Tensorpack is a training interface based on TensorFlow.

It's Yet Another TF wrapper, but different in:

1. Focus on training speed.

   • Speed comes for free with tensorpack -- it uses TensorFlow in the efficient way with no extra overhead. On different CNNs, it runs 1.1~3.5x faster than the equivalent Keras code.

   • Data-parallel multi-GPU training is off-the-shelf to use. It scales as well as Google's official benchmark.

   • See tensorpack/benchmarks for the benchmark scripts.

2. Focus on large datasets.

   • It's painful to read/preprocess data through TF. Tensorpack helps you load large datasets (e.g. ImageNet) in pure Python with autoparallelization.

3. It's not a model wrapper.

   • There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models. But you can use any symbolic function library inside tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....

See tutorials to know more about these features.

Instead of showing you 10 random networks with random accuracy, tensorpack examples faithfully replicate papers and care about performance. And everything runs on multiple GPUs. Some highlights:

• Train ResNet and other models on ImageNet.
• Train Faster-RCNN / Mask-RCNN on COCO object detection
• Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
• DoReFa-Net: train binary / low-bitwidth CNN on ImageNet
• Fully-convolutional Network for Holistically-Nested Edge Detection(HED)
• Spatial Transformer Networks on MNIST addition
• Visualize CNN saliency maps
• Similarity learning on MNIST

• Deep Q-Network(DQN) variants on Atari games, including DQN, DoubleDQN, DuelingDQN.
• Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym

• LSTM-CTC for speech recognition
• char-rnn for fun
• LSTM language model on PennTreebank

Dependencies:

• Python 2.7 or 3
• Python bindings for OpenCV (Optional, but required by a lot of features)
• TensorFlow >= 1.3.0 (Optional if you only want to use tensorpack.dataflow alone as a data processing library)

# install git, then:
pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
# or add `--user` to avoid system-wide installation.

If you use Tensorpack in your research or wish to refer to the examples, please cite with:

@misc{wu2016tensorpack,
  title={Tensorpack},
  author={Wu, Yuxin and others},
  howpublished={\url{https://github.com/tensorpack/}},
  year={2016}
}