diff --git a/.travis.yml b/.travis.yml
index 0344dedbd..70097ede2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -7,6 +7,7 @@ python:
   
 install:
   - pip install tensorflow
+  - pip install -r requirements.txt
   - pip install -r tests/requirements.txt
   - pip install .
   
diff --git a/README.rst b/README.rst
new file mode 100644
index 000000000..28092cbba
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,451 @@
+|TensorLayer Logo|
+
+|Codacy Badge| |Gitter| |Build Status| |Documentation Status| |Docker Pulls|
+
+TensorLayer is a deep learning and reinforcement learning library on top
+of `TensorFlow <https://www.tensorflow.org>`__. It provides rich neural
+layers and utility functions to help researchers and engineers build
+real-world AI applications. TensorLayer is awarded the 2017 Best Open
+Source Software by the prestigious `ACM Multimedia
+Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
+
+-  Useful links: `Documentation <http://tensorlayer.readthedocs.io>`__,
+   `Examples <http://tensorlayer.readthedocs.io/en/latest/user/example.html>`__,
+   `中文文档 <https://tensorlayercn.readthedocs.io>`__,
+   `中文书 <http://www.broadview.com.cn/book/5059>`__
+
+News
+====
+
+-  [10 Apr] Load and visualize MPII dataset in one line of code.
+-  [05 Apr] Release `models
+   APIs <http://tensorlayer.readthedocs.io/en/latest/modules/models.html#>`__
+   for well-known pretained networks.
+-  [18 Mar] Release experimental APIs for binary networks.
+-  [18 Jan]
+   [《深度学习：一起玩转TensorLayer》](http://www.broadview.com.cn/book/5059)
+   (Deep Learning using TensorLayer)
+-  [17 Dec] Release experimental APIs for distributed training (by
+   `TensorPort <https://tensorport.com>`__). See `tiny
+   example <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_distributed.py>`__.
+-  [17 Nov] Release data augmentation APIs for object detection, see
+   `tl.prepro <http://tensorlayer.readthedocs.io/en/latest/modules/prepro.html#object-detection>`__.
+-  [17 Nov] Support `Convolutional
+   LSTM <https://arxiv.org/abs/1506.04214>`__, see
+   `ConvLSTMLayer <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#conv-lstm-layer>`__.
+-  [17 Nov] Support `Deformable
+   Convolution <https://arxiv.org/abs/1703.06211>`__, see
+   `DeformableConv2dLayer <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#d-deformable-conv>`__.
+-  [17 Sep] New example `Chatbot in 200 lines of
+   code <https://github.com/zsdonghao/seq2seq-chatbot>`__ for
+   `Seq2Seq <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#simple-seq2seq>`__.
+
+Installation
+============
+
+TensorLayer has pre-requisites including TensorFlow, numpy, matplotlib
+and nltk (optional). For GPU support, CUDA and cuDNN are required. The
+simplest way to install TensorLayer is:
+
+.. code:: bash
+
+    # for master version (Recommended)
+    $ pip install git+https://github.com/tensorlayer/tensorlayer.git 
+
+    # for stable version 
+    $ pip install tensorlayer
+
+Dockerfile is supplied to build images, build as usual
+
+.. code:: bash
+
+    # for CPU version
+    $ docker build -t tensorlayer:latest .
+
+    # for GPU version
+    $ docker build -t tensorlayer:latest-gpu -f Dockerfile.gpu . 
+
+Please check
+`documentation <http://tensorlayer.readthedocs.io/en/latest/user/installation.html>`__
+for detailed instructions.
+
+Examples and Tutorials
+======================
+
+Examples can be found `in this
+folder <https://github.com/zsdonghao/tensorlayer/tree/master/example>`__
+and `Github
+topic <https://github.com/search?q=topic%3Atensorlayer&type=Repositories>`__.
+
+Basics
+------
+
+-  Multi-layer perceptron (MNIST) - Classification task, see
+   `tutorial\_mnist\_simple.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`__.
+-  Multi-layer perceptron (MNIST) - Classification using Iterator, see
+   `method1 <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`__
+   and
+   `method2 <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout2.py>`__.
+
+Computer Vision
+---------------
+
+-  Denoising Autoencoder (MNIST). Classification task, see
+   `tutorial\_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`__.
+-  Stacked Denoising Autoencoder and Fine-Tuning (MNIST). Classification
+   task, see
+   `tutorial\_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`__.
+-  Convolutional Network (MNIST). Classification task, see
+   `tutorial\_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`__.
+-  Convolutional Network (CIFAR-10). Classification task, see
+   `tutorial\_cifar10.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10.py>`__
+   and
+   `tutorial\_cifar10\_tfrecord.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py>`__.
+-  VGG 16 (ImageNet). Classification task, see
+   `tl.models.VGG16 <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_vgg16.py>`__
+   or
+   `tutorial\_vgg16.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg16.py>`__.
+-  VGG 19 (ImageNet). Classification task, see
+   `tutorial\_vgg19.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py>`__.
+-  InceptionV3 (ImageNet). Classification task, see
+   `tutorial\_inceptionV3\_tfslim.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py>`__.
+-  SqueezeNet (ImageNet). Model compression, see
+   `tl.models.SqueezeNetV1 <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_squeezenetv1.py>`__
+   or
+   `tutorial\_squeezenet.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_squeezenet.py>`__
+-  MobileNet (ImageNet). Model compression, see
+   `tl.models.MobileNetV1 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_mobilenetv1.py>`__
+   or
+   `tutorial\_mobilenet.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mobilenet.py>`__.
+-  BinaryNet. Model compression, see
+   `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py>`__
+   `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_cifar10_tfrecord.py>`__.
+-  Ternary Weight Network. Model compression, see
+   `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_mnist_cnn.py>`__
+   `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_cifar10_tfrecord.py>`__.
+-  DoReFa-Net. Model compression, see
+   `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_mnist_cnn.py>`__
+   `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_cifar10_tfrecord.py>`__.
+-  Wide ResNet (CIFAR) by
+   `ritchieng <https://github.com/ritchieng/wideresnet-tensorlayer>`__.
+-  More CNN implementations of
+   `TF-Slim <https://github.com/tensorflow/models/tree/master/research/slim>`__
+   can be connected to TensorLayer via SlimNetsLayer.
+-  `Spatial Transformer Networks <https://arxiv.org/abs/1506.02025>`__
+   by
+   `zsdonghao <https://github.com/zsdonghao/Spatial-Transformer-Nets>`__.
+-  `U-Net for brain tumor
+   segmentation <https://github.com/zsdonghao/u-net-brain-tumor>`__ by
+   `zsdonghao <https://github.com/zsdonghao/u-net-brain-tumor>`__.
+-  Variational Autoencoder (VAE) for (CelebA) by
+   `yzwxx <https://github.com/yzwxx/vae-celebA>`__.
+-  Variational Autoencoder (VAE) for (MNIST) by
+   `BUPTLdy <https://github.com/BUPTLdy/tl-vae>`__.
+-  Image Captioning - Reimplementation of Google's
+   `im2txt <https://github.com/tensorflow/models/tree/master/research/im2txt>`__
+   by `zsdonghao <https://github.com/zsdonghao/Image-Captioning>`__.
+
+Natural Language Processing
+---------------------------
+
+-  Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset
+   for language modeling, see
+   `tutorial\_ptb\_lstm.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm.py>`__
+   and
+   `tutorial\_ptb\_lstm\_state\_is\_tuple.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py>`__.
+-  Word Embedding (Word2vec). Train a word embedding matrix, see
+   `tutorial\_word2vec\_basic.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_word2vec_basic.py>`__.
+-  Restore Embedding matrix. Restore a pre-train embedding matrix, see
+   `tutorial\_generate\_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`__.
+-  Text Generation. Generates new text scripts, using LSTM network, see
+   `tutorial\_generate\_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`__.
+-  Chinese Text Anti-Spam by
+   `pakrchen <https://github.com/pakrchen/text-antispam>`__.
+-  `Chatbot in 200 lines of
+   code <https://github.com/zsdonghao/seq2seq-chatbot>`__ for
+   `Seq2Seq <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#simple-seq2seq>`__.
+-  FastText Sentence Classification (IMDB), see
+   `tutorial\_imdb\_fasttext.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_imdb_fasttext.py>`__
+   by `tomtung <https://github.com/tomtung>`__.
+
+Adversarial Learning
+--------------------
+
+-  DCGAN (CelebA). Generating images by `Deep Convolutional Generative
+   Adversarial Networks <http://arxiv.org/abs/1511.06434>`__ by
+   `zsdonghao <https://github.com/zsdonghao/dcgan>`__.
+-  `Generative Adversarial Text to Image
+   Synthesis <https://github.com/zsdonghao/text-to-image>`__ by
+   `zsdonghao <https://github.com/zsdonghao/text-to-image>`__.
+-  `Unsupervised Image to Image Translation with Generative Adversarial
+   Networks <https://github.com/zsdonghao/Unsup-Im2Im>`__ by
+   `zsdonghao <https://github.com/zsdonghao/Unsup-Im2Im>`__.
+-  `Improved
+   CycleGAN <https://github.com/luoxier/CycleGAN_Tensorlayer>`__ with
+   resize-convolution by
+   `luoxier <https://github.com/luoxier/CycleGAN_Tensorlayer>`__
+-  `Super Resolution GAN <https://arxiv.org/abs/1609.04802>`__ by
+   `zsdonghao <https://github.com/zsdonghao/SRGAN>`__.
+-  `DAGAN: Fast Compressed Sensing MRI
+   Reconstruction <https://github.com/nebulaV/DAGAN>`__ by
+   `nebulaV <https://github.com/nebulaV/DAGAN>`__.
+
+Reinforcement Learning
+----------------------
+
+-  Policy Gradient / Network (Atari Ping Pong), see
+   `tutorial\_atari\_pong.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py>`__.
+-  Deep Q-Network (Frozen lake), see
+   `tutorial\_frozenlake\_dqn.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_dqn.py>`__.
+-  Q-Table learning algorithm (Frozen lake), see
+   `tutorial\_frozenlake\_q\_table.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_q_table.py>`__.
+-  Asynchronous Policy Gradient using TensorDB (Atari Ping Pong) by
+   `nebulaV <https://github.com/akaraspt/tl_paper>`__.
+-  AC for discrete action space (Cartpole), see
+   `tutorial\_cartpole\_ac.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cartpole_ac.py>`__.
+-  A3C for continuous action space (Bipedal Walker), see
+   `tutorial\_bipedalwalker\_a3c\*.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_bipedalwalker_a3c_continuous_action.py>`__.
+-  `DAGGER <https://www.cs.cmu.edu/%7Esross1/publications/Ross-AIStats11-NoRegret.pdf>`__
+   for (`Gym Torcs <https://github.com/ugo-nama-kun/gym_torcs>`__) by
+   `zsdonghao <https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs>`__.
+-  `TRPO <https://arxiv.org/abs/1502.05477>`__ for continuous and
+   discrete action space by
+   `jjkke88 <https://github.com/jjkke88/RL_toolbox>`__.
+
+Miscellaneous
+-------------
+
+-  Distributed Training.
+   `mnist <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_distributed.py>`__
+   and
+   `imagenet <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_imagenet_inceptionV3_distributed.py>`__
+   by `jorgemf <https://github.com/jorgemf>`__.
+-  Merge TF-Slim into TensorLayer.
+   `tutorial\_inceptionV3\_tfslim.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py>`__.
+-  Merge Keras into TensorLayer.
+   `tutorial\_keras.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py>`__.
+-  Data augmentation with TFRecord. Effective way to load and
+   pre-process data, see
+   `tutorial\_tfrecord\*.py <https://github.com/zsdonghao/tensorlayer/tree/master/example>`__
+   and
+   `tutorial\_cifar10\_tfrecord.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py>`__.
+-  Data augmentation with TensorLayer, see
+   `tutorial\_image\_preprocess.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_image_preprocess.py>`__.
+-  TensorDB by `fangde <https://github.com/fangde>`__ see
+   `here <https://github.com/akaraspt/tl_paper>`__.
+-  A simple web service -
+   `TensorFlask <https://github.com/JoelKronander/TensorFlask>`__ by
+   `JoelKronander <https://github.com/JoelKronander>`__.
+-  Float 16 half-precision model, see
+   `tutorial\_mnist\_float16.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py>`__
+
+Notes
+-----
+
+TensorLayer provides two set of Convolutional layer APIs, see
+`(Advanced) <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-pro>`__
+and
+`(Basic) <http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-simplified>`__
+on readthedocs website.
+
+Features
+========
+
+Design Philosophy
+-----------------
+
+As TensorFlow users, we have been looking for a library that can serve
+for various development phases. This library is easy for beginners by
+providing rich neural network implementations, examples and tutorials.
+Later, its APIs shall naturally allow users to leverage the powerful
+features of TensorFlow, exhibiting best performance in addressing
+real-world problems. In the end, the extra abstraction shall not
+compromise TensorFlow performance, and thus suit for production
+deployment. TensorLayer is a novel library that aims to satisfy these
+requirements. It has three key features:
+
+-  *Simplicity* : TensorLayer lifts the low-level dataflow abstraction
+   of TensorFlow to **high-level** layers. It also provides users with
+   massive examples and tutorials to minimize learning barrier.
+-  *Flexibility* : TensorLayer APIs are transparent: it does not mask
+   TensorFlow from users; but leaving massive hooks that support diverse
+   **low-level tuning**.
+-  *Zero-cost Abstraction* : TensorLayer is able to achieve the **full
+   performance** of TensorFlow.
+
+Negligible Overhead
+-------------------
+
+TensorLayer has negligible performance overhead. We benchmark classic
+deep learning models using TensorLayer and native TensorFlow on a Titan
+X Pascal GPU. Here are the training speeds of respective tasks:
+
++---------------+-----------------+-----------------+-----------------+
+|               | 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   |
++---------------+-----------------+-----------------+-----------------+
+
+Compared with Keras and TFLearn
+-------------------------------
+
+Similar to TensorLayer, Keras and TFLearn are also popular TensorFlow
+wrapper libraries. These libraries are comfortable to start with. They
+provide high-level abstractions; but mask the underlying engine from
+users. It is thus hard to customize model behaviors and touch the
+essential features of TensorFlow. Without compromise in simplicity,
+TensorLayer APIs are generally more flexible and transparent. Users
+often find it easy to start with the examples and tutorials of
+TensorLayer, and then dive into the TensorFlow low-level APIs only if
+need. TensorLayer does not create library lock-in. Users can easily
+import models from Keras, TFSlim and TFLearn into a TensorLayer
+environment.
+
+Documentation
+=============
+
+The documentation
+`[Online] <http://tensorlayer.readthedocs.io/en/latest/>`__
+`[PDF] <https://media.readthedocs.org/pdf/tensorlayer/latest/tensorlayer.pdf>`__
+`[Epub] <http://readthedocs.org/projects/tensorlayer/downloads/epub/latest/>`__
+`[HTML] <http://readthedocs.org/projects/tensorlayer/downloads/htmlzip/latest/>`__
+describes the usages of TensorLayer APIs. It is also a self-contained
+document that walks through different types of deep neural networks,
+reinforcement learning and their applications in Natural Language
+Processing (NLP) problems.
+
+We have included the corresponding modularized implementations of Google
+TensorFlow Deep Learning tutorial, so you can read the TensorFlow
+tutorial
+`[en] <https://www.tensorflow.org/versions/master/tutorials/index.html>`__
+`[cn] <http://wiki.jikexueyuan.com/project/tensorflow-zh/>`__ along with
+our document. `Chinese
+documentation <http://tensorlayercn.readthedocs.io/zh/latest/>`__ is
+also available.
+
+.. raw:: html
+
+   <!---
+   # Your First Program
+
+   The first program trains a multi-layer perception network to solve the MNIST problem. We use the well-known  [scikit](http://scikit-learn.org/stable/)-style functions such as ``fit()`` and ``test()``. The program is self-explained.
+
+   ```python
+   import tensorflow as tf
+   import tensorlayer as tl
+
+   sess = tf.InteractiveSession()
+
+   # Prepare data
+   X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784))
+
+   # Define placeholder
+   x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
+   y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
+
+   # Define the neural network structure
+   network = tl.layers.InputLayer(x, name='input')
+   network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
+   network = tl.layers.DenseLayer(network, 800, tf.nn.relu, name='relu1')
+   network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
+   network = tl.layers.DenseLayer(network, 800, tf.nn.relu, name='relu2')
+   network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
+
+   # The softmax is implemented internally in tl.cost.cross_entropy(y, y_) to
+   # speed up computation, so we use identity here.
+   # see tf.nn.sparse_softmax_cross_entropy_with_logits()
+   network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output')
+                                   
+   # Define cost function and metric.
+   y = network.outputs
+   cost = tl.cost.cross_entropy(y, y_, 'cost')
+   correct_prediction = tf.equal(tf.argmax(y, 1), y_)
+   acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+   y_op = tf.argmax(tf.nn.softmax(y), 1)
+
+   # Define the optimizer
+   train_params = network.all_params
+   train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params)
+
+   # Initialize all variables in the session
+   tl.layers.initialize_global_variables(sess)
+
+   # Print network information
+   network.print_params()
+   network.print_layers()
+
+   # Train the network, we recommend to use tl.iterate.minibatches()
+   tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
+               acc=acc, batch_size=500, n_epoch=500, print_freq=5,
+               X_val=X_val, y_val=y_val, eval_train=False)
+
+   # Evaluation
+   tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)
+
+   # Save the network to .npz file
+   tl.files.save_npz(network.all_params , name='model.npz')
+
+   sess.close()
+   ```
+
+   We provide many helper functions (like `fit()` , `test()`) that is similar to Keras to facilitate your development; however, if you want to obtain a fine-grain control over the model or its training process, you can use TensorFlow’s methods like `sess.run()` in your program directly (`tutorial_mnist.py` provides more details about this). Many more DL and RL examples can be found [here](http://tensorlayer.readthedocs.io/en/latest/user/example.html).
+
+   [Tricks to use TL](https://github.com/wagamamaz/tensorlayer-tricks) is also a good introduction to use TensorLayer.
+   -->
+
+Academic and Industry Users
+===========================
+
+TensorLayer has an open and fast growing community. It has been widely
+used by researchers from Imperial College London, Carnegie Mellon
+University, Stanford University, Tsinghua University, UCLA, Linköping
+University and etc., as well as engineers from Google, Microsoft,
+Alibaba, Tencent, Penguins Innovate, ReFULE4, Bloomberg, GoodAILab and
+many others.
+
+-  🇬🇧 If you have any question, we suggest to create an issue to discuss
+   with us.
+-  🇨🇳 我们有中文讨论社区:
+   如\ `QQ群 <img/img_qq.png>`__\ 和\ `微信群 <https://github.com/shorxp/tensorlayer-chinese/blob/master/docs/wechat_group.md>`__.
+
+Contribution Guideline
+======================
+
+`Guideline in 5 lines <./CONTRIBUTING.md>`__
+
+Citation
+========
+
+If you find this project useful, we would be grateful if you cite the
+TensorLayer paper：
+
+::
+
+    @article{tensorlayer2017,
+    author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
+    journal = {ACM Multimedia},
+    title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
+    url = {http://tensorlayer.org},
+    year = {2017}
+    }
+
+License
+=======
+
+TensorLayer is released under the Apache 2.0 license.
+
+.. |TensorLayer Logo| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/tl_transparent_logo.png
+   :width: 50%
+
+.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/ca2a29ddcf7445588beff50bee5406d9
+   :target: https://app.codacy.com/app/tensorlayer/tensorlayer?utm_source=github.com&utm_medium=referral&utm_content=tensorlayer/tensorlayer&utm_campaign=badger
+.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/tensorlayer/Lobby#?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
+.. |Build Status| image:: https://travis-ci.org/tensorlayer/tensorlayer.svg?branch=master
+   :target: https://travis-ci.org/tensorlayer/tensorlayer
+.. |Documentation Status| image:: https://readthedocs.org/projects/tensorlayer/badge/?version=latest
+   :target: http://tensorlayer.readthedocs.io/en/latest/?badge=latest
+.. |Docker Pulls| image:: https://img.shields.io/docker/pulls/tensorlayer/tensorlayer.svg?maxAge=604800
+   :target: https://hub.docker.com/r/tensorlayer/tensorlayer/
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 000000000..cd2220705
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+numpy
+scipy
+scikit-image
+matplotlib
diff --git a/setup.py b/setup.py
index 49f150af1..e1ef6b84b 100755
--- a/setup.py
+++ b/setup.py
@@ -1,27 +1,143 @@
-from setuptools import find_packages, setup
+#!/usr/bin/env python
+import os
+import codecs
 
-install_requires = [
-    'numpy',
-    # 'tensorflow', # user install it
-    'scipy',
-    'scikit-image',
-    'matplotlib',
-]
+try:
+    from setuptools import (
+        setup,
+        find_packages
+    )
+
+except ImportError:
+    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__
+)
+
+
+# =================== 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'
+    ).read()
+
+else:
+    long_description = 'See ' + __homepage__
+
+
+# ======================= Reading Requirements files as TXT files =======================
+
+def req_file(filename):
+    with open(filename) as f:
+        content = f.readlines()
+    # you may also want to remove whitespace characters
+    # Example: `\n` at the end of each line
+    return [x.strip() for x in content]
 
 setup(
-    name="tensorlayer",
-    version="1.8.4",
-    include_package_data=True,
-    author='TensorLayer Contributors',
-    author_email='hao.dong11@imperial.ac.uk',
-    url="https://github.com/tensorlayer/tensorlayer",
-    license="apache",
+    name=__package_name__,
+
+    # Versions should comply with PEP440.  For a discussion on single-sourcing
+    # 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,
+
+    # The project's main homepage.
+    url=__repository_url__,
+    download_url=__download_url__,
+
+    # Author details
+    author=__contact_names__,
+    author_email=__contact_emails__,
+
+    # maintainer Details
+    maintainer=__contact_names__,
+    maintainer_email=__contact_emails__,
+
+    # 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
+        'Intended Audience :: Developers',
+        'Intended Audience :: Science/Research',
+        'Intended Audience :: Information Technology',
+
+        # Indicate what your project relates to
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Image Recognition',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Utilities',
+
+        # Pick your license as you wish (should match "license" above)
+        'License :: OSI Approved :: Apache Software License',
+
+        # Specify the Python versions you support here. In particular, ensure
+        # that you indicate whether you support Python 2, Python 3 or both.
+        'Programming Language :: Python :: 2',
+        'Programming Language :: Python :: 2.7',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+
+        # Additionnal Settings
+        'Environment :: Console',
+        'Natural Language :: English',
+        'Operating System :: OS Independent',
+    ],
+
+    keywords=__keywords__,
     packages=find_packages(),
-    install_requires=install_requires,
+
+    # List run-time dependencies here.  These will be installed by pip when
+    # your project is installed. For an analysis of "install_requires" vs pip's
+    # requirements files see:
+    # https://packaging.python.org/en/latest/requirements.html
+    install_requires=req_file("requirements.txt"),
+
+    # List additional groups of dependencies here (e.g. development
+    # dependencies). You can install these using the following syntax,
+    # $ pip install -e .[test]
+    extras_require={
+        'test': req_file("tests/requirements.txt")
+    },
+
+    zip_safe=True,
+    include_package_data=True,
     scripts=[
         'tl',
     ],
-    description="Reinforcement Learning and Deep Learning Library for Researcher and Engineer.",
-    keywords="deep learning, reinforcement learning, tensorflow",
     platform=['any'],
 )
diff --git a/tensorlayer/__init__.py b/tensorlayer/__init__.py
index 42a2653a7..d93a8b0b3 100644
--- a/tensorlayer/__init__.py
+++ b/tensorlayer/__init__.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
 """Deep learning and Reinforcement learning library for Researchers and Engineers"""
 from __future__ import absolute_import
 
@@ -24,7 +26,19 @@
 act = activation
 vis = visualize
 
-__version__ = "1.8.4"
+# Use the following formating: (major, minor, patch, prerelease)
+VERSION = (1, 8, 4, '')
+__version__ = '.'.join(map(str, VERSION[:3])) + "".join(VERSION[3:])
+
+__package_name__ = 'tensorlayer'
+__contact_names__ = 'TensorLayer Contributors'
+__contact_emails__ = 'hao.dong11@imperial.ac.uk'
+__homepage__ = 'http://tensorlayer.readthedocs.io/en/latest/'
+__repository_url__ = 'https://github.com/tensorlayer/tensorlayer'
+__download_url__ = 'https://github.com/tensorlayer/tensorlayer'
+__description__ = 'Reinforcement Learning and Deep Learning Library for Researcher and Engineer.'
+__license__ = 'apache'
+__keywords__ = 'deep learning, machine learning, computer vision, nlp, supervised learning, unsupervised learning, reinforcement learning, tensorflow'
 
 global_flag = {}
 global_dict = {}