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README.md

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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/).
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- Discussion: talk to a wide community by creating your [Github Issues](https://github.com/tensorlayer/tensorlayer/issues) and joining in the [Slack Channel](https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc)
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- 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)
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<br/>
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<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc" target="\_blank">
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<div align="center">
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<img src="img/join_slack.png" width="50%" height="30%"/>
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</div>
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</a>
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<br/>
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- **You have found a bug**: Please open a [Github Issues](https://github.com/tensorlayer/tensorlayer/issues)
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- **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)
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# News
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* [10 Apr] Load and visualize MPII dataset in one line of code.
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# Installation
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TensorLayer has pre-requisites including TensorFlow, numpy, matplotlib and nltk (optional). For GPU support, CUDA and cuDNN are required.
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The simplest way to install TensorLayer is:
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TensorLayer has pre-requisites including TensorFlow, numpy, matplotlib and nltk (optional). For GPU support, CUDA and cuDNN are required.
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The simplest way to install TensorLayer is:
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```bash
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# for master version (Recommended)
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$ pip install git+https://github.com/tensorlayer/tensorlayer.git
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$ pip install git+https://github.com/tensorlayer/tensorlayer.git
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# for stable version
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# for stable version
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$ pip install tensorlayer
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```
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$ docker build -t tensorlayer:latest .
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# for GPU version
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$ docker build -t tensorlayer:latest-gpu -f Dockerfile.gpu .
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$ docker build -t tensorlayer:latest-gpu -f Dockerfile.gpu .
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```
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Please check [documentation](http://tensorlayer.readthedocs.io/en/latest/user/installation.html) for detailed instructions.
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Please check [documentation](http://tensorlayer.readthedocs.io/en/latest/user/installation.html) for detailed instructions.
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# Examples and Tutorials
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## Notes
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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.
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<!--
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<!--
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* If you get into trouble, you can start a discussion on [Slack](https://join.slack.com/t/tensorlayer/shared_invite/MjI1NjQ5NTUxOTY5LTE1MDI3MDYwNTItYzYwNmFiZmZkOA), [Gitter](https://gitter.im/tensorlayer/Lobby#?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge>),
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[Help Wanted Issues](https://waffle.io/zsdonghao/tensorlayer),
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[QQ group](https://github.com/zsdonghao/tensorlayer/blob/master/img/img_qq.png) and [Wechat group](https://github.com/shorxp/tensorlayer-chinese/blob/master/docs/wechat_group.md).
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[QQ group](https://github.com/zsdonghao/tensorlayer/blob/master/img/img_qq.png) and [Wechat group](https://github.com/shorxp/tensorlayer-chinese/blob/master/docs/wechat_group.md).
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-->
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## Compared with Keras and TFLearn
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Similar to TensorLayer, Keras and TFLearn are also popular TensorFlow wrapper libraries.
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These libraries are comfortable to start with. They provide high-level abstractions;
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but mask the underlying engine from users. It is thus hard to
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customize model behaviors and touch the essential features of TensorFlow.
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These libraries are comfortable to start with. They provide high-level abstractions;
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but mask the underlying engine from users. It is thus hard to
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customize model behaviors and touch the essential features of TensorFlow.
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Without compromise in simplicity, TensorLayer APIs are generally more flexible and transparent.
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Users often find it easy to start with the examples and tutorials of TensorLayer, and
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Users often find it easy to start with the examples and tutorials of TensorLayer, and
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then dive into the TensorFlow low-level APIs only if need.
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TensorLayer does not create library lock-in. Users can easily import models from Keras, TFSlim and TFLearn into
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a TensorLayer environment.
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# Documentation
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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.
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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.
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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.
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[Chinese documentation](http://tensorlayercn.readthedocs.io/zh/latest/) is also available.
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# speed up computation, so we use identity here.
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# see tf.nn.sparse_softmax_cross_entropy_with_logits()
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network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output')
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# Define cost function and metric.
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y = network.outputs
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cost = tl.cost.cross_entropy(y, y_, 'cost')
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# Academic and Industry Users
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TensorLayer has an open and fast growing community.
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It has been widely used by researchers from Imperial College London, Carnegie Mellon University, Stanford University,
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Tsinghua University, UCLA, Linköping University and etc.,
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TensorLayer has an open and fast growing community.
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It has been widely used by researchers from Imperial College London, Carnegie Mellon University, Stanford University,
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Tsinghua University, UCLA, Linköping University and etc.,
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as well as engineers from Google, Microsoft, Alibaba, Tencent, Penguins Innovate, ReFULE4, Bloomberg, GoodAILab and many others.
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- 🇬🇧 If you have any question, we suggest to create an issue to discuss with us.

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