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Awesome Tensorlayer - A curated list of dedicated resources

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You have just found TensorLayer! High performance DL and RL library for industry and academic.


Contributions welcome! Read the contribution guidelines first.

1. Basics Examples

1.1 MNIST and CIFAR10

TensorLayer can define models in two ways. Static model allows you to build model in a fluent way while dynamic model allows you to fully control the forward process. Please read this DOCS before you start.

1.2 DatasetAPI and TFRecord Examples

2. General Computer Vision

3. Quantization Networks

See examples/quantized_net.

4. GAN

5. Natural Language Processing

5.1 ChatBot

5.2 Text Generation

5.3 Text Classification

5.4 Word Embedding

5.5 Spam Detection

6. Reinforcement Learning

7. (Variational) Autoencoders

8. Pretrained Models

  • The guideline of using pretrained models is here.

9. Data and Model Managment Tools

How to cite TL in Research Papers ?

If you find this project useful, we would be grateful if you cite the TensorLayer paper

    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     = {},
    year    = {2017}


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