Awesome Tensorlayer - A curated list of dedicated resources
You have just found TensorLayer! High performance DL and RL library for industry and academic.
Contribute
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
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DCGAN trained on the CelebA dataset based on the paper by [A. Radford et al, 2015].
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CycleGAN improved with resize-convolution based on the paper by [J. Zhu et al, 2017].
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SRGAN - A Super Resolution GAN based on the paper by [C. Ledig et al, 2016].
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DAGAN: Fast Compressed Sensing MRI Reconstruction based on the paper by [G. Yang et al, 2017].
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GAN-CLS for Text to Image Synthesis based on the paper by [S. Reed et al, 2016]
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Unsupervised Image-to-Image Translation with Generative Adversarial Networks, code
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BEGAN: Boundary Equilibrium Generative Adversarial Networks based on the paper by [D. Berthelot et al, 2017].
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BiGAN Adversarial Feature Learning
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Attention CycleGAN: Unsupervised Attention-guided Image-to-Image Translation
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MoCoGAN Decomposing Motion and Content for Video Generation
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, 2016
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Lifelong GAN: Continual Learning for Conditional Image Generation, 2019, ICCV
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:
@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}
}
ENJOY