Neural Network Toolbox on TensorFlow
Project description
Tensorpack is a zero-overhead training interface based on TensorFlow.
See some examples to learn about the framework. Everything runs on multiple GPUs, because why not?
Vision:
Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
Fully-convolutional Network for Holistically-Nested Edge Detection(HED)
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:
Examples are not only for demonstration of the framework – you can train them and reproduce the results in papers.
Features:
It’s Yet Another TF wrapper, but different in:
Focus on training speed.
Speed comes for free with tensorpack – it uses TensorFlow in the correct way with no extra overhead. On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.
Data-parallel multi-GPU/distributed training is off-the-shelf to use. It is as fast as Google’s official benchmark.
See tensorpack/benchmarks for some benchmark scripts.
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.
It’s not a model wrapper.
There are already too many symbolic function wrappers. Tensorpack includes only a few common models, but you can use any other wrappers within tensorpack, including sonnet/Keras/slim/tflearn/tensorlayer/….
See tutorials to know more about these features.
Install:
Dependencies:
Python 2.7 or 3
TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)
Python bindings for OpenCV (Optional, but required by a lot of features)
pip install -U git+https://github.com/ppwwyyxx/tensorpack.git # or add `--user` to avoid system-wide installation.
If you only want to use tensorpack.dataflow alone as a data processing library, TensorFlow is also optional.
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