tensorpack 0.9.2

Neural Network Toolbox on TensorFlow

Tensorpack is a neural network training interface based on TensorFlow.

Features:

It's Yet Another TF high-level API, with speed, and flexibility built together.

1. Focus on training speed.

   • Speed comes for free with Tensorpack -- it uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack.

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

   • See tensorpack/benchmarks for some benchmark scripts.

2. Focus on large datasets.

   • You don't usually need tf.data. Symbolic programming often makes data processing harder. Tensorpack helps you efficiently process 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 and documentations to know more about these features.

Examples:

We refuse toy examples. We refuse low-quality implementations. Unlike most open source repos which only implement papers, Tensorpack examples faithfully reproduce papers, demonstrating its flexibility for actual research.

Vision:

• Train ResNet and other models on ImageNet.
• Train Mask/Faster R-CNN 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

Reinforcement Learning:

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

Speech / NLP:

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

Install:

Dependencies:

• Python 2.7 or 3.3+. Python 2.7 is supported until it retires in 2020.
• Python bindings for OpenCV. (Optional, but required by a lot of features)
• TensorFlow ≥ 1.3, < 2. (Optional, if you only want to use tensorpack.dataflow alone as a data processing library)

pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
# or add `--user` to install to user's local directories

Citing Tensorpack:

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}
}