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Awesome Tensorlayer - A curated list of dedicated resources

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

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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.

• MNIST Simplest Example
• MNIST Static Example
• MNIST Static Example for Reused Model
• MNIST Dynamic Example
• MNIST Dynamic Example for Seperated Models
• MNIST Static Siamese Model Example
• CIFAR10 Static Example with Data Augmentation

1.2 DatasetAPI and TFRecord Examples

• Downloading and Preprocessing PASCAL VOC with TensorLayer VOC data loader. 知乎文章
• Read and Save data in TFRecord Format.
• Read and Save time-series data in TFRecord Format.
• Convert CIFAR10 in TFRecord Format for performance optimization.
• More dataset loader can be found in tl.files.load_xxx

2. General Computer Vision

• Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
• OpenPose: Real-time multi-person keypoint detection
• InsignFace - Additive Angular Margin Loss for Deep Face Recognition
• Spatial-Transformer-Nets (STN) trained on MNIST dataset based on the paper by [M. Jaderberg et al, 2015].
• U-Net Brain Tumor Segmentation trained on BRATS 2017 dataset based on the paper by [M. Jaderberg et al, 2015] with some modifications.
• Image2Text: im2txt based on the paper by [O. Vinyals et al, 2016].
• More Computer Vision Application can be found in Adversarial Learning Section

3. Quantization Networks

See examples/quantized_net.

• Binary Networks works on mnist and cifar10.
• Ternary Network works on mnist and cifar10.
• DoReFa-Net works on mnist and cifar10.
• Quantization For Efficient Integer-Arithmetic-Only Inference works on mnist and cifar10.

4. GAN

• DCGAN trained on the CelebA dataset based on the paper by [A. Radford et al, 2015].
• CycleGAN improved with resize-convolution based on the paper by [J. Zhu et al, 2017].
• SRGAN - A Super Resolution GAN based on the paper by [C. Ledig et al, 2016].
• DAGAN: Fast Compressed Sensing MRI Reconstruction based on the paper by [G. Yang et al, 2017].
• GAN-CLS for Text to Image Synthesis based on the paper by [S. Reed et al, 2016]
• Unsupervised Image-to-Image Translation with Generative Adversarial Networks, code
• BEGAN: Boundary Equilibrium Generative Adversarial Networks based on the paper by [D. Berthelot et al, 2017].
• BiGAN Adversarial Feature Learning
• Attention CycleGAN: Unsupervised Attention-guided Image-to-Image Translation
• MoCoGAN Decomposing Motion and Content for Video Generation
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, 2016
• Lifelong GAN: Continual Learning for Conditional Image Generation, 2019, ICCV

5. Natural Language Processing

5.1 ChatBot

• Seq2Seq Chatbot in 200 lines of code for Seq2Seq.

5.2 Text Generation

• Text Generation with LSTMs - Generating Trump Speech.
• Modelling PennTreebank code1 and code2, see blog post.

5.3 Text Classification

• FastText Classifier running on the IMDB dataset based on the paper by [A. Joulin et al, 2016].

5.4 Word Embedding

• Minimalistic Implementation of Word2Vec based on the paper by [T. Mikolov et al, 2013].

5.5 Spam Detection

• Chinese Spam Detector.

6. Reinforcement Learning

• DRL Tutorial for Academic
• DRL Zoo for Industry

7. (Variational) Autoencoders

• Variational Autoencoder trained on the CelebA dataset.
• Variational Autoencoder trained on the MNIST dataset.

8. Pretrained Models

• The guideline of using pretrained models is here.

9. Data and Model Managment Tools

• Why Database?.
• Put Tasks into Database and Execute on Other Agents, see code.
• TensorDB applied on Pong Game on OpenAI Gym: Trainer File and Generator File based on the following blog post.
• TensorDB applied to classification task on MNIST dataset: Master File and Worker File.

How to cite TL in Research Papers ?

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}
}

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