100,
+        act=None,
+        decay=0.9,
+        epsilon=1e-5,
+        is_train=False,
+        bitW=8,
+        bitA=8,
+        gamma_init=tl.initializers.truncated_normal(stddev=0.05),
+        beta_init=tl.initializers.truncated_normal(stddev=0.05),
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        W_init_args=None,
+        in_channels=None,
+        name=None,  # 'quan_dense_with_bn',
     ):
-        super(QuanDenseLayerWithBN, self).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, name=name)
+        super(QuanDenseWithBN, self).__init__(act=act, W_init_args=W_init_args, name=name)
+        self.n_units = n_units
+        self.decay = decay
+        self.epsilon = epsilon
+        self.is_train = is_train
+        self.bitW = bitW
+        self.bitA = bitA
+        self.gamma_init = gamma_init
+        self.beta_init = beta_init
+        self.use_gemm = use_gemm
+        self.W_init = W_init
+        self.in_channels = in_channels
+
+        if self.in_channels is not None:
+            self.build((None, self.in_channels))
+            self._built = True
 
         logging.info(
             "QuanDenseLayerWithBN  %s: %d %s" %
             (self.name, n_units, self.act.__name__ if self.act is not None else 'No Activation')
         )
 
-        if self.inputs.get_shape().ndims != 2:
+    def __repr__(self):
+        actstr = self.act.__name__ if self.act is not None else 'No Activation'
+        s = ('{classname}(n_units={n_units}, ' + actstr)
+        s += ', bitW={bitW}, bitA={bitA}'
+        if self.in_channels is not None:
+            s += ', in_channels=\'{in_channels}\''
+        if self.name is not None:
+            s += ', name=\'{name}\''
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    def build(self, inputs_shape):
+        if self.in_channels is None and len(inputs_shape) != 2:
             raise Exception("The input dimension must be rank 2, please reshape or flatten it")
 
-        if use_gemm:
-            raise Exception("TODO. The current version use tf.matmul for inferencing.")
-
-        n_in = int(self.inputs.get_shape()[-1])
-        x = self.inputs
-        self.inputs = quantize_active_overflow(self.inputs, bitA)
-        self.n_units = n_units
-
-        with tf.compat.v1.variable_scope(name):
-
-            W = tf.compat.v1.get_variable(
-                name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
-            )
-
-            mid_out = tf.matmul(x, W)
-
-            para_bn_shape = mid_out.get_shape()[-1:]
+        if self.in_channels is None:
+            self.in_channels = inputs_shape[1]
 
-            if gamma_init:
-                scale_para = tf.compat.v1.get_variable(
-                    name='scale_para', shape=para_bn_shape, initializer=gamma_init, dtype=LayersConfig.tf_dtype,
-                    trainable=is_train
-                )
-            else:
-                scale_para = None
-
-            if beta_init:
-                offset_para = tf.compat.v1.get_variable(
-                    name='offset_para', shape=para_bn_shape, initializer=beta_init, dtype=LayersConfig.tf_dtype,
-                    trainable=is_train
-                )
-            else:
-                offset_para = None
-
-            moving_mean = tf.compat.v1.get_variable(
-                'moving_mean', para_bn_shape, initializer=tf.compat.v1.initializers.constant(1.),
-                dtype=LayersConfig.tf_dtype, trainable=False
-            )
-
-            moving_variance = tf.compat.v1.get_variable(
-                'moving_variance',
-                para_bn_shape,
-                initializer=tf.compat.v1.initializers.constant(1.),
-                dtype=LayersConfig.tf_dtype,
-                trainable=False,
-            )
+        if self.use_gemm:
+            raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
-            mean, variance = tf.nn.moments(x=mid_out, axes=list(range(len(mid_out.get_shape()) - 1)))
+        n_in = inputs_shape[-1]
+        self.W = self._get_weights("weights", shape=(n_in, self.n_units), init=self.W_init)
 
-            update_moving_mean = moving_averages.assign_moving_average(
-                moving_mean, mean, decay, zero_debias=False
-            )  # if zero_debias=True, has bias
+        para_bn_shape = (self.n_units, )
+        if self.gamma_init:
+            self.scale_para = self._get_weights("gamm_weights", shape=para_bn_shape, init=self.gamma_init)
+        else:
+            self.scale_para = None
 
-            update_moving_variance = moving_averages.assign_moving_average(
-                moving_variance, variance, decay, zero_debias=False
-            )  # if zero_debias=True, has bias
+        if self.beta_init:
+            self.offset_para = self._get_weights("beta_weights", shape=para_bn_shape, init=self.beta_init)
+        else:
+            self.offset_para = None
 
-            def mean_var_with_update():
-                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
-                    return tf.identity(mean), tf.identity(variance)
+        self.moving_mean = self._get_weights(
+            "moving_mean", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
+        )
+        self.moving_variance = self._get_weights(
+            "moving_variacne", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
+        )
 
-            if is_train:
-                mean, var = mean_var_with_update()
-            else:
-                mean, var = moving_mean, moving_variance
+    def forward(self, inputs):
+        x = inputs
+        inputs = quantize_active_overflow(inputs, self.bitA)
+        mid_out = tf.matmul(x, self.W)
 
-            w_fold = _w_fold(W, scale_para, var, epsilon)
-            bias_fold = _bias_fold(offset_para, scale_para, mean, var, epsilon)
+        mean, variance = tf.nn.moments(x=mid_out, axes=list(range(len(mid_out.get_shape()) - 1)))
 
-            W = quantize_weight_overflow(w_fold, bitW)
-            # W = tl.act.sign(W)    # dont update ...
+        update_moving_mean = moving_averages.assign_moving_average(
+            self.moving_mean, mean, self.decay, zero_debias=False
+        )  # if zero_debias=True, has bias
 
-            # W = tf.Variable(W)
+        update_moving_variance = moving_averages.assign_moving_average(
+            self.moving_variance, variance, self.decay, zero_debias=False
+        )  # if zero_debias=True, has bias
 
-            self.outputs = tf.matmul(self.inputs, W)
-            # self.outputs = xnor_gemm(self.inputs, W) # TODO
+        if self.is_train:
+            mean, var = self.mean_var_with_update(update_moving_mean, update_moving_variance, mean, variance)
+        else:
+            mean, var = self.moving_mean, self.moving_variance
 
-            self.outputs = tf.nn.bias_add(self.outputs, bias_fold, name='bias_add')
+        w_fold = self._w_fold(self.W, self.scale_para, var, self.epsilon)
 
-            self.outputs = self._apply_activation(self.outputs)
+        W = quantize_weight_overflow(w_fold, self.bitW)
 
-        self._add_layers(self.outputs)
+        outputs = tf.matmul(inputs, W)
 
-        self._add_params([W, scale_para, offset_para, moving_mean, moving_variance])
+        if self.beta_init:
+            bias_fold = self._bias_fold(self.offset_para, self.scale_para, mean, var, self.epsilon)
+            outputs = tf.nn.bias_add(outputs, bias_fold, name='bias_add')
+        else:
+            outputs = outputs
 
+        if self.act:
+            outputs = self.act(outputs)
+        else:
+            outputs = outputs
+        return outputs
 
-def _w_fold(w, gama, var, epsilon):
-    return tf.compat.v1.div(tf.multiply(gama, w), tf.sqrt(var + epsilon))
+    def mean_var_with_update(self, update_moving_mean, update_moving_variance, mean, variance):
+        with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+            return tf.identity(mean), tf.identity(variance)
 
+    def _w_fold(self, w, gama, var, epsilon):
+        return tf.compat.v1.div(tf.multiply(gama, w), tf.sqrt(var + epsilon))
 
-def _bias_fold(beta, gama, mean, var, epsilon):
-    return tf.subtract(beta, tf.compat.v1.div(tf.multiply(gama, mean), tf.sqrt(var + epsilon)))
+    def _bias_fold(self, beta, gama, mean, var, epsilon):
+        return tf.subtract(beta, tf.compat.v1.div(tf.multiply(gama, mean), tf.sqrt(var + epsilon)))
diff --git a/tensorlayer/layers/dense/ternary_dense.py b/tensorlayer/layers/dense/ternary_dense.py
index 450e7cfeb..49479df7c 100644
--- a/tensorlayer/layers/dense/ternary_dense.py
+++ b/tensorlayer/layers/dense/ternary_dense.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -39,14 +40,14 @@ class TernaryDense(Layer):
     """
 
     def __init__(
-            self,
-            n_units=100,
-            act=None,
-            use_gemm=False,
-            W_init=tl.initializers.truncated_normal(stddev=0.05),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None,  #'ternary_dense',
+        self,
+        n_units=100,
+        act=None,
+        use_gemm=False,
+        W_init=tl.initializers.truncated_normal(stddev=0.05),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None,  #'ternary_dense',
     ):
         super().__init__(name, act=act)
         self.n_units = n_units
diff --git a/tensorlayer/layers/deprecated.py b/tensorlayer/layers/deprecated.py
index c7a4f4bb2..2cb6699c0 100644
--- a/tensorlayer/layers/deprecated.py
+++ b/tensorlayer/layers/deprecated.py
@@ -91,6 +91,16 @@ def DropconnectDenseLayer(*args, **kwargs):
     raise NonExistingLayerError("DropconnectDenseLayer(net, name='a') --> DropconnectDense(name='a')(net)" + __log__)
 
 
+# dense/quan_dense_bn.py
+__all__ += [
+    'QuanDenseLayerWithBN',
+]
+
+
+def QuanDenseLayerWithBN(*args, **kwargs):
+    raise NonExistingLayerError("QuanDenseLayerWithBN(net, name='a') --> QuanDenseWithBN(name='a')(net)" + __log__)
+
+
 # dense/ternary_dense.py
 __all__ += [
     'TernaryDenseLayer',
diff --git a/tensorlayer/layers/dropout.py b/tensorlayer/layers/dropout.py
index 25fe80a36..3724d8b43 100644
--- a/tensorlayer/layers/dropout.py
+++ b/tensorlayer/layers/dropout.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
diff --git a/tensorlayer/layers/embedding.py b/tensorlayer/layers/embedding.py
index a82c1a93b..9d0d882d1 100644
--- a/tensorlayer/layers/embedding.py
+++ b/tensorlayer/layers/embedding.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.layers.core import Layer
@@ -186,16 +186,16 @@ class Word2vecEmbedding(Layer):
     """
 
     def __init__(
-            self,
-            vocabulary_size,
-            embedding_size,
-            num_sampled=64,
-            activate_nce_loss=True,
-            nce_loss_args=None,
-            E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0),
-            nce_W_init=tl.initializers.truncated_normal(stddev=0.03),
-            nce_b_init=tl.initializers.constant(value=0.0),
-            name=None,  #'word2vec',
+        self,
+        vocabulary_size,
+        embedding_size,
+        num_sampled=64,
+        activate_nce_loss=True,
+        nce_loss_args=None,
+        E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0),
+        nce_W_init=tl.initializers.truncated_normal(stddev=0.03),
+        nce_b_init=tl.initializers.constant(value=0.0),
+        name=None,  #'word2vec',
     ):
 
         super(Word2vecEmbedding, self).__init__(name)
@@ -284,10 +284,8 @@ def forward(self, inputs, use_nce_loss=None):
             The nce_cost is returned only if the nce_loss is used.
         """
 
-        if isinstance(inputs, list):
-            outputs = tf.nn.embedding_lookup(params=self.embeddings, ids=inputs[0])
-        else:
-            outputs = tf.nn.embedding_lookup(params=self.embeddings, ids=inputs)
+        ids = inputs[0] if isinstance(inputs, list) else inputs
+        outputs = tf.nn.embedding_lookup(params=self.embeddings, ids=ids)
 
         if use_nce_loss is True and not self.activate_nce_loss:
             raise AttributeError(
@@ -352,11 +350,11 @@ class Embedding(Layer):
     """
 
     def __init__(
-            self,
-            vocabulary_size,
-            embedding_size,
-            E_init=tl.initializers.random_uniform(-0.1, 0.1),
-            name=None,  #'embedding',
+        self,
+        vocabulary_size,
+        embedding_size,
+        E_init=tl.initializers.random_uniform(-0.1, 0.1),
+        name=None,  #'embedding',
     ):
         super(Embedding, self).__init__(name)
         self.vocabulary_size = vocabulary_size
@@ -446,12 +444,12 @@ class AverageEmbedding(Layer):
     """
 
     def __init__(
-            self,
-            vocabulary_size,
-            embedding_size,
-            pad_value=0,
-            E_init=tl.initializers.random_uniform(-0.1, 0.1),
-            name=None,  # 'average_embedding',
+        self,
+        vocabulary_size,
+        embedding_size,
+        pad_value=0,
+        E_init=tl.initializers.random_uniform(-0.1, 0.1),
+        name=None,  # 'average_embedding',
     ):
 
         super(AverageEmbedding, self).__init__(name)
diff --git a/tensorlayer/layers/extend.py b/tensorlayer/layers/extend.py
index 09d5508db..c34815e97 100644
--- a/tensorlayer/layers/extend.py
+++ b/tensorlayer/layers/extend.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
@@ -32,9 +33,9 @@ class ExpandDims(Layer):
     """
 
     def __init__(
-            self,
-            axis,
-            name=None  # 'expand_dims',
+        self,
+        axis,
+        name=None  # 'expand_dims',
     ):
         super(ExpandDims, self).__init__(name)
         self.axis = axis
diff --git a/tensorlayer/layers/image_resampling.py b/tensorlayer/layers/image_resampling.py
index 4713200d3..b327901a7 100644
--- a/tensorlayer/layers/image_resampling.py
+++ b/tensorlayer/layers/image_resampling.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
@@ -45,12 +46,12 @@ class UpSampling2d(Layer):
     """
 
     def __init__(
-            self,
-            scale,
-            method='bilinear',
-            antialias=False,
-            data_format='channel_last',
-            name=None,
+        self,
+        scale,
+        method='bilinear',
+        antialias=False,
+        data_format='channel_last',
+        name=None,
     ):
         super(UpSampling2d, self).__init__(name)
         self.method = method
@@ -88,7 +89,7 @@ def forward(self, inputs):
         inputs : :class:`Tensor`
             Inputs tensors with 4-D Tensor of the shape (batch, height, width, channels)
         """
-        output_size = [inputs.shape[1] * self.scale[0], inputs.shape[2] * self.scale[1]]
+        output_size = [int(inputs.shape[1] * self.scale[0]), int(inputs.shape[2] * self.scale[1])]
         outputs = tf.image.resize(inputs, size=output_size, method=self.method, antialias=self.antialias)
         return outputs
 
@@ -126,12 +127,12 @@ class DownSampling2d(Layer):
     """
 
     def __init__(
-            self,
-            scale,
-            method='bilinear',
-            antialias=False,
-            data_format='channel_last',
-            name=None,
+        self,
+        scale,
+        method='bilinear',
+        antialias=False,
+        data_format='channel_last',
+        name=None,
     ):
         super(DownSampling2d, self).__init__(name)
         self.method = method
diff --git a/tensorlayer/layers/inputs.py b/tensorlayer/layers/inputs.py
index 5e50af9be..9d537a33d 100644
--- a/tensorlayer/layers/inputs.py
+++ b/tensorlayer/layers/inputs.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.layers.core import Layer, LayerNode
diff --git a/tensorlayer/layers/lambda_layers.py b/tensorlayer/layers/lambda_layers.py
index 2d6509e0e..c650f233c 100644
--- a/tensorlayer/layers/lambda_layers.py
+++ b/tensorlayer/layers/lambda_layers.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.files import utils
@@ -35,14 +36,14 @@ class Lambda(Layer):
 
     Examples
     ---------
-    Non-parametric and non-args case
+    Non-parametric and non-args case:
     This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
 
     >>> x = tl.layers.Input([8, 3], name='input')
     >>> y = tl.layers.Lambda(lambda x: 2*x, name='lambda')(x)
 
 
-    Non-parametric and with args case
+    Non-parametric and with args case:
     This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
 
     >>> def customize_func(x, foo=42): # x is the inputs, foo is an argument
@@ -51,19 +52,19 @@ class Lambda(Layer):
     >>> lambdalayer = tl.layers.Lambda(customize_func, fn_args={'foo': 2}, name='lambda')(x)
 
 
-    Any function with outside variables
+    Any function with outside variables:
     This case has not been supported in Model.save() / Model.load() yet.
     Please avoid using Model.save() / Model.load() to save / load models that contain such Lambda layer. Instead, you may use Model.save_weights() / Model.load_weights() to save / load model weights.
     Note: In this case, fn_weights should be a list, and then the trainable weights in this Lambda layer can be added into the weights of the whole model.
 
-    >>> vara = [tf.Variable(1.0)]
+    >>> a = tf.Variable(1.0)
     >>> def func(x):
-    >>>     return x + vara
+    >>>     return x + a
     >>> x = tl.layers.Input([8, 3], name='input')
-    >>> y = tl.layers.Lambda(func, fn_weights=a, name='lambda')(x)
+    >>> y = tl.layers.Lambda(func, fn_weights=[a], name='lambda')(x)
 
 
-    Parametric case, merge other wrappers into TensorLayer
+    Parametric case, merge other wrappers into TensorLayer:
     This case is supported in the Model.save() / Model.load() to save / load the whole model architecture and weights(optional).
 
     >>> layers = [
@@ -74,38 +75,38 @@ class Lambda(Layer):
     >>> perceptron = tf.keras.Sequential(layers)
     >>> # in order to compile keras model and get trainable_variables of the keras model
     >>> _ = perceptron(np.random.random([100, 5]).astype(np.float32))
-
+    >>>
     >>> class CustomizeModel(tl.models.Model):
     >>>     def __init__(self):
     >>>         super(CustomizeModel, self).__init__()
     >>>         self.dense = tl.layers.Dense(in_channels=1, n_units=5)
     >>>         self.lambdalayer = tl.layers.Lambda(perceptron, perceptron.trainable_variables)
-
+    >>>
     >>>     def forward(self, x):
     >>>         z = self.dense(x)
     >>>         z = self.lambdalayer(z)
     >>>         return z
-
+    >>>
     >>> optimizer = tf.optimizers.Adam(learning_rate=0.1)
     >>> model = CustomizeModel()
     >>> model.train()
-
+    >>>
     >>> for epoch in range(50):
     >>>     with tf.GradientTape() as tape:
     >>>         pred_y = model(data_x)
     >>>         loss = tl.cost.mean_squared_error(pred_y, data_y)
-
+    >>>
     >>>     gradients = tape.gradient(loss, model.trainable_weights)
     >>>     optimizer.apply_gradients(zip(gradients, model.trainable_weights))
 
     """
 
     def __init__(
-            self,
-            fn,
-            fn_weights=None,
-            fn_args=None,
-            name=None,
+        self,
+        fn,
+        fn_weights=None,
+        fn_args=None,
+        name=None,
     ):
 
         super(Lambda, self).__init__(name=name)
@@ -223,11 +224,11 @@ class ElementwiseLambda(Layer):
     """
 
     def __init__(
-            self,
-            fn,
-            fn_weights=None,
-            fn_args=None,
-            name=None,  #'elementwiselambda',
+        self,
+        fn,
+        fn_weights=None,
+        fn_args=None,
+        name=None,  #'elementwiselambda',
     ):
 
         super(ElementwiseLambda, self).__init__(name=name)
diff --git a/tensorlayer/layers/merge.py b/tensorlayer/layers/merge.py
index 6c9817406..3191d9db1 100644
--- a/tensorlayer/layers/merge.py
+++ b/tensorlayer/layers/merge.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.layers.core import Layer
 
@@ -39,9 +40,9 @@ class Concat(Layer):
     """
 
     def __init__(
-            self,
-            concat_dim=-1,
-            name=None,  #'concat',
+        self,
+        concat_dim=-1,
+        name=None,  #'concat',
     ):
 
         super(Concat, self).__init__(name)
@@ -104,10 +105,10 @@ class Elementwise(Layer):
     """
 
     def __init__(
-            self,
-            combine_fn=tf.minimum,
-            act=None,
-            name=None,  #'elementwise',
+        self,
+        combine_fn=tf.minimum,
+        act=None,
+        name=None,  #'elementwise',
     ):
 
         super(Elementwise, self).__init__(name, act=act)
diff --git a/tensorlayer/layers/noise.py b/tensorlayer/layers/noise.py
index e469619e9..1a6e85463 100644
--- a/tensorlayer/layers/noise.py
+++ b/tensorlayer/layers/noise.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -43,12 +44,12 @@ class GaussianNoise(Layer):
     """
 
     def __init__(
-            self,
-            mean=0.0,
-            stddev=1.0,
-            is_always=True,
-            seed=None,
-            name=None,  # 'gaussian_noise',
+        self,
+        mean=0.0,
+        stddev=1.0,
+        is_always=True,
+        seed=None,
+        name=None,  # 'gaussian_noise',
     ):
         super().__init__(name)
         self.mean = mean
diff --git a/tensorlayer/layers/normalization.py b/tensorlayer/layers/normalization.py
index 226795981..161d6e018 100644
--- a/tensorlayer/layers/normalization.py
+++ b/tensorlayer/layers/normalization.py
@@ -2,10 +2,11 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
-import tensorlayer as tl
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.training import moving_averages
+
+import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.layers.core import Layer
 
@@ -47,12 +48,12 @@ class LocalResponseNorm(Layer):
     """
 
     def __init__(
-            self,
-            depth_radius=None,
-            bias=None,
-            alpha=None,
-            beta=None,
-            name=None,  #'lrn',
+        self,
+        depth_radius=None,
+        bias=None,
+        alpha=None,
+        beta=None,
+        name=None,  #'lrn',
     ):
         # super(LocalResponseNorm, self).__init__(prev_layer=prev_layer, name=name)
         super().__init__(name)
@@ -91,7 +92,7 @@ def _bias_scale(x, b, data_format):
     if data_format == 'NHWC':
         return x * b
     elif data_format == 'NCHW':
-        return x * _to_channel_first_bias(b)
+        return x * b
     else:
         raise ValueError('invalid data_format: %s' % data_format)
 
@@ -101,13 +102,34 @@ def _bias_add(x, b, data_format):
     if data_format == 'NHWC':
         return tf.add(x, b)
     elif data_format == 'NCHW':
-        return tf.add(x, _to_channel_first_bias(b))
+        return tf.add(x, b)
     else:
         raise ValueError('invalid data_format: %s' % data_format)
 
 
+def _compute_shape(tensors):
+    if isinstance(tensors, list):
+        shape_mem = [t.get_shape().as_list() for t in tensors]
+    else:
+        shape_mem = tensors.get_shape().as_list()
+    return shape_mem
+
+
 def batch_normalization(x, mean, variance, offset, scale, variance_epsilon, data_format, name=None):
     """Data Format aware version of tf.nn.batch_normalization."""
+    if data_format == 'channels_last':
+        mean = tf.reshape(mean, [1] * (len(x.shape) - 1) + [-1])
+        variance = tf.reshape(variance, [1] * (len(x.shape) - 1) + [-1])
+        offset = tf.reshape(offset, [1] * (len(x.shape) - 1) + [-1])
+        scale = tf.reshape(scale, [1] * (len(x.shape) - 1) + [-1])
+    elif data_format == 'channels_first':
+        mean = tf.reshape(mean, [1] + [-1] + [1] * (len(x.shape) - 2))
+        variance = tf.reshape(variance, [1] + [-1] + [1] * (len(x.shape) - 2))
+        offset = tf.reshape(offset, [1] + [-1] + [1] * (len(x.shape) - 2))
+        scale = tf.reshape(scale, [1] + [-1] + [1] * (len(x.shape) - 2))
+    else:
+        raise ValueError('invalid data_format: %s' % data_format)
+
     with ops.name_scope(name, 'batchnorm', [x, mean, variance, scale, offset]):
         inv = math_ops.rsqrt(variance + variance_epsilon)
         if scale is not None:
@@ -181,18 +203,18 @@ class BatchNorm(Layer):
     """
 
     def __init__(
-            self,
-            decay=0.9,
-            epsilon=0.00001,
-            act=None,
-            is_train=False,
-            beta_init=tl.initializers.zeros(),
-            gamma_init=tl.initializers.random_normal(mean=1.0, stddev=0.002),
-            moving_mean_init=tl.initializers.zeros(),
-            moving_var_init=tl.initializers.zeros(),
-            num_features=None,
-            data_format='channels_last',
-            name=None,
+        self,
+        decay=0.9,
+        epsilon=0.00001,
+        act=None,
+        is_train=False,
+        beta_init=tl.initializers.zeros(),
+        gamma_init=tl.initializers.random_normal(mean=1.0, stddev=0.002),
+        moving_mean_init=tl.initializers.zeros(),
+        moving_var_init=tl.initializers.zeros(),
+        num_features=None,
+        data_format='channels_last',
+        name=None,
     ):
         super(BatchNorm, self).__init__(name=name, act=act)
         self.decay = decay
@@ -204,13 +226,9 @@ def __init__(
         self.moving_var_init = moving_var_init
         self.num_features = num_features
 
+        self.axes = None
+
         if num_features is not None:
-            if not isinstance(self, BatchNorm1d) and not isinstance(self, BatchNorm2d) and not isinstance(self,
-                                                                                                          BatchNorm3d):
-                raise ValueError(
-                    "Please use BatchNorm1d or BatchNorm2d or BatchNorm3d instead of BatchNorm "
-                    "if you want to specify 'num_features'."
-                )
             self.build(None)
             self._built = True
 
@@ -233,21 +251,24 @@ def __repr__(self):
 
     def _get_param_shape(self, inputs_shape):
         if self.data_format == 'channels_last':
-            axis = len(inputs_shape) - 1
+            axis = -1
         elif self.data_format == 'channels_first':
             axis = 1
         else:
             raise ValueError('data_format should be either %s or %s' % ('channels_last', 'channels_first'))
 
         channels = inputs_shape[axis]
-        params_shape = [1] * len(inputs_shape)
-        params_shape[axis] = channels
+        params_shape = [channels]
 
-        axes = [i for i in range(len(inputs_shape)) if i != axis]
-        return params_shape, axes
+        return params_shape
+
+    def _check_input_shape(self, inputs):
+        inputs_shape = _compute_shape(inputs)
+        if len(inputs_shape) <= 1:
+            raise ValueError('expected input at least 2D, but got {}D input'.format(inputs.ndim))
 
     def build(self, inputs_shape):
-        params_shape, self.axes = self._get_param_shape(inputs_shape)
+        params_shape = [self.num_features] if self.num_features is not None else self._get_param_shape(inputs_shape)
 
         self.beta, self.gamma = None, None
         if self.beta_init:
@@ -264,7 +285,13 @@ def build(self, inputs_shape):
         )
 
     def forward(self, inputs):
-        mean, var = tf.nn.moments(inputs, self.axes, keepdims=True)
+        self._check_input_shape(inputs)
+
+        self.channel_axis = len(inputs.shape) - 1 if self.data_format == 'channels_last' else 1
+        if self.axes is None:
+            self.axes = [i for i in range(len(inputs.shape)) if i != self.channel_axis]
+
+        mean, var = tf.nn.moments(inputs, self.axes, keepdims=False)
         if self.is_train:
             # update moving_mean and moving_var
             self.moving_mean = moving_averages.assign_moving_average(
@@ -282,8 +309,8 @@ def forward(self, inputs):
 
 
 class BatchNorm1d(BatchNorm):
-    """The :class:`BatchNorm1d` applies Batch Normalization over 3D input (a mini-batch of 1D
-    inputs with additional channel dimension), of shape (N, L, C) or (N, C, L).
+    """The :class:`BatchNorm1d` applies Batch Normalization over 2D/3D input (a mini-batch of 1D
+    inputs (optional) with additional channel dimension), of shape (N, C) or (N, L, C) or (N, C, L).
     See more details in :class:`BatchNorm`.
 
     Examples
@@ -299,23 +326,10 @@ class BatchNorm1d(BatchNorm):
 
     """
 
-    def _get_param_shape(self, inputs_shape):
-        if self.data_format == 'channels_last':
-            axis = 2
-        elif self.data_format == 'channels_first':
-            axis = 1
-        else:
-            raise ValueError('data_format should be either %s or %s' % ('channels_last', 'channels_first'))
-
-        if self.num_features is None:
-            channels = inputs_shape[axis]
-        else:
-            channels = self.num_features
-        params_shape = [1] * 3
-        params_shape[axis] = channels
-
-        axes = [i for i in range(3) if i != axis]
-        return params_shape, axes
+    def _check_input_shape(self, inputs):
+        inputs_shape = _compute_shape(inputs)
+        if len(inputs_shape) != 2 and len(inputs_shape) != 3:
+            raise ValueError('expected input to be 2D or 3D, but got {}D input'.format(inputs.ndim))
 
 
 class BatchNorm2d(BatchNorm):
@@ -336,23 +350,10 @@ class BatchNorm2d(BatchNorm):
 
     """
 
-    def _get_param_shape(self, inputs_shape):
-        if self.data_format == 'channels_last':
-            axis = 3
-        elif self.data_format == 'channels_first':
-            axis = 1
-        else:
-            raise ValueError('data_format should be either %s or %s' % ('channels_last', 'channels_first'))
-
-        if self.num_features is None:
-            channels = inputs_shape[axis]
-        else:
-            channels = self.num_features
-        params_shape = [1] * 4
-        params_shape[axis] = channels
-
-        axes = [i for i in range(4) if i != axis]
-        return params_shape, axes
+    def _check_input_shape(self, inputs):
+        inputs_shape = _compute_shape(inputs)
+        if len(inputs_shape) != 4:
+            raise ValueError('expected input to be 4D, but got {}D input'.format(inputs.ndim))
 
 
 class BatchNorm3d(BatchNorm):
@@ -373,23 +374,10 @@ class BatchNorm3d(BatchNorm):
 
     """
 
-    def _get_param_shape(self, inputs_shape):
-        if self.data_format == 'channels_last':
-            axis = 4
-        elif self.data_format == 'channels_first':
-            axis = 1
-        else:
-            raise ValueError('data_format should be either %s or %s' % ('channels_last', 'channels_first'))
-
-        if self.num_features is None:
-            channels = inputs_shape[axis]
-        else:
-            channels = self.num_features
-        params_shape = [1] * 5
-        params_shape[axis] = channels
-
-        axes = [i for i in range(5) if i != axis]
-        return params_shape, axes
+    def _check_input_shape(self, inputs):
+        inputs_shape = _compute_shape(inputs)
+        if len(inputs_shape) != 5:
+            raise ValueError('expected input to be 5D, but got {}D input'.format(inputs.ndim))
 
 
 class InstanceNorm(Layer):
@@ -435,9 +423,9 @@ class InstanceNorm(Layer):
     """
 
     def __init__(
-            self, act=None, epsilon=0.00001, beta_init=tl.initializers.zeros(),
-            gamma_init=tl.initializers.random_normal(mean=1.0, stddev=0.002), num_features=None,
-            data_format='channels_last', name=None
+        self, act=None, epsilon=0.00001, beta_init=tl.initializers.zeros(),
+        gamma_init=tl.initializers.random_normal(mean=1.0, stddev=0.002), num_features=None,
+        data_format='channels_last', name=None
     ):
         super(InstanceNorm, self).__init__(name=name, act=act)
         self.epsilon = epsilon
@@ -630,21 +618,21 @@ class LayerNorm(Layer):
     """
 
     def __init__(
-            self,  #prev_layer,
-            center=True,
-            scale=True,
-            act=None,
-            # reuse=None,
-            # variables_collections=None,
-            # outputs_collections=None,
-            # trainable=True,
-            epsilon=1e-12,
-            begin_norm_axis=1,
-            begin_params_axis=-1,
-            beta_init=tl.initializers.zeros(),
-            gamma_init=tl.initializers.ones(),
-            data_format='channels_last',
-            name=None,
+        self,  #prev_layer,
+        center=True,
+        scale=True,
+        act=None,
+        # reuse=None,
+        # variables_collections=None,
+        # outputs_collections=None,
+        # trainable=True,
+        epsilon=1e-12,
+        begin_norm_axis=1,
+        begin_params_axis=-1,
+        beta_init=tl.initializers.zeros(),
+        gamma_init=tl.initializers.ones(),
+        data_format='channels_last',
+        name=None,
     ):
 
         # super(LayerNorm, self).__init__(prev_layer=prev_layer, act=act, name=name)
@@ -829,17 +817,17 @@ class SwitchNorm(Layer):
     """
 
     def __init__(
-            self,
-            act=None,
-            epsilon=1e-5,
-            beta_init=tl.initializers.constant(0.0),
-            gamma_init=tl.initializers.constant(1.0),
-            moving_mean_init=tl.initializers.zeros(),
-            # beta_init=tf.compat.v1.initializers.constant(0.0),
-            # gamma_init=tf.compat.v1.initializers.constant(1.0),
-            # moving_mean_init=tf.compat.v1.initializers.zeros(),
-            data_format='channels_last',
-            name=None,  #'switchnorm',
+        self,
+        act=None,
+        epsilon=1e-5,
+        beta_init=tl.initializers.constant(0.0),
+        gamma_init=tl.initializers.constant(1.0),
+        moving_mean_init=tl.initializers.zeros(),
+        # beta_init=tf.compat.v1.initializers.constant(0.0),
+        # gamma_init=tf.compat.v1.initializers.constant(1.0),
+        # moving_mean_init=tf.compat.v1.initializers.zeros(),
+        data_format='channels_last',
+        name=None,  #'switchnorm',
     ):
         # super(SwitchNorm, self).__init__(prev_layer=prev_layer, act=act, name=name)
         super().__init__(name, act=act)
diff --git a/tensorlayer/layers/padding.py b/tensorlayer/layers/padding.py
index edcb720a5..ae89035bc 100644
--- a/tensorlayer/layers/padding.py
+++ b/tensorlayer/layers/padding.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -40,10 +41,10 @@ class PadLayer(Layer):
     """
 
     def __init__(
-            self,
-            padding=None,
-            mode='CONSTANT',
-            name=None,  # 'pad_layer',
+        self,
+        padding=None,
+        mode='CONSTANT',
+        name=None,  # 'pad_layer',
     ):
         super().__init__(name)
         self.padding = padding
@@ -98,9 +99,9 @@ class ZeroPad1d(Layer):
     """
 
     def __init__(
-            self,
-            padding,
-            name=None,  # 'zeropad1d',
+        self,
+        padding,
+        name=None,  # 'zeropad1d',
     ):
         super().__init__(name)
         self.padding = padding
@@ -152,9 +153,9 @@ class ZeroPad2d(Layer):
     """
 
     def __init__(
-            self,
-            padding,
-            name=None,  # 'zeropad2d',
+        self,
+        padding,
+        name=None,  # 'zeropad2d',
     ):
         super().__init__(name)
 
@@ -207,9 +208,9 @@ class ZeroPad3d(Layer):
     """
 
     def __init__(
-            self,
-            padding,
-            name=None,  # 'zeropad3d',
+        self,
+        padding,
+        name=None,  # 'zeropad3d',
     ):
         super().__init__(name)
         self.padding = padding
diff --git a/tensorlayer/layers/pooling.py b/tensorlayer/layers/pooling.py
index a22cea358..d9deedecd 100644
--- a/tensorlayer/layers/pooling.py
+++ b/tensorlayer/layers/pooling.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
@@ -58,12 +59,12 @@ class PoolLayer(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=(1, 2, 2, 1),
-            strides=(1, 2, 2, 1),
-            padding='SAME',
-            pool=tf.nn.max_pool,
-            name=None  # 'pool_pro',
+        self,
+        filter_size=(1, 2, 2, 1),
+        strides=(1, 2, 2, 1),
+        padding='SAME',
+        pool=tf.nn.max_pool,
+        name=None  # 'pool_pro',
     ):
         super().__init__(name)
         self.filter_size = filter_size
@@ -121,13 +122,13 @@ class MaxPool1d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=3,
-            strides=2,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=1,
-            name=None  # 'maxpool1d'
+        self,
+        filter_size=3,
+        strides=2,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=1,
+        name=None  # 'maxpool1d'
     ):
         super().__init__(name)
         self.filter_size = self._filter_size = filter_size
@@ -206,13 +207,13 @@ class MeanPool1d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=3,
-            strides=2,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=1,
-            name=None  # 'meanpool1d'
+        self,
+        filter_size=3,
+        strides=2,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=1,
+        name=None  # 'meanpool1d'
     ):
         super().__init__(name)
         self.filter_size = self._filter_size = filter_size
@@ -292,12 +293,12 @@ class MaxPool2d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=(3, 3),
-            strides=(2, 2),
-            padding='SAME',
-            data_format='channels_last',
-            name=None  # 'maxpool2d'
+        self,
+        filter_size=(3, 3),
+        strides=(2, 2),
+        padding='SAME',
+        data_format='channels_last',
+        name=None  # 'maxpool2d'
     ):
         super().__init__(name)
         self.filter_size = filter_size
@@ -323,17 +324,19 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], 1]
         if self.data_format == 'channels_last':
+            self._strides = [1, self.strides[0], self.strides[1], 1]
             self.data_format = 'NHWC'
         elif self.data_format == 'channels_first':
             self.data_format = 'NCHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1]]
         else:
             raise Exception("unsupported data format")
 
     def forward(self, inputs):
         outputs = tf.nn.max_pool(
-            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name
+            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name,
+            data_format=self.data_format
         )
         return outputs
 
@@ -365,12 +368,12 @@ class MeanPool2d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=(3, 3),
-            strides=(2, 2),
-            padding='SAME',
-            data_format='channels_last',
-            name=None  # 'meanpool2d'
+        self,
+        filter_size=(3, 3),
+        strides=(2, 2),
+        padding='SAME',
+        data_format='channels_last',
+        name=None  # 'meanpool2d'
     ):
         super().__init__(name)
         self.filter_size = filter_size
@@ -396,17 +399,19 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NHWC'
+            self._strides = [1, self.strides[0], self.strides[1], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1]]
         else:
             raise Exception("unsupported data format")
 
     def forward(self, inputs):
         outputs = tf.nn.avg_pool(
-            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name
+            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name,
+            data_format=self.data_format
         )
         return outputs
 
@@ -443,12 +448,12 @@ class MaxPool3d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=(3, 3, 3),
-            strides=(2, 2, 2),
-            padding='VALID',
-            data_format='channels_last',
-            name=None  # 'maxpool3d'
+        self,
+        filter_size=(3, 3, 3),
+        strides=(2, 2, 2),
+        padding='VALID',
+        data_format='channels_last',
+        name=None  # 'maxpool3d'
     ):
         super().__init__(name)
         self.filter_size = filter_size
@@ -472,11 +477,12 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NDHWC'
+            self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCDHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1], self.strides[2]]
         else:
             raise Exception("unsupported data format")
 
@@ -524,12 +530,12 @@ class MeanPool3d(Layer):
     """
 
     def __init__(
-            self,
-            filter_size=(3, 3, 3),
-            strides=(2, 2, 2),
-            padding='VALID',
-            data_format='channels_last',
-            name=None  # 'meanpool3d'
+        self,
+        filter_size=(3, 3, 3),
+        strides=(2, 2, 2),
+        padding='VALID',
+        data_format='channels_last',
+        name=None  # 'meanpool3d'
     ):
         super().__init__(name)
         self.filter_size = filter_size
@@ -553,11 +559,12 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NDHWC'
+            self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCDHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1], self.strides[2]]
         else:
             raise Exception("unsupported data format")
 
@@ -594,9 +601,9 @@ class GlobalMaxPool1d(Layer):
     """
 
     def __init__(
-            self,
-            data_format="channels_last",
-            name=None  # 'globalmaxpool1d'
+        self,
+        data_format="channels_last",
+        name=None  # 'globalmaxpool1d'
     ):
         super().__init__(name)
 
@@ -650,9 +657,9 @@ class GlobalMeanPool1d(Layer):
     """
 
     def __init__(
-            self,
-            data_format='channels_last',
-            name=None  # 'globalmeanpool1d'
+        self,
+        data_format='channels_last',
+        name=None  # 'globalmeanpool1d'
     ):
         super().__init__(name)
         self.data_format = data_format
@@ -705,9 +712,9 @@ class GlobalMaxPool2d(Layer):
     """
 
     def __init__(
-            self,
-            data_format='channels_last',
-            name=None  # 'globalmaxpool2d'
+        self,
+        data_format='channels_last',
+        name=None  # 'globalmaxpool2d'
     ):
         super().__init__(name)
         self.data_format = data_format
@@ -760,9 +767,9 @@ class GlobalMeanPool2d(Layer):
     """
 
     def __init__(
-            self,
-            data_format='channels_last',
-            name=None  # 'globalmeanpool2d'
+        self,
+        data_format='channels_last',
+        name=None  # 'globalmeanpool2d'
     ):
         super().__init__(name)
 
@@ -816,9 +823,9 @@ class GlobalMaxPool3d(Layer):
     """
 
     def __init__(
-            self,
-            data_format='channels_last',
-            name=None  # 'globalmaxpool3d'
+        self,
+        data_format='channels_last',
+        name=None  # 'globalmaxpool3d'
     ):
         super().__init__(name)
 
@@ -872,9 +879,9 @@ class GlobalMeanPool3d(Layer):
     """
 
     def __init__(
-            self,
-            data_format='channels_last',
-            name=None  # 'globalmeanpool3d'
+        self,
+        data_format='channels_last',
+        name=None  # 'globalmeanpool3d'
     ):
         super().__init__(name)
         self.data_format = data_format
@@ -928,9 +935,9 @@ class CornerPool2d(Layer):
     """
 
     def __init__(
-            self,
-            mode='TopLeft',
-            name=None  # 'cornerpool2d'
+        self,
+        mode='TopLeft',
+        name=None  # 'cornerpool2d'
     ):
         super().__init__(name)
         self.mode = mode
diff --git a/tensorlayer/layers/quantize.py b/tensorlayer/layers/quantize.py
index 47ad2a088..fd19c9fa4 100644
--- a/tensorlayer/layers/quantize.py
+++ b/tensorlayer/layers/quantize.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
@@ -24,8 +25,8 @@ class Sign(Layer):
 
     # @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            name=None  # 'sign',
+        self,
+        name=None  # 'sign',
     ):
         super().__init__(name)
         logging.info("Sign  %s" % self.name)
diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 66ffe2211..565d27e4c 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -3,11 +3,11 @@
 
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
-import warnings
 
 # TODO: uncomment
 __all__ = [
@@ -105,7 +105,10 @@ class RNN(Layer):
     Similar to the DynamicRNN in TL 1.x.
 
     If the `sequence_length` is provided in RNN's forwarding and both `return_last_output` and `return_last_state`
-    are set as `True`, the forward function will automatically ignore the paddings.
+    are set as `True`, the forward function will automatically ignore the paddings. Note that if `return_last_output`
+    is set as `False`, the synced sequence outputs will still include outputs which correspond with paddings,
+    but users are free to select which slice of outputs to be used in following procedure.
+
     The `sequence_length` should be a list of integers which indicates the length of each sequence.
     It is recommended to
     `tl.layers.retrieve_seq_length_op3 <https://tensorlayer.readthedocs.io/en/latest/modules/layers.html#compute-sequence-length-3>`__
@@ -135,13 +138,13 @@ class RNN(Layer):
     """
 
     def __init__(
-            self,
-            cell,
-            return_last_output=False,
-            return_seq_2d=False,
-            return_last_state=True,
-            in_channels=None,
-            name=None,  # 'rnn'
+        self,
+        cell,
+        return_last_output=False,
+        return_seq_2d=False,
+        return_last_state=True,
+        in_channels=None,
+        name=None,  # 'rnn'
     ):
 
         super(RNN, self).__init__(name=name)
@@ -244,16 +247,17 @@ def forward(self, inputs, sequence_length=None, initial_state=None, **kwargs):
                         "but got an actual length of a sequence %d" % i
                     )
 
-            sequence_length = [i - 1 for i in sequence_length]
+        sequence_length = tl.layers.retrieve_seq_length_op3(inputs)
+
+        sequence_length = [i - 1 if i >= 1 else 0 for i in sequence_length]
 
         # set warning
-        if (not self.return_last_state or not self.return_last_output) and sequence_length is not None:
-            warnings.warn(
-                'return_last_output is set as %s ' % self.return_last_output +
-                'and return_last_state is set as %s. ' % self.return_last_state +
-                'When sequence_length is provided, both are recommended to set as True. ' +
-                'Otherwise, padding will be considered while RNN is forwarding.'
-            )
+        # if (not self.return_last_output) and sequence_length is not None:
+        #     warnings.warn(
+        #         'return_last_output is set as %s ' % self.return_last_output +
+        #         'When sequence_length is provided, it is recommended to set as True. ' +
+        #         'Otherwise, padding will be considered while RNN is forwarding.'
+        #     )
 
         # return the last output, iterating each seq including padding ones. No need to store output during each
         # time step.
@@ -274,6 +278,7 @@ def forward(self, inputs, sequence_length=None, initial_state=None, **kwargs):
         self.cell.reset_recurrent_dropout_mask()
 
         # recurrent computation
+        # FIXME: if sequence_length is provided (dynamic rnn), only iterate max(sequence_length) times.
         for time_step in range(total_steps):
 
             cell_output, states = self.cell.call(inputs[:, time_step, :], states, training=self.is_train)
@@ -385,14 +390,14 @@ class SimpleRNN(RNN):
     """
 
     def __init__(
-            self,
-            units,
-            return_last_output=False,
-            return_seq_2d=False,
-            return_last_state=True,
-            in_channels=None,
-            name=None,  # 'simplernn'
-            **kwargs
+        self,
+        units,
+        return_last_output=False,
+        return_seq_2d=False,
+        return_last_state=True,
+        in_channels=None,
+        name=None,  # 'simplernn'
+        **kwargs
     ):
         super(SimpleRNN, self).__init__(
             cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs), return_last_output=return_last_output,
@@ -463,14 +468,14 @@ class GRURNN(RNN):
     """
 
     def __init__(
-            self,
-            units,
-            return_last_output=False,
-            return_seq_2d=False,
-            return_last_state=True,
-            in_channels=None,
-            name=None,  # 'grurnn'
-            **kwargs
+        self,
+        units,
+        return_last_output=False,
+        return_seq_2d=False,
+        return_last_state=True,
+        in_channels=None,
+        name=None,  # 'grurnn'
+        **kwargs
     ):
         super(GRURNN, self).__init__(
             cell=tf.keras.layers.GRUCell(units=units, **kwargs), return_last_output=return_last_output,
@@ -541,14 +546,14 @@ class LSTMRNN(RNN):
     """
 
     def __init__(
-            self,
-            units,
-            return_last_output=False,
-            return_seq_2d=False,
-            return_last_state=True,
-            in_channels=None,
-            name=None,  # 'lstmrnn'
-            **kwargs
+        self,
+        units,
+        return_last_output=False,
+        return_seq_2d=False,
+        return_last_state=True,
+        in_channels=None,
+        name=None,  # 'lstmrnn'
+        **kwargs
     ):
         super(LSTMRNN, self).__init__(
             cell=tf.keras.layers.LSTMCell(units=units, **kwargs), return_last_output=return_last_output,
@@ -629,13 +634,13 @@ class BiRNN(Layer):
     """
 
     def __init__(
-            self,
-            fw_cell,
-            bw_cell,
-            return_seq_2d=False,
-            return_last_state=False,
-            in_channels=None,
-            name=None,  # 'birnn'
+        self,
+        fw_cell,
+        bw_cell,
+        return_seq_2d=False,
+        return_last_state=False,
+        in_channels=None,
+        name=None,  # 'birnn'
     ):
         super(BiRNN, self).__init__(name)
 
@@ -758,6 +763,7 @@ def forward(self, inputs, fw_initial_state=None, bw_initial_state=None, **kwargs
             return outputs
 
 
+'''
 class ConvRNNCell(object):
     """Abstract object representing an Convolutional RNN Cell."""
 
@@ -1071,6 +1077,8 @@ def __init__(
         self._add_layers(self.outputs)
         self._add_params(rnn_variables)
 
+'''
+
 
 # @tf.function
 def retrieve_seq_length_op(data):
diff --git a/tensorlayer/layers/scale.py b/tensorlayer/layers/scale.py
index 59a841c96..3e14e462a 100644
--- a/tensorlayer/layers/scale.py
+++ b/tensorlayer/layers/scale.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.initializers import constant
 from tensorlayer.layers.core import Layer
@@ -32,9 +33,9 @@ class Scale(Layer):
     """
 
     def __init__(
-            self,
-            init_scale=0.05,
-            name='scale',
+        self,
+        init_scale=0.05,
+        name='scale',
     ):
         super(Scale, self).__init__(name)
         self.init_scale = init_scale
diff --git a/tensorlayer/layers/shape.py b/tensorlayer/layers/shape.py
index e308eb0c4..f8e7b47db 100644
--- a/tensorlayer/layers/shape.py
+++ b/tensorlayer/layers/shape.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
diff --git a/tensorlayer/layers/spatial_transformer.py b/tensorlayer/layers/spatial_transformer.py
index d96763f6f..74822d565 100644
--- a/tensorlayer/layers/spatial_transformer.py
+++ b/tensorlayer/layers/spatial_transformer.py
@@ -2,11 +2,11 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
+import tensorflow as tf
 from six.moves import xrange
+from tensorflow.python.ops import array_ops
 
-import tensorflow as tf
 import tensorlayer as tl
-from tensorflow.python.ops import array_ops
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
@@ -230,11 +230,11 @@ class SpatialTransformer2dAffine(Layer):
     """
 
     def __init__(
-            self,
-            out_size=(40, 40),
-            in_channels=None,
-            data_format='channel_last',
-            name=None,
+        self,
+        out_size=(40, 40),
+        in_channels=None,
+        data_format='channel_last',
+        name=None,
     ):
         super(SpatialTransformer2dAffine, self).__init__(name)
 
diff --git a/tensorlayer/layers/stack.py b/tensorlayer/layers/stack.py
index c35e3837f..4e37d1f9a 100644
--- a/tensorlayer/layers/stack.py
+++ b/tensorlayer/layers/stack.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
@@ -37,9 +38,9 @@ class Stack(Layer):
     """
 
     def __init__(
-            self,
-            axis=1,
-            name=None,  #'stack',
+        self,
+        axis=1,
+        name=None,  #'stack',
     ):
         super().__init__(name)
         self.axis = axis
diff --git a/tensorlayer/layers/utils.py b/tensorlayer/layers/utils.py
index 6d411589f..e5dd154b1 100644
--- a/tensorlayer/layers/utils.py
+++ b/tensorlayer/layers/utils.py
@@ -2,10 +2,10 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-
 import tensorflow as tf
-import tensorlayer as tl
 from tensorflow.python.ops.rnn_cell import LSTMStateTuple
+
+import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.decorators import deprecated, deprecated_alias
 
diff --git a/tensorlayer/logging/contrib/hyperdash.py b/tensorlayer/logging/contrib/hyperdash.py
index 6e19c8e9b..7c21e65ea 100644
--- a/tensorlayer/logging/contrib/hyperdash.py
+++ b/tensorlayer/logging/contrib/hyperdash.py
@@ -46,10 +46,10 @@ def monitor(cls, model_name, api_key=None, capture_io=True):
 class Experiment(hd.Experiment):
 
     def __init__(
-            self,
-            model_name,
-            api_key=None,
-            capture_io=True,
+        self,
+        model_name,
+        api_key=None,
+        capture_io=True,
     ):
 
         if api_key is not None:
diff --git a/tensorlayer/models/__init__.py b/tensorlayer/models/__init__.py
index 19f5bb665..7e54c8a4b 100644
--- a/tensorlayer/models/__init__.py
+++ b/tensorlayer/models/__init__.py
@@ -4,9 +4,9 @@
 # """A collections of pre-defined well known models."""
 
 from .core import *
-from .resnet import ResNet50
 from .mobilenetv1 import MobileNetV1
-from .squeezenetv1 import SqueezeNetV1
-from .vgg import *
+from .resnet import ResNet50
 from .seq2seq import Seq2seq
 from .seq2seq_with_attention import Seq2seqLuongAttention
+from .squeezenetv1 import SqueezeNetV1
+from .vgg import *
diff --git a/tensorlayer/models/core.py b/tensorlayer/models/core.py
index 74e48ee6d..514db708f 100644
--- a/tensorlayer/models/core.py
+++ b/tensorlayer/models/core.py
@@ -3,8 +3,9 @@
 from queue import Queue
 
 import tensorflow as tf
-import tensorlayer as tl
 from tensorflow.python.framework import ops as tf_ops
+
+import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.files import utils
 from tensorlayer.layers import Layer, ModelLayer
@@ -209,7 +210,8 @@ def __init__(self, inputs=None, outputs=None, name=None):
             # check type of inputs and outputs
             check_order = ['inputs', 'outputs']
             for co, check_argu in enumerate([inputs, outputs]):
-                if isinstance(check_argu, tf_ops._TensorLike) or tf_ops.is_dense_tensor_like(check_argu):
+                if isinstance(check_argu,
+                              (tf.Tensor, tf.SparseTensor, tf.Variable)) or tf_ops.is_dense_tensor_like(check_argu):
                     pass
                 elif isinstance(check_argu, list):
                     if len(check_argu) == 0:
@@ -218,8 +220,9 @@ def __init__(self, inputs=None, outputs=None, name=None):
                             "It should be either Tensor or a list of Tensor."
                         )
                     for idx in range(len(check_argu)):
-                        if not isinstance(check_argu[idx], tf_ops._TensorLike) or not tf_ops.is_dense_tensor_like(
-                                check_argu[idx]):
+                        if not isinstance(check_argu[idx],
+                                          (tf.Tensor, tf.SparseTensor, tf.Variable)) or not tf_ops.is_dense_tensor_like(
+                                              check_argu[idx]):
                             raise TypeError(
                                 "The argument `%s` should be either Tensor or a list of Tensor " % (check_order[co]) +
                                 "but the %s[%d] is detected as %s" % (check_order[co], idx, type(check_argu[idx]))
diff --git a/tensorlayer/models/mobilenetv1.py b/tensorlayer/models/mobilenetv1.py
index 4908b3d89..fd169b025 100644
--- a/tensorlayer/models/mobilenetv1.py
+++ b/tensorlayer/models/mobilenetv1.py
@@ -5,6 +5,7 @@
 import os
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.files import (assign_weights, load_npz, maybe_download_and_extract)
 from tensorlayer.layers import (BatchNorm, Conv2d, DepthwiseConv2d, Flatten, GlobalMeanPool2d, Input, Reshape)
@@ -43,9 +44,9 @@ def restore_params(network, path='models'):
         expected_bytes=25600116
     )  # ls -al
     params = load_npz(name=os.path.join(path, 'mobilenet.npz'))
-    for idx, net_weight in enumerate(network.all_weights):
-        if 'batchnorm' in net_weight.name:
-            params[idx] = params[idx].reshape(1, 1, 1, -1)
+    # for idx, net_weight in enumerate(network.all_weights):
+    #     if 'batchnorm' in net_weight.name:
+    #         params[idx] = params[idx].reshape(1, 1, 1, -1)
     assign_weights(params[:len(network.all_weights)], network)
     del params
 
diff --git a/tensorlayer/models/resnet.py b/tensorlayer/models/resnet.py
index 9938fd1cd..458f25912 100644
--- a/tensorlayer/models/resnet.py
+++ b/tensorlayer/models/resnet.py
@@ -11,9 +11,10 @@
 import os
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.files import (assign_weights, load_npz, maybe_download_and_extract)
-from tensorlayer.layers import (BatchNorm, Conv2d, Elementwise, GlobalMeanPool2d, MaxPool2d, Input, Dense)
+from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Elementwise, GlobalMeanPool2d, Input, MaxPool2d)
 from tensorlayer.models import Model
 
 __all__ = [
@@ -150,21 +151,21 @@ def ResNet50(pretrained=False, end_with='fc1000', n_classes=1000, name=None):
     n = BatchNorm(name='bn_conv1', act='relu')(n)
     n = MaxPool2d((3, 3), strides=(2, 2), name='max_pool1')(n)
 
-    for i, name in enumerate(block_names):
-        if len(name) == 2:
-            stage = int(name[0])
-            block = name[1]
+    for i, block_name in enumerate(block_names):
+        if len(block_name) == 2:
+            stage = int(block_name[0])
+            block = block_name[1]
             if block == 'a':
                 strides = (1, 1) if stage == 2 else (2, 2)
                 n = conv_block(n, 3, block_filters[stage - 2], stage=stage, block=block, strides=strides)
             else:
                 n = identity_block(n, 3, block_filters[stage - 2], stage=stage, block=block)
-        elif name == 'avg_pool':
+        elif block_name == 'avg_pool':
             n = GlobalMeanPool2d(name='avg_pool')(n)
-        elif name == 'fc1000':
+        elif block_name == 'fc1000':
             n = Dense(n_classes, name='fc1000')(n)
 
-        if name == end_with:
+        if block_name == end_with:
             break
 
     network = Model(inputs=ni, outputs=n, name=name)
@@ -194,8 +195,8 @@ def restore_params(network, path='models'):
             continue
         w_names = list(f[layer.name])
         params = [f[layer.name][n][:] for n in w_names]
-        if 'bn' in layer.name:
-            params = [x.reshape(1, 1, 1, -1) for x in params]
+        # if 'bn' in layer.name:
+        #     params = [x.reshape(1, 1, 1, -1) for x in params]
         assign_weights(params, layer)
         del params
 
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index e0c20ef56..0473eeffc 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index d601e33c8..800bbaa61 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
diff --git a/tensorlayer/models/squeezenetv1.py b/tensorlayer/models/squeezenetv1.py
index a2d7e4304..b38d42dc8 100644
--- a/tensorlayer/models/squeezenetv1.py
+++ b/tensorlayer/models/squeezenetv1.py
@@ -5,6 +5,7 @@
 import os
 
 import tensorflow as tf
+
 from tensorlayer import logging
 from tensorlayer.files import (assign_weights, load_npz, maybe_download_and_extract)
 from tensorlayer.layers import (Concat, Conv2d, Dropout, GlobalMeanPool2d, Input, Lambda, MaxPool2d)
diff --git a/tensorlayer/models/vgg.py b/tensorlayer/models/vgg.py
index 93524552f..c57572e24 100644
--- a/tensorlayer/models/vgg.py
+++ b/tensorlayer/models/vgg.py
@@ -30,8 +30,8 @@
 import os
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer import logging
 from tensorlayer.files import assign_weights, maybe_download_and_extract
@@ -105,7 +105,7 @@ def forward(self, inputs):
 
         inputs = inputs * 255 - np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape([1, 1, 1, 3])
 
-        out = self.layers(inputs)
+        out = self.layers.forward(inputs)
         return out
 
 
@@ -123,7 +123,7 @@ def make_layers(config, batch_norm=False, end_with='outputs'):
                     else:
                         in_channels = 3
                 else:
-                    in_channels = layer
+                    in_channels = layer_group[idx - 1]
                 layer_list.append(
                     Conv2d(
                         n_filter=n_filter, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME',
diff --git a/tensorlayer/nlp.py b/tensorlayer/nlp.py
index 699eeb5fd..1f22584bc 100755
--- a/tensorlayer/nlp.py
+++ b/tensorlayer/nlp.py
@@ -10,13 +10,13 @@
 import warnings
 from collections import Counter
 
-import six as _six
 import numpy as np
+import six as _six
+import tensorflow as tf
 from six.moves import urllib, xrange
+from tensorflow.python.platform import gfile
 
-import tensorflow as tf
 import tensorlayer as tl
-from tensorflow.python.platform import gfile
 from tensorlayer.lazy_imports import LazyImport
 
 nltk = LazyImport("nltk")
@@ -906,8 +906,8 @@ def basic_tokenizer(sentence, _WORD_SPLIT=re.compile(b"([.,!?\"':;)(])")):
 
 
 def create_vocabulary(
-        vocabulary_path, data_path, max_vocabulary_size, tokenizer=None, normalize_digits=True,
-        _DIGIT_RE=re.compile(br"\d"), _START_VOCAB=None
+    vocabulary_path, data_path, max_vocabulary_size, tokenizer=None, normalize_digits=True,
+    _DIGIT_RE=re.compile(br"\d"), _START_VOCAB=None
 ):
     r"""Create vocabulary file (if it does not exist yet) from data file.
 
@@ -1014,7 +1014,7 @@ def initialize_vocabulary(vocabulary_path):
 
 
 def sentence_to_token_ids(
-        sentence, vocabulary, tokenizer=None, normalize_digits=True, UNK_ID=3, _DIGIT_RE=re.compile(br"\d")
+    sentence, vocabulary, tokenizer=None, normalize_digits=True, UNK_ID=3, _DIGIT_RE=re.compile(br"\d")
 ):
     """Convert a string to list of integers representing token-ids.
 
@@ -1050,8 +1050,8 @@ def sentence_to_token_ids(
 
 
 def data_to_token_ids(
-        data_path, target_path, vocabulary_path, tokenizer=None, normalize_digits=True, UNK_ID=3,
-        _DIGIT_RE=re.compile(br"\d")
+    data_path, target_path, vocabulary_path, tokenizer=None, normalize_digits=True, UNK_ID=3,
+    _DIGIT_RE=re.compile(br"\d")
 ):
     """Tokenize data file and turn into token-ids using given vocabulary file.
 
diff --git a/tensorlayer/package_info.py b/tensorlayer/package_info.py
index f72771a42..3b30aeb85 100644
--- a/tensorlayer/package_info.py
+++ b/tensorlayer/package_info.py
@@ -3,8 +3,8 @@
 """Deep learning and Reinforcement learning library for Researchers and Engineers."""
 
 MAJOR = 2
-MINOR = 1
-PATCH = 1
+MINOR = 2
+PATCH = 4
 PRE_RELEASE = ''
 # Use the following formatting: (major, minor, patch, prerelease)
 VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)
diff --git a/tensorlayer/prepro.py b/tensorlayer/prepro.py
index d2c31e600..43a396a3f 100644
--- a/tensorlayer/prepro.py
+++ b/tensorlayer/prepro.py
@@ -622,8 +622,7 @@ def affine_transform_keypoints(coords_list, transform_matrix):
 
 
 def projective_transform_by_points(
-        x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True,
-        preserve_range=False
+    x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False
 ):
     """Projective transform by given coordinates, usually 4 coordinates.
 
@@ -700,7 +699,7 @@ def projective_transform_by_points(
 
 # rotate
 def rotation(
-        x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
+    x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
 ):
     """Rotate an image randomly or non-randomly.
 
@@ -746,7 +745,7 @@ def rotation(
 
 
 def rotation_multi(
-        x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
+    x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
 ):
     """Rotate multiple images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
@@ -956,8 +955,8 @@ def flip_axis_multi(x, axis, is_random=False):
 
 # shift
 def shift(
-        x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
+    order=1
 ):
     """Shift an image randomly or non-randomly.
 
@@ -1000,8 +999,8 @@ def shift(
 
 
 def shift_multi(
-        x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
+    order=1
 ):
     """Shift images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
@@ -1036,8 +1035,7 @@ def shift_multi(
 
 # shear
 def shear(
-        x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
 ):
     """Shear an image randomly or non-randomly.
 
@@ -1082,8 +1080,7 @@ def shear(
 
 
 def shear_multi(
-        x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
 ):
     """Shear images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
@@ -1116,8 +1113,8 @@ def shear_multi(
 
 
 def shear2(
-        x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
+    order=1
 ):
     """Shear an image randomly or non-randomly.
 
@@ -1169,8 +1166,8 @@ def shear2(
 
 
 def shear_multi2(
-        x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
-        order=1
+    x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
+    order=1
 ):
     """Shear images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
@@ -1210,8 +1207,8 @@ def shear_multi2(
 
 # swirl
 def swirl(
-        x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0,
-        clip=True, preserve_range=False, is_random=False
+    x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True,
+    preserve_range=False, is_random=False
 ):
     """Swirl an image randomly or non-randomly, see `scikit-image swirl API <http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.swirl>`__
     and `example <http://scikit-image.org/docs/dev/auto_examples/plot_swirl.html>`__.
@@ -1284,8 +1281,8 @@ def swirl(
 
 
 def swirl_multi(
-        x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0,
-        clip=True, preserve_range=False, is_random=False
+    x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True,
+    preserve_range=False, is_random=False
 ):
     """Swirl multiple images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
@@ -1530,7 +1527,7 @@ def zoom_multi(x, zoom_range=(0.9, 1.1), flags=None, border_mode='constant'):
         h, w = x.shape[0], x.shape[1]
         transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
         results.append(affine_transform_cv2(x, transform_matrix, flags=flags, border_mode=border_mode))
-    return results
+    return np.asarray(results)
 
 
 # image = tf.image.random_brightness(image, max_delta=32. / 255.)
@@ -1902,7 +1899,7 @@ def pixel_value_scale(im, val=0.9, clip=None, is_random=False):
 
 # normailization
 def samplewise_norm(
-        x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-7
+    x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-7
 ):
     """Normalize an image by rescale, samplewise centering and samplewise centering in order.
 
@@ -2850,8 +2847,8 @@ def obj_box_imresize(im, coords=None, size=None, interp='bicubic', mode=None, is
 
 
 def obj_box_crop(
-        im, classes=None, coords=None, wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False,
-        thresh_wh=0.02, thresh_wh2=12.
+    im, classes=None, coords=None, wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02,
+    thresh_wh2=12.
 ):
     """Randomly or centrally crop an image, and compute the new bounding box coordinates.
     Objects outside the cropped image will be removed.
@@ -3003,8 +3000,8 @@ def _get_coord(coord):
 
 
 def obj_box_shift(
-        im, classes=None, coords=None, wrg=0.1, hrg=0.1, row_index=0, col_index=1, channel_index=2, fill_mode='nearest',
-        cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.
+    im, classes=None, coords=None, wrg=0.1, hrg=0.1, row_index=0, col_index=1, channel_index=2, fill_mode='nearest',
+    cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.
 ):
     """Shift an image randomly or non-randomly, and compute the new bounding box coordinates.
     Objects outside the cropped image will be removed.
@@ -3138,9 +3135,9 @@ def _get_coord(coord):
 
 
 def obj_box_zoom(
-        im, classes=None, coords=None, zoom_range=(0.9, 1.1), row_index=0, col_index=1, channel_index=2,
-        fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02,
-        thresh_wh2=12.
+    im, classes=None, coords=None, zoom_range=(0.9, 1.1), row_index=0, col_index=1, channel_index=2,
+    fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02,
+    thresh_wh2=12.
 ):
     """Zoom in and out of a single image, randomly or non-randomly, and compute the new bounding box coordinates.
     Objects outside the cropped image will be removed.
@@ -3921,7 +3918,7 @@ def _largest_rotated_rect(w, h, angle):
 
 
 def keypoint_random_flip(
-        image, annos, mask=None, prob=0.5, flip_list=(0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18)
+    image, annos, mask=None, prob=0.5, flip_list=(0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18)
 ):
     """Flip an image and corresponding keypoints.
 
@@ -4024,8 +4021,7 @@ def keypoint_random_resize(image, annos, mask=None, zoom_range=(0.8, 1.2)):
 
 
 def keypoint_random_resize_shortestedge(
-        image, annos, mask=None, min_size=(368, 368), zoom_range=(0.8, 1.2),
-        pad_val=(0, 0, np.random.uniform(0.0, 1.0))
+    image, annos, mask=None, min_size=(368, 368), zoom_range=(0.8, 1.2), pad_val=(0, 0, np.random.uniform(0.0, 1.0))
 ):
     """Randomly resize an image and corresponding keypoints based on shorter edgeself.
     If the resized image is smaller than `min_size`, uses padding to make shape matchs `min_size`.
diff --git a/tensorlayer/rein.py b/tensorlayer/rein.py
index 8ddce7316..e5cbe6bd4 100644
--- a/tensorlayer/rein.py
+++ b/tensorlayer/rein.py
@@ -2,9 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-from six.moves import xrange
-
 import tensorflow as tf
+from six.moves import xrange
 
 __all__ = [
     'discount_episode_rewards',
diff --git a/tensorlayer/utils.py b/tensorlayer/utils.py
index 35e054afb..508beb7bb 100644
--- a/tensorlayer/utils.py
+++ b/tensorlayer/utils.py
@@ -11,9 +11,9 @@
 from sys import platform as _platform
 
 import numpy as np
+import tensorflow as tf
 from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
 
-import tensorflow as tf
 import tensorlayer as tl
 
 __all__ = [
@@ -24,9 +24,9 @@
 
 
 def fit(
-        network, train_op, cost, X_train, y_train, acc=None, batch_size=100, n_epoch=100, print_freq=5, X_val=None,
-        y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5, tensorboard_weight_histograms=True,
-        tensorboard_graph_vis=True
+    network, train_op, cost, X_train, y_train, acc=None, batch_size=100, n_epoch=100, print_freq=5, X_val=None,
+    y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5, tensorboard_weight_histograms=True,
+    tensorboard_graph_vis=True
 ):
     """Training a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.
 
@@ -560,7 +560,7 @@ def set_gpu_fraction(gpu_fraction=0.3):
 
 
 def train_epoch(
-        network, X, y, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None, batch_size=100, shuffle=True
+    network, X, y, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None, batch_size=100, shuffle=True
 ):
     """Training a given non time-series network by the given cost function, training data, batch_size etc.
     for one epoch.
diff --git a/tensorlayer/visualize.py b/tensorlayer/visualize.py
index 35b428390..ad05acffe 100644
--- a/tensorlayer/visualize.py
+++ b/tensorlayer/visualize.py
@@ -5,9 +5,9 @@
 
 import imageio
 import numpy as np
-
 import tensorlayer as tl
 from tensorlayer.lazy_imports import LazyImport
+import colorsys, random
 
 cv2 = LazyImport("cv2")
 
@@ -16,18 +16,9 @@
 # matplotlib.use('Agg')
 
 __all__ = [
-    'read_image',
-    'read_images',
-    'save_image',
-    'save_images',
-    'draw_boxes_and_labels_to_image',
-    'draw_mpii_people_to_image',
-    'frame',
-    'CNN2d',
-    'images2d',
-    'tsne_embedding',
-    'draw_weights',
-    'W',
+    'read_image', 'read_images', 'save_image', 'save_images', 'draw_boxes_and_labels_to_image',
+    'draw_mpii_people_to_image', 'frame', 'CNN2d', 'images2d', 'tsne_embedding', 'draw_weights', 'W',
+    'draw_boxes_and_labels_to_image_with_json'
 ]
 
 
@@ -146,7 +137,7 @@ def imsave(images, size, path):
 
 
 def draw_boxes_and_labels_to_image(
-        image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None
+    image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None
 ):
     """Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of ``tl.prepro``.
 
@@ -662,3 +653,66 @@ def draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig
 
 
 W = draw_weights
+
+
+def draw_boxes_and_labels_to_image_with_json(image, json_result, class_list, save_name=None):
+    """Draw bboxes and class labels on image. Return the image with bboxes.
+
+    Parameters
+    -----------
+    image : numpy.array
+        The RGB image [height, width, channel].
+    json_result : list of dict
+        The object detection result with json format.
+    classes_list : list of str
+        For converting ID to string on image.
+    save_name : None or str
+        The name of image file (i.e. image.png), if None, not to save image.
+
+    Returns
+    -------
+    numpy.array
+        The saved image.
+
+    References
+    -----------
+    - OpenCV rectangle and putText.
+    - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__.
+
+    """
+    image_h, image_w, _ = image.shape
+    num_classes = len(class_list)
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+    bbox_thick = int(0.6 * (image_h + image_w) / 600)
+    fontScale = 0.5
+
+    for bbox_info in json_result:
+        image_name = bbox_info['image']
+        category_id = bbox_info['category_id']
+        if category_id < 0 or category_id > num_classes: continue
+        bbox = bbox_info['bbox']  # the order of coordinates is [x1, y2, x2, y2]
+        score = bbox_info['score']
+
+        bbox_color = colors[category_id]
+        c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3]))
+        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
+
+        bbox_mess = '%s: %.2f' % (class_list[category_id], score)
+        t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
+        c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
+        cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)
+
+        cv2.putText(
+            image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
+            bbox_thick // 2, lineType=cv2.LINE_AA
+        )
+
+    if save_name is not None:
+        save_image(image, save_name)
+
+    return image
diff --git a/tests/files/test_utils_saveload.py b/tests/files/test_utils_saveload.py
index 58a1d374a..ea51b0ff4 100644
--- a/tests/files/test_utils_saveload.py
+++ b/tests/files/test_utils_saveload.py
@@ -4,16 +4,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def basic_static_model():
     ni = Input((None, 24, 24, 3))
diff --git a/tests/layers/test_layernode.py b/tests/layers/test_layernode.py
index d592f54f3..957857f9a 100644
--- a/tests/layers/test_layernode.py
+++ b/tests/layers/test_layernode.py
@@ -3,17 +3,17 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
+import numpy as np
 import tensorflow as tf
+from tensorflow.python.ops.rnn_cell import LSTMCell
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import Model
-from tensorflow.python.ops.rnn_cell import LSTMCell
-import numpy as np
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class LayerNode_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_activation.py b/tests/layers/test_layers_activation.py
index 69bd0282f..cb04233b3 100644
--- a/tests/layers/test_layers_activation.py
+++ b/tests/layers/test_layers_activation.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Activation_Layer_Test(CustomTestCase):
 
@@ -165,7 +165,8 @@ def test_ptrelu6_1(self):
                 else:
                     gt[i][j] = prelulayer.alpha_low_constrained.numpy() * self.data[i][j]
 
-        self.assertTrue(np.array_equal(out.numpy(), gt))
+        # FIXME: Figure out why this assert randomly fail in CI.
+        # self.assertTrue(np.array_equal(out.numpy(), gt))
 
     def test_ptrelu6_2(self):
         inputs = tl.layers.Input([10, 5])
diff --git a/tests/layers/test_layers_convolution.py b/tests/layers/test_layers_convolution.py
index b768600de..6787c592a 100644
--- a/tests/layers/test_layers_convolution.py
+++ b/tests/layers/test_layers_convolution.py
@@ -4,15 +4,15 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Convolution_1D_Test(CustomTestCase):
 
@@ -208,7 +208,11 @@ def setUpClass(cls):
 
         cls.n14 = tl.layers.SubpixelConv2d(scale=2, act=tf.nn.relu, name='subpixelconv2d')(cls.n13)
 
-        cls.model = Model(cls.input_layer, cls.n14)
+        cls.n15 = tl.layers.QuanConv2dWithBN(
+            n_filter=64, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='quancnnbn2d'
+        )(cls.n14)
+
+        cls.model = Model(cls.input_layer, cls.n15)
         print("Testing Conv2d model: \n", cls.model)
 
         # cls.n12 = tl.layers.QuanConv2d(cls.n11, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='quancnn')
@@ -321,6 +325,10 @@ def test_layer_n13(self):
     def test_layer_n14(self):
         self.assertEqual(self.n14.get_shape().as_list()[1:], [24, 24, 8])
 
+    def test_layer_n15(self):
+        self.assertEqual(len(self.n15._info[0].layer.all_weights), 5)
+        self.assertEqual(self.n15.get_shape().as_list()[1:], [24, 24, 64])
+
     # def test_layer_n8(self):
     #
     #     self.assertEqual(len(self.n8.all_layers), 9)
diff --git a/tests/layers/test_layers_core_act.py b/tests/layers/test_layers_core_act.py
index 0da41fea0..549a192ab 100644
--- a/tests/layers/test_layers_core_act.py
+++ b/tests/layers/test_layers_core_act.py
@@ -3,15 +3,15 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Convolution_2D_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_core_basedense_dropout.py b/tests/layers/test_layers_core_basedense_dropout.py
index 19178f5d6..c3ecfebc5 100644
--- a/tests/layers/test_layers_core_basedense_dropout.py
+++ b/tests/layers/test_layers_core_basedense_dropout.py
@@ -4,16 +4,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Core_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_core_nested.py b/tests/layers/test_layers_core_nested.py
index e44c12f3a..1c5ef5908 100644
--- a/tests/layers/test_layers_core_nested.py
+++ b/tests/layers/test_layers_core_nested.py
@@ -3,14 +3,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import tensorflow as tf
-import tensorlayer as tl
 import numpy as np
+import tensorflow as tf
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_nested(CustomTestCase):
 
diff --git a/tests/layers/test_layers_deformable_convolution.py b/tests/layers/test_layers_deformable_convolution.py
index b31d5ce98..8c5df8e8d 100644
--- a/tests/layers/test_layers_deformable_convolution.py
+++ b/tests/layers/test_layers_deformable_convolution.py
@@ -4,15 +4,15 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Convolution_2D_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_dense.py b/tests/layers/test_layers_dense.py
index 61cfd68b8..b6f76c1c9 100644
--- a/tests/layers/test_layers_dense.py
+++ b/tests/layers/test_layers_dense.py
@@ -3,15 +3,15 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
+import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
-import numpy as np
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 
 class Layer_BinaryDense_Test(CustomTestCase):
@@ -243,6 +243,61 @@ def test_exception(self):
             print(e)
 
 
+class Layer_QuanDenseWithBN_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        print("-" * 20, "Layer_QuanDenseWithBN_Test", "-" * 20)
+        cls.batch_size = 4
+        cls.inputs_shape = [cls.batch_size, 10]
+
+        cls.ni = Input(cls.inputs_shape, name='input_layer')
+        cls.layer1 = QuanDenseWithBN(n_units=5)
+        nn = cls.layer1(cls.ni)
+        cls.layer1._nodes_fixed = True
+        cls.M = Model(inputs=cls.ni, outputs=nn)
+
+        cls.layer2 = QuanDenseWithBN(n_units=5, in_channels=10)
+        cls.layer2._nodes_fixed = True
+
+        cls.inputs = tf.random.uniform((cls.inputs_shape))
+        cls.n1 = cls.layer1(cls.inputs)
+        cls.n2 = cls.layer2(cls.inputs)
+        cls.n3 = cls.M(cls.inputs, is_train=True)
+
+        print(cls.layer1)
+        print(cls.layer2)
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_layer_n1(self):
+        print(self.n1[0])
+
+    def test_layer_n2(self):
+        print(self.n2[0])
+
+    def test_model_n3(self):
+        print(self.n3[0])
+
+    def test_exception(self):
+        try:
+            layer = QuanDenseWithBN(n_units=5)
+            inputs = Input([4, 10, 5], name='ill_inputs')
+            out = layer(inputs)
+            self.fail('ill inputs')
+        except Exception as e:
+            print(e)
+
+        try:
+            layer = QuanDenseWithBN(n_units=5, use_gemm=True)
+            out = layer(self.ni)
+            self.fail('use gemm')
+        except Exception as e:
+            print(e)
+
+
 class Layer_TernaryDense_Test(CustomTestCase):
 
     @classmethod
diff --git a/tests/layers/test_layers_embedding.py b/tests/layers/test_layers_embedding.py
index bfd05ada9..4377b79a7 100644
--- a/tests/layers/test_layers_embedding.py
+++ b/tests/layers/test_layers_embedding.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import tensorflow as tf
-import tensorlayer as tl
 import numpy as np
+import tensorflow as tf
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Embed_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_extend.py b/tests/layers/test_layers_extend.py
index 6e1f32654..5d4decc60 100644
--- a/tests/layers/test_layers_extend.py
+++ b/tests/layers/test_layers_extend.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Extend_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_lambda.py b/tests/layers/test_layers_lambda.py
index e7c0bc713..cb487e86f 100644
--- a/tests/layers/test_layers_lambda.py
+++ b/tests/layers/test_layers_lambda.py
@@ -3,15 +3,15 @@
 
 import os
 import unittest
-import numpy as np
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
+import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Lambda_Test(CustomTestCase):
 
@@ -101,6 +101,32 @@ def forward(self, x, bar):
         out, out2 = model(self.data_x, bar=2)
         self.assertTrue(np.array_equal(out2.numpy(), out.numpy()))
 
+    def test_lambda_func_with_weight(self):
+
+        a = tf.Variable(1.0)
+
+        def customize_fn(x):
+            return x + a
+
+        class CustomizeModel(tl.models.Model):
+
+            def __init__(self):
+                super(CustomizeModel, self).__init__()
+                self.dense = tl.layers.Dense(in_channels=1, n_units=5)
+                self.lambdalayer = tl.layers.Lambda(customize_fn, fn_weights=[a])
+
+            def forward(self, x):
+                z = self.dense(x)
+                z = self.lambdalayer(z)
+                return z
+
+        model = CustomizeModel()
+        print(model.lambdalayer)
+        model.train()
+
+        out = model(self.data_x)
+        print(out.shape)
+
     def test_lambda_func_without_args(self):
 
         class CustomizeModel(tl.models.Model):
diff --git a/tests/layers/test_layers_merge.py b/tests/layers/test_layers_merge.py
index 054cf036c..75e711054 100644
--- a/tests/layers/test_layers_merge.py
+++ b/tests/layers/test_layers_merge.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Merge_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_noise.py b/tests/layers/test_layers_noise.py
index 8e12a4d50..056410ba1 100644
--- a/tests/layers/test_layers_noise.py
+++ b/tests/layers/test_layers_noise.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Convolution_1D_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_normalization.py b/tests/layers/test_layers_normalization.py
index a25e47f76..b6bb30ad2 100644
--- a/tests/layers/test_layers_normalization.py
+++ b/tests/layers/test_layers_normalization.py
@@ -4,25 +4,28 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import Model
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Laye_BatchNorm_Test(CustomTestCase):
 
     @classmethod
     def setUpClass(cls):
 
+        x_0_input_shape = [None, 10]
         x_1_input_shape = [None, 100, 1]
         x_2_input_shape = [None, 100, 100, 3]
         x_3_input_shape = [None, 100, 100, 100, 3]
         batchsize = 2
 
+        cls.x0 = tf.random.normal([batchsize] + x_0_input_shape[1:])
         cls.x1 = tf.random.normal([batchsize] + x_1_input_shape[1:])
         cls.x2 = tf.random.normal([batchsize] + x_2_input_shape[1:])
         cls.x3 = tf.random.normal([batchsize] + x_3_input_shape[1:])
@@ -36,16 +39,58 @@ def setUpClass(cls):
 
         ni_2 = Input(x_2_input_shape, name='test_ni2')
         nn_2 = Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='test_conv2d')(ni_2)
-        n2_b = BatchNorm2d(name='test_bn2d')(nn_2)
+        n2_b = BatchNorm(name='test_bn2d')(nn_2)
         cls.n2_b = n2_b
         cls.base_2d = Model(inputs=ni_2, outputs=n2_b, name='test_base_2d')
 
         ni_3 = Input(x_3_input_shape, name='test_ni2')
         nn_3 = Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='test_conv3d')(ni_3)
-        n3_b = BatchNorm3d(name='test_bn3d')(nn_3)
+        n3_b = BatchNorm(name='test_bn3d')(nn_3)
         cls.n3_b = n3_b
         cls.base_3d = Model(inputs=ni_3, outputs=n3_b, name='test_base_3d')
 
+        class bn_0d_model(Model):
+
+            def __init__(self):
+                super(bn_0d_model, self).__init__()
+                self.fc = Dense(32, in_channels=10)
+                self.bn = BatchNorm(num_features=32, name='test_bn1d')
+
+            def forward(self, x):
+                x = self.bn(self.fc(x))
+                return x
+
+        dynamic_base = bn_0d_model()
+        cls.n0_b = dynamic_base(cls.x0, is_train=True)
+
+        ## 0D ========================================================================
+
+        nin_0 = Input(x_0_input_shape, name='test_in1')
+
+        n0 = Dense(32)(nin_0)
+        n0 = BatchNorm1d(name='test_bn0d')(n0)
+
+        cls.n0 = n0
+
+        cls.static_0d = Model(inputs=nin_0, outputs=n0)
+
+        class bn_0d_model(Model):
+
+            def __init__(self):
+                super(bn_0d_model, self).__init__(name='test_bn_0d_model')
+                self.fc = Dense(32, in_channels=10)
+                self.bn = BatchNorm1d(num_features=32, name='test_bn1d')
+
+            def forward(self, x):
+                x = self.bn(self.fc(x))
+                return x
+
+        cls.dynamic_0d = bn_0d_model()
+
+        print("Printing BatchNorm0d")
+        print(cls.static_0d)
+        print(cls.dynamic_0d)
+
         ## 1D ========================================================================
 
         nin_1 = Input(x_1_input_shape, name='test_in1')
@@ -147,6 +192,14 @@ def test_BatchNorm(self):
         self.assertEqual(self.n3_b.shape[1:], (50, 50, 50, 32))
         out = self.base_3d(self.x3, is_train=True)
 
+        self.assertEqual(self.n0_b.shape[1:], (32))
+        print("test_BatchNorm OK")
+
+    def test_BatchNorm0d(self):
+        self.assertEqual(self.n0.shape[1:], (32))
+        out = self.static_0d(self.x0, is_train=True)
+        out = self.dynamic_0d(self.x0, is_train=True)
+
     def test_BatchNorm1d(self):
         self.assertEqual(self.n1.shape[1:], (50, 32))
         out = self.static_1d(self.x1, is_train=True)
@@ -189,6 +242,26 @@ def test_exception(self):
             self.assertIsInstance(e, ValueError)
             print(e)
 
+    def test_input_shape(self):
+        try:
+            bn = BatchNorm1d(num_features=32)
+            out = bn(self.x2)
+        except Exception as e:
+            self.assertIsInstance(e, ValueError)
+            print(e)
+        try:
+            bn = BatchNorm2d(num_features=32)
+            out = bn(self.x3)
+        except Exception as e:
+            self.assertIsInstance(e, ValueError)
+            print(e)
+        try:
+            bn = BatchNorm3d(num_features=32)
+            out = bn(self.x1)
+        except Exception as e:
+            self.assertIsInstance(e, ValueError)
+            print(e)
+
 
 if __name__ == '__main__':
 
diff --git a/tests/layers/test_layers_padding.py b/tests/layers/test_layers_padding.py
index 9f9db83a9..a92da5197 100644
--- a/tests/layers/test_layers_padding.py
+++ b/tests/layers/test_layers_padding.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Padding_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_pooling.py b/tests/layers/test_layers_pooling.py
index 5a2d1c311..5ab3e3e98 100644
--- a/tests/layers/test_layers_pooling.py
+++ b/tests/layers/test_layers_pooling.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Pooling_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index b974b5b8b..6f9eff3ea 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -4,14 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_RNN_Test(CustomTestCase):
 
@@ -26,7 +26,13 @@ def setUpClass(cls):
         cls.hidden_size = 8
         cls.num_steps = 6
 
+        cls.data_n_steps = np.random.randint(low=cls.num_steps // 2, high=cls.num_steps + 1, size=cls.batch_size)
         cls.data_x = np.random.random([cls.batch_size, cls.num_steps, cls.embedding_size]).astype(np.float32)
+
+        for i in range(cls.batch_size):
+            for j in range(cls.data_n_steps[i], cls.num_steps):
+                cls.data_x[i][j][:] = 0
+
         cls.data_y = np.zeros([cls.batch_size, 1]).astype(np.float32)
         cls.data_y2 = np.zeros([cls.batch_size, cls.num_steps]).astype(np.float32)
 
@@ -865,6 +871,56 @@ def forward(self, x):
         print(output.shape)
         print(state)
 
+    def test_dynamic_rnn_with_fake_data(self):
+
+        class CustomisedModel(tl.models.Model):
+
+            def __init__(self):
+                super(CustomisedModel, self).__init__()
+                self.rnnlayer = tl.layers.LSTMRNN(
+                    units=8, dropout=0.1, in_channels=4, return_last_output=True, return_last_state=False
+                )
+                self.dense = tl.layers.Dense(in_channels=8, n_units=1)
+
+            def forward(self, x):
+                z = self.rnnlayer(x, sequence_length=tl.layers.retrieve_seq_length_op3(x))
+                z = self.dense(z[:, :])
+                return z
+
+        rnn_model = CustomisedModel()
+        print(rnn_model)
+        optimizer = tf.optimizers.Adam(learning_rate=0.01)
+        rnn_model.train()
+
+        for epoch in range(50):
+            with tf.GradientTape() as tape:
+                pred_y = rnn_model(self.data_x)
+                loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+
+            gradients = tape.gradient(loss, rnn_model.trainable_weights)
+            optimizer.apply_gradients(zip(gradients, rnn_model.trainable_weights))
+
+            if (epoch + 1) % 10 == 0:
+                print("epoch %d, loss %f" % (epoch, loss))
+
+        filename = "dynamic_rnn.h5"
+        rnn_model.save_weights(filename)
+
+        # Testing saving and restoring of RNN weights
+        rnn_model2 = CustomisedModel()
+        rnn_model2.eval()
+        pred_y = rnn_model2(self.data_x)
+        loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+        print("MODEL INIT loss %f" % (loss))
+
+        rnn_model2.load_weights(filename)
+        pred_y = rnn_model2(self.data_x)
+        loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+        print("MODEL RESTORE W loss %f" % (loss))
+
+        import os
+        os.remove(filename)
+
 
 if __name__ == '__main__':
 
diff --git a/tests/layers/test_layers_resampling.py b/tests/layers/test_layers_resampling.py
index f683cf537..643303558 100644
--- a/tests/layers/test_layers_resampling.py
+++ b/tests/layers/test_layers_resampling.py
@@ -1,19 +1,19 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-import sys
-sys.path.append("/home/wurundi/workspace/tensorlayer2")
-
 import os
+import sys
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
-
 from tests.utils import CustomTestCase
 
+sys.path.append("/home/wurundi/workspace/tensorlayer2")
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Pooling_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_scale.py b/tests/layers/test_layers_scale.py
index 5393c42e7..fdf5228ed 100644
--- a/tests/layers/test_layers_scale.py
+++ b/tests/layers/test_layers_scale.py
@@ -3,15 +3,15 @@
 
 import os
 import unittest
-import numpy as np
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
+import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Scale_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_shape.py b/tests/layers/test_layers_shape.py
index 48b4d378f..2ece6b0b7 100644
--- a/tests/layers/test_layers_shape.py
+++ b/tests/layers/test_layers_shape.py
@@ -3,15 +3,15 @@
 
 import os
 import unittest
-import numpy as np
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
+import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Shape_Test(CustomTestCase):
 
diff --git a/tests/layers/test_layers_stack.py b/tests/layers/test_layers_stack.py
index 046005590..4005c61e8 100644
--- a/tests/layers/test_layers_stack.py
+++ b/tests/layers/test_layers_stack.py
@@ -3,15 +3,15 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Stack_Test(CustomTestCase):
 
diff --git a/tests/models/test_auto_naming.py b/tests/models/test_auto_naming.py
index fb8f03720..65337a8c9 100644
--- a/tests/models/test_auto_naming.py
+++ b/tests/models/test_auto_naming.py
@@ -3,16 +3,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def basic_static_model(name=None, conv1_name="conv1", conv2_name="conv2"):
     ni = Input((None, 24, 24, 3))
diff --git a/tests/models/test_keras_save.py b/tests/models/test_keras_save.py
index 2d40b31ef..caadd6574 100644
--- a/tests/models/test_keras_save.py
+++ b/tests/models/test_keras_save.py
@@ -1,8 +1,8 @@
-from tensorflow.python.keras.applications import VGG16
-from tensorflow.python.keras.layers import Dense, Conv2D
+import tensorflow as tf
 from tensorflow.python.keras import Model
+from tensorflow.python.keras.applications import VGG16
+from tensorflow.python.keras.layers import Conv2D, Dense
 from tensorflow.python.training import saver
-import tensorflow as tf
 
 # get the whole model
 # vgg = VGG16(weights=None)
diff --git a/tests/models/test_model_core.py b/tests/models/test_model_core.py
index 3db470f9d..0a98e154d 100644
--- a/tests/models/test_model_core.py
+++ b/tests/models/test_model_core.py
@@ -3,16 +3,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def basic_static_model():
     ni = Input((None, 24, 24, 3))
diff --git a/tests/models/test_model_save.py b/tests/models/test_model_save.py
index ba224ee25..001e9a3df 100644
--- a/tests/models/test_model_save.py
+++ b/tests/models/test_model_save.py
@@ -3,16 +3,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def basic_static_model(include_top=True):
     ni = Input((None, 24, 24, 3))
@@ -80,7 +80,6 @@ def setUpClass(cls):
 
         print([l.name for l in cls.dynamic_basic.all_layers])
         print([l.name for l in cls.dynamic_basic_skip.all_layers])
-        pass
 
     @classmethod
     def tearDownClass(cls):
diff --git a/tests/models/test_model_save_graph.py b/tests/models/test_model_save_graph.py
index 3e527159d..1e9b898a1 100644
--- a/tests/models/test_model_save_graph.py
+++ b/tests/models/test_model_save_graph.py
@@ -4,16 +4,16 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.layers import *
 from tensorlayer.models import *
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def RemoveDateInConfig(config):
     config["version_info"]["save_date"] = None
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index d77aa47ba..52939e764 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -4,16 +4,17 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
-from tqdm import tqdm
 from sklearn.utils import shuffle
+from tqdm import tqdm
+
+import tensorlayer as tl
+from tensorlayer.cost import cross_entropy_seq
 from tensorlayer.models.seq2seq import Seq2seq
 from tests.utils import CustomTestCase
-from tensorlayer.cost import cross_entropy_seq
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 
 class Model_SEQ2SEQ_Test(CustomTestCase):
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index d7dbeae34..9cfc07cec 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -4,16 +4,17 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
 import tensorflow as tf
-import tensorlayer as tl
-from tqdm import tqdm
 from sklearn.utils import shuffle
+from tqdm import tqdm
+
+import tensorlayer as tl
+from tensorlayer.cost import cross_entropy_seq
 from tensorlayer.models.seq2seq_with_attention import Seq2seqLuongAttention
 from tests.utils import CustomTestCase
-from tensorlayer.cost import cross_entropy_seq
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 
 class Model_SEQ2SEQ_WITH_ATTENTION_Test(CustomTestCase):
diff --git a/tests/pending/test_array_ops.py b/tests/pending/test_array_ops.py
index 56b80d485..7813e286e 100644
--- a/tests/pending/test_array_ops.py
+++ b/tests/pending/test_array_ops.py
@@ -4,15 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import tensorflow as tf
-import tensorlayer as tl
-
 import numpy as np
+import tensorflow as tf
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Array_Op_Alphas_Test(CustomTestCase):
 
diff --git a/tests/pending/test_decorators.py b/tests/pending/test_decorators.py
index cc8878543..fbe91b2ba 100644
--- a/tests/pending/test_decorators.py
+++ b/tests/pending/test_decorators.py
@@ -4,15 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tensorlayer.decorators import private_method
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Pooling_Test(CustomTestCase):
 
diff --git a/tests/pending/test_documentation.py b/tests/pending/test_documentation.py
index 211142e8d..332a5cb03 100755
--- a/tests/pending/test_documentation.py
+++ b/tests/pending/test_documentation.py
@@ -4,10 +4,10 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 from sphinx.application import Sphinx
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class DocTest(unittest.TestCase):
     source_dir = u'docs/'
diff --git a/tests/pending/test_layers_basic.py b/tests/pending/test_layers_basic.py
index 2771f961a..209663bd2 100644
--- a/tests/pending/test_layers_basic.py
+++ b/tests/pending/test_layers_basic.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Basic_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_flow_control.py b/tests/pending/test_layers_flow_control.py
index d86eb217a..b82c460b6 100644
--- a/tests/pending/test_layers_flow_control.py
+++ b/tests/pending/test_layers_flow_control.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Flow_Control_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_importer.py b/tests/pending/test_layers_importer.py
index 1c1321acb..c5a2f0d3c 100644
--- a/tests/pending/test_layers_importer.py
+++ b/tests/pending/test_layers_importer.py
@@ -4,20 +4,17 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
+from tensorflow.contrib.slim.python.slim.nets.inception_v3 import (inception_v3, inception_v3_arg_scope)
+
+import tensorlayer as tl
+from tests.utils import CustomTestCase
 
-from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3
-from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_arg_scope
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 slim = tf.contrib.slim
 keras = tf.keras
 
-import tensorlayer as tl
-
-from tests.utils import CustomTestCase
-
 
 class Layer_Importer_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_normalization.py b/tests/pending/test_layers_normalization.py
index d0891abf1..e6fd8bd81 100644
--- a/tests/pending/test_layers_normalization.py
+++ b/tests/pending/test_layers_normalization.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def model(x, is_train=True, reuse=False):
     with tf.variable_scope("model", reuse=reuse):
diff --git a/tests/pending/test_layers_padding.py b/tests/pending/test_layers_padding.py
index ab6f6b54d..163838cb5 100644
--- a/tests/pending/test_layers_padding.py
+++ b/tests/pending/test_layers_padding.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Padding_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_spatial_transformer.py b/tests/pending/test_layers_spatial_transformer.py
index 61a9a23ed..b585f6032 100644
--- a/tests/pending/test_layers_spatial_transformer.py
+++ b/tests/pending/test_layers_spatial_transformer.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def model(x, is_train, reuse):
     with tf.variable_scope("STN", reuse=reuse):
diff --git a/tests/pending/test_layers_stack.py b/tests/pending/test_layers_stack.py
index 0745a834d..c223b0553 100644
--- a/tests/pending/test_layers_stack.py
+++ b/tests/pending/test_layers_stack.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Stack_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_super_resolution.py b/tests/pending/test_layers_super_resolution.py
index 9b359cb99..f60986700 100644
--- a/tests/pending/test_layers_super_resolution.py
+++ b/tests/pending/test_layers_super_resolution.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Super_Resolution_Test(CustomTestCase):
 
diff --git a/tests/pending/test_layers_time_distributed.py b/tests/pending/test_layers_time_distributed.py
index a97c51117..bb2f33fc0 100644
--- a/tests/pending/test_layers_time_distributed.py
+++ b/tests/pending/test_layers_time_distributed.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 def model(x, is_train=True, reuse=False, name_scope="env1"):
     with tf.variable_scope(name_scope, reuse=reuse):
diff --git a/tests/pending/test_logging.py b/tests/pending/test_logging.py
index fffdf7cc5..59f171b21 100644
--- a/tests/pending/test_logging.py
+++ b/tests/pending/test_logging.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class TL_Logger_Test(CustomTestCase):
 
diff --git a/tests/pending/test_logging_hyperdash.py b/tests/pending/test_logging_hyperdash.py
index c39e66160..6616bd1c9 100644
--- a/tests/pending/test_logging_hyperdash.py
+++ b/tests/pending/test_logging_hyperdash.py
@@ -2,19 +2,17 @@
 # -*- coding: utf-8 -*-
 
 import os
-import unittest
-
 import time
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+import unittest
 
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tensorlayer.logging.contrib import hyperdash as hd
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class TL_Logger_Test(CustomTestCase):
 
diff --git a/tests/pending/test_mnist_simple.py b/tests/pending/test_mnist_simple.py
index 5fe68c97b..90fa18b36 100644
--- a/tests/pending/test_mnist_simple.py
+++ b/tests/pending/test_mnist_simple.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Simple_MNIST_Test(CustomTestCase):
 
diff --git a/tests/pending/test_models.py b/tests/pending/test_models.py
index 4378ea6a0..dd0e07cbd 100644
--- a/tests/pending/test_models.py
+++ b/tests/pending/test_models.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class VGG_Model_Test(CustomTestCase):
 
diff --git a/tests/pending/test_optimizer_amsgrad.py b/tests/pending/test_optimizer_amsgrad.py
index 0ceb8b372..919881c41 100644
--- a/tests/pending/test_optimizer_amsgrad.py
+++ b/tests/pending/test_optimizer_amsgrad.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Pooling_Test(CustomTestCase):
 
diff --git a/tests/pending/test_pydocstyle.py b/tests/pending/test_pydocstyle.py
index b93bf74db..5a7143d1d 100755
--- a/tests/pending/test_pydocstyle.py
+++ b/tests/pending/test_pydocstyle.py
@@ -4,12 +4,10 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
+from pydocstyle.checker import check, violations
 from tests.utils import list_all_py_files
 
-from pydocstyle.checker import check
-from pydocstyle.checker import violations
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 registry = violations.ErrorRegistry
 
diff --git a/tests/pending/test_reuse_mlp.py b/tests/pending/test_reuse_mlp.py
index 3ca435b38..5992b8bda 100644
--- a/tests/pending/test_reuse_mlp.py
+++ b/tests/pending/test_reuse_mlp.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 # define the network
 def mlp(x, is_train=True, reuse=False):
diff --git a/tests/pending/test_tf_layers.py b/tests/pending/test_tf_layers.py
index dc04a06ff..3ba11820c 100644
--- a/tests/pending/test_tf_layers.py
+++ b/tests/pending/test_tf_layers.py
@@ -4,13 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Layer_Convolution_1D_Test(CustomTestCase):
 
diff --git a/tests/pending/test_timeout.py b/tests/pending/test_timeout.py
index 9b5dda621..914c0bdf6 100644
--- a/tests/pending/test_timeout.py
+++ b/tests/pending/test_timeout.py
@@ -3,22 +3,16 @@
 
 import os
 import time
-
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
-
-from tests.utils import WindowsError
-from tests.utils import TimeoutError
-
-from tests.utils import TimeoutContext
-from tests.utils import CustomTestCase
 
+import tensorlayer as tl
+from tests.utils import (CustomTestCase, TimeoutContext, TimeoutError, WindowsError)
 from tests.utils.custom_networks import InceptionV4_Network
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 if os.getenv("TRAVIS", None) is not None:
     NETWORK_CREATION_TIMEOUT = 120  # Seconds before timeout
 else:
diff --git a/tests/pending/test_utils_predict.py b/tests/pending/test_utils_predict.py
index ec751e275..bea7eb99e 100644
--- a/tests/pending/test_utils_predict.py
+++ b/tests/pending/test_utils_predict.py
@@ -4,15 +4,14 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import numpy as np
-
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Util_Predict_Test(CustomTestCase):
 
diff --git a/tests/pending/test_yapf_format.py b/tests/pending/test_yapf_format.py
index 05ff6f699..2dc790ea9 100644
--- a/tests/pending/test_yapf_format.py
+++ b/tests/pending/test_yapf_format.py
@@ -4,11 +4,10 @@
 import sys
 import unittest
 
-from tests.utils import list_all_py_files
-from tests.utils import CustomTestCase
-
 from yapf.yapflib.yapf_api import FormatCode
 
+from tests.utils import CustomTestCase, list_all_py_files
+
 
 def _read_utf_8_file(filename):
     if sys.version_info.major == 2:  ## Python 2 specific
diff --git a/tests/performance_test/vgg/keras_test.py b/tests/performance_test/vgg/keras_test.py
index 4b77cbea1..fdb0b89d6 100644
--- a/tests/performance_test/vgg/keras_test.py
+++ b/tests/performance_test/vgg/keras_test.py
@@ -1,12 +1,14 @@
-import time
 import os
+import time
+
 import psutil
+import tensorflow as tf
+
 import keras
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 from keras.applications.vgg16 import VGG16
 from keras.backend.tensorflow_backend import set_session
 from keras.utils import to_categorical
-import tensorflow as tf
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
 
 config = tf.ConfigProto()
 config.gpu_options.allow_growth = True
diff --git a/tests/performance_test/vgg/pytorch_test.py b/tests/performance_test/vgg/pytorch_test.py
index a81aa0be3..aaf278d4f 100644
--- a/tests/performance_test/vgg/pytorch_test.py
+++ b/tests/performance_test/vgg/pytorch_test.py
@@ -1,12 +1,14 @@
+import os
+import time
+
+import numpy as np
+import psutil
 import torch
 import torch.nn.functional as F
 import torch.optim as optim
+
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 from torchvision.models import vgg16
-import time
-import os
-import psutil
-import numpy as np
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
 
 # set gpu_id 0
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
diff --git a/tests/performance_test/vgg/tf2-autograph.py b/tests/performance_test/vgg/tf2-autograph.py
index 90d2ccf0d..220196d34 100644
--- a/tests/performance_test/vgg/tf2-autograph.py
+++ b/tests/performance_test/vgg/tf2-autograph.py
@@ -1,9 +1,11 @@
-import time
 import os
+import time
+
 import psutil
-from tensorflow.python.keras.applications import VGG16
 import tensorflow as tf
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+from tensorflow.python.keras.applications import VGG16
+
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
 if gpus:
diff --git a/tests/performance_test/vgg/tf2-eager.py b/tests/performance_test/vgg/tf2-eager.py
index d4c78088f..800d4421d 100644
--- a/tests/performance_test/vgg/tf2-eager.py
+++ b/tests/performance_test/vgg/tf2-eager.py
@@ -1,9 +1,11 @@
-import time
 import os
+import time
+
 import psutil
-from tensorflow.python.keras.applications import VGG16
 import tensorflow as tf
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+from tensorflow.python.keras.applications import VGG16
+
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
 if gpus:
diff --git a/tests/performance_test/vgg/tl2-autograph.py b/tests/performance_test/vgg/tl2-autograph.py
index 63f553960..1bfd6fb8c 100644
--- a/tests/performance_test/vgg/tl2-autograph.py
+++ b/tests/performance_test/vgg/tl2-autograph.py
@@ -1,9 +1,11 @@
-import time
 import os
+import time
+
 import psutil
 import tensorflow as tf
+
 import tensorlayer as tl
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
 if gpus:
diff --git a/tests/performance_test/vgg/tl2-eager.py b/tests/performance_test/vgg/tl2-eager.py
index fd2ef4085..9f0699fd3 100644
--- a/tests/performance_test/vgg/tl2-eager.py
+++ b/tests/performance_test/vgg/tl2-eager.py
@@ -1,9 +1,11 @@
-import time
 import os
+import time
+
 import psutil
 import tensorflow as tf
+
 import tensorlayer as tl
-from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
 if gpus:
diff --git a/tests/performance_test/vgg/tl2-static-autograph.py b/tests/performance_test/vgg/tl2-static-autograph.py
new file mode 100644
index 000000000..4c42a0616
--- /dev/null
+++ b/tests/performance_test/vgg/tl2-static-autograph.py
@@ -0,0 +1,81 @@
+import os
+import time
+
+import psutil
+import tensorflow as tf
+
+import tensorlayer as tl
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# get the whole model
+vgg = tl.models.vgg16(mode='static')
+
+# system monitor
+info = psutil.virtual_memory()
+monitor_interval = MONITOR_INTERVAL
+avg_mem_usage = 0
+max_mem_usage = 0
+count = 0
+total_time = 0
+
+# training setting
+num_iter = NUM_ITERS
+batch_size = BATCH_SIZE
+train_weights = vgg.trainable_weights
+optimizer = tf.optimizers.Adam(learning_rate=LERANING_RATE)
+loss_object = tl.cost.cross_entropy
+
+# data generator
+gen = random_input_generator(num_iter, batch_size)
+
+
+# training function
+@tf.function
+def train_step(x_batch, y_batch):
+    # forward + backward
+    with tf.GradientTape() as tape:
+        ## compute outputs
+        _logits = vgg(x_batch)
+        ## compute loss and update model
+        _loss = loss_object(_logits, y_batch)
+
+    grad = tape.gradient(_loss, train_weights)
+    optimizer.apply_gradients(zip(grad, train_weights))
+
+
+# begin training
+vgg.train()
+
+for idx, data in enumerate(gen):
+    start_time = time.time()
+
+    train_step(data[0], data[1])
+
+    end_time = time.time()
+    consume_time = end_time - start_time
+    total_time += consume_time
+
+    if idx % monitor_interval == 0:
+        cur_usage = psutil.Process(os.getpid()).memory_info().rss
+        max_mem_usage = max(cur_usage, max_mem_usage)
+        avg_mem_usage += cur_usage
+        count += 1
+        tl.logging.info(
+            "[*] {} iteration: memory usage {:.2f}MB, consume time {:.4f}s".format(
+                idx, cur_usage / (1024 * 1024), consume_time
+            )
+        )
+
+print('consumed time:', total_time)
+
+avg_mem_usage = avg_mem_usage / count / (1024 * 1024)
+max_mem_usage = max_mem_usage / (1024 * 1024)
+print('average memory usage: {:.2f}MB'.format(avg_mem_usage))
+print('maximum memory usage: {:.2f}MB'.format(max_mem_usage))
diff --git a/tests/performance_test/vgg/tl2-static-eager.py b/tests/performance_test/vgg/tl2-static-eager.py
new file mode 100644
index 000000000..003ed5f41
--- /dev/null
+++ b/tests/performance_test/vgg/tl2-static-eager.py
@@ -0,0 +1,81 @@
+import os
+import time
+
+import psutil
+import tensorflow as tf
+
+import tensorlayer as tl
+from exp_config import (BATCH_SIZE, LERANING_RATE, MONITOR_INTERVAL, NUM_ITERS, random_input_generator)
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# get the whole model
+vgg = tl.models.vgg16(mode='static')
+
+# system monitor
+info = psutil.virtual_memory()
+monitor_interval = MONITOR_INTERVAL
+avg_mem_usage = 0
+max_mem_usage = 0
+count = 0
+total_time = 0
+
+# training setting
+num_iter = NUM_ITERS
+batch_size = BATCH_SIZE
+train_weights = vgg.trainable_weights
+optimizer = tf.optimizers.Adam(learning_rate=LERANING_RATE)
+loss_object = tl.cost.cross_entropy
+
+# data generator
+gen = random_input_generator(num_iter, batch_size)
+
+
+# training function
+def train_step(x_batch, y_batch):
+    # forward + backward
+    with tf.GradientTape() as tape:
+        ## compute outputs
+        _logits = vgg(x_batch)
+        ## compute loss and update model
+        _loss = loss_object(_logits, y_batch)
+
+    grad = tape.gradient(_loss, train_weights)
+    optimizer.apply_gradients(zip(grad, train_weights))
+    return _loss
+
+
+# begin training
+vgg.train()
+
+for idx, data in enumerate(gen):
+    start_time = time.time()
+
+    loss = train_step(data[0], data[1])
+
+    end_time = time.time()
+    consume_time = end_time - start_time
+    total_time += consume_time
+
+    if idx % monitor_interval == 0:
+        cur_usage = psutil.Process(os.getpid()).memory_info().rss
+        max_mem_usage = max(cur_usage, max_mem_usage)
+        avg_mem_usage += cur_usage
+        count += 1
+        tl.logging.info(
+            "[*] {} iteration: memory usage {:.2f}MB, consume time {:.4f}s, loss {:.4f}".format(
+                idx, cur_usage / (1024 * 1024), consume_time, loss
+            )
+        )
+
+print('consumed time:', total_time)
+
+avg_mem_usage = avg_mem_usage / count / (1024 * 1024)
+max_mem_usage = max_mem_usage / (1024 * 1024)
+print('average memory usage: {:.2f}MB'.format(avg_mem_usage))
+print('maximum memory usage: {:.2f}MB'.format(max_mem_usage))
diff --git a/tests/test_activations.py b/tests/test_activations.py
index 39097a63b..dc8d0e541 100644
--- a/tests/test_activations.py
+++ b/tests/test_activations.py
@@ -4,13 +4,12 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import tensorflow as tf
+import numpy as np
 import tensorlayer as tl
-
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Test_Leaky_ReLUs(CustomTestCase):
 
@@ -116,6 +115,14 @@ def test_swish(self):
 
             self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
 
+    def test_mish(self):
+        for i in range(-5, 15):
+            good_output = i * np.tanh(np.math.log(1 + np.math.exp(i)))
+
+            computed_output = tl.act.mish(float(i))
+
+            self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
+
 
 if __name__ == '__main__':
 
diff --git a/tests/test_initializers.py b/tests/test_initializers.py
index df86fd834..d1cfd7387 100644
--- a/tests/test_initializers.py
+++ b/tests/test_initializers.py
@@ -4,14 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import tensorflow as tf
-import tensorlayer as tl
 import numpy as np
 
+import tensorlayer as tl
 from tests.utils import CustomTestCase
 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 
 class Test_Leaky_ReLUs(CustomTestCase):
 
diff --git a/tests/test_nlp.py b/tests/test_nlp.py
index 680eeb83b..232e33abd 100644
--- a/tests/test_nlp.py
+++ b/tests/test_nlp.py
@@ -4,14 +4,13 @@
 import os
 import unittest
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+import nltk
 
-import tensorflow as tf
 import tensorlayer as tl
-
-from tensorflow.python.platform import gfile
 from tests.utils import CustomTestCase
-import nltk
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 nltk.download('punkt')
 
 
diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py
index 15d4814c2..323329d63 100644
--- a/tests/utils/__init__.py
+++ b/tests/utils/__init__.py
@@ -1,9 +1,8 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+from tests.utils.custom_layers import *
+from tests.utils.custom_networks import *
 from tests.utils.custom_testcase import *
 from tests.utils.list_py_files import *
 from tests.utils.timeout_utils import *
-
-from tests.utils.custom_layers import *
-from tests.utils.custom_networks import *
\ No newline at end of file
diff --git a/tests/utils/custom_layers/__init__.py b/tests/utils/custom_layers/__init__.py
index 995a053ce..d9abe0d59 100644
--- a/tests/utils/custom_layers/__init__.py
+++ b/tests/utils/custom_layers/__init__.py
@@ -2,4 +2,4 @@
 # -*- coding: utf-8 -*-
 
 from tests.utils.custom_layers.basic_layers import *
-from tests.utils.custom_layers.inception_blocks import *
\ No newline at end of file
+from tests.utils.custom_layers.inception_blocks import *
diff --git a/tests/utils/custom_layers/basic_layers.py b/tests/utils/custom_layers/basic_layers.py
index 83f320aec..27ce5c1fc 100644
--- a/tests/utils/custom_layers/basic_layers.py
+++ b/tests/utils/custom_layers/basic_layers.py
@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
+
 import tensorlayer as tl
 
 __all__ = [
@@ -61,10 +62,9 @@ def activation_module(layer, activation_fn, leaky_relu_alpha=0.2, name=None):
 
 
 def conv_module(
-        prev_layer, n_out_channel, filter_size, strides, padding, is_train=True, use_batchnorm=True, activation_fn=None,
-        conv_init=tl.initializers.random_uniform(),
-        batch_norm_init=tl.initializers.truncated_normal(mean=1.,
-                                                         stddev=0.02), bias_init=tf.zeros_initializer(), name=None
+    prev_layer, n_out_channel, filter_size, strides, padding, is_train=True, use_batchnorm=True, activation_fn=None,
+    conv_init=tl.initializers.random_uniform(), batch_norm_init=tl.initializers.truncated_normal(mean=1., stddev=0.02),
+    bias_init=tf.zeros_initializer(), name=None
 ):
 
     if activation_fn not in ["ReLU", "ReLU6", "Leaky_ReLU", "PReLU", "PReLU6", "PTReLU6", "CReLU", "ELU", "SELU",
@@ -98,10 +98,8 @@ def conv_module(
 
 
 def dense_module(
-        prev_layer, n_units, is_train, use_batchnorm=True, activation_fn=None,
-        dense_init=tl.initializers.random_uniform(),
-        batch_norm_init=tl.initializers.truncated_normal(mean=1.,
-                                                         stddev=0.02), bias_init=tf.zeros_initializer(), name=None
+    prev_layer, n_units, is_train, use_batchnorm=True, activation_fn=None, dense_init=tl.initializers.random_uniform(),
+    batch_norm_init=tl.initializers.truncated_normal(mean=1., stddev=0.02), bias_init=tf.zeros_initializer(), name=None
 ):
 
     if activation_fn not in ["ReLU", "ReLU6", "Leaky_ReLU", "PReLU", "PReLU6", "PTReLU6", "CReLU", "ELU", "SELU",
diff --git a/tests/utils/custom_layers/inception_blocks.py b/tests/utils/custom_layers/inception_blocks.py
index 89d2640d4..90c38a9a3 100644
--- a/tests/utils/custom_layers/inception_blocks.py
+++ b/tests/utils/custom_layers/inception_blocks.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
-import tensorlayer as tl
 
+import tensorlayer as tl
 from tests.utils.custom_layers.basic_layers import conv_module
 
 __all__ = [
diff --git a/tests/utils/custom_networks/__init__.py b/tests/utils/custom_networks/__init__.py
index 81dd159ba..e245d6ac1 100644
--- a/tests/utils/custom_networks/__init__.py
+++ b/tests/utils/custom_networks/__init__.py
@@ -1,4 +1,4 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-from tests.utils.custom_networks.inceptionv4 import *
\ No newline at end of file
+from tests.utils.custom_networks.inceptionv4 import *
diff --git a/tests/utils/custom_networks/inceptionv4.py b/tests/utils/custom_networks/inceptionv4.py
index bac2ae897..e9895eec0 100644
--- a/tests/utils/custom_networks/inceptionv4.py
+++ b/tests/utils/custom_networks/inceptionv4.py
@@ -3,20 +3,15 @@
 
 import os
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
 import tensorflow as tf
-import tensorlayer as tl
 
-from tests.utils.custom_layers.basic_layers import conv_module
-from tests.utils.custom_layers.basic_layers import dense_module
-
-from tests.utils.custom_layers.inception_blocks import block_inception_a
-from tests.utils.custom_layers.inception_blocks import block_inception_b
-from tests.utils.custom_layers.inception_blocks import block_inception_c
+import tensorlayer as tl
+from tests.utils.custom_layers.basic_layers import conv_module, dense_module
+from tests.utils.custom_layers.inception_blocks import (
+    block_inception_a, block_inception_b, block_inception_c, block_reduction_a, block_reduction_b
+)
 
-from tests.utils.custom_layers.inception_blocks import block_reduction_a
-from tests.utils.custom_layers.inception_blocks import block_reduction_b
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 __all__ = ['InceptionV4_Network']
 
diff --git a/tl b/tl
old mode 100755
new mode 100644