Neural Network Toolbox on TensorFlow
Project description
![Tensorpack](.github/tensorpack.png)
Tensorpack is a neural network training interface based on TensorFlow.
[![Build Status](https://travis-ci.org/tensorpack/tensorpack.svg?branch=master)](https://travis-ci.org/tensorpack/tensorpack)
[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io/en/latest/index.html)
[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
## Features:
It's Yet Another TF high-level API, with __speed__, __readability__ 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](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.
+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.
It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for
some benchmark scripts.
2. Focus on __large datasets__.
+ [You don't usually need `tf.data`](http://tensorpack.readthedocs.io/tutorial/input-source.html#tensorflow-reader-cons).
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](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.
## [Examples](examples):
We refuse toy examples.
Instead of showing you 10 arbitrary networks trained on toy datasets,
[Tensorpack examples](examples) faithfully replicate papers and care about reproducing numbers,
demonstrating its flexibility for actual research.
### Vision:
+ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet.
+ [Train Faster-RCNN / Mask-RCNN on COCO object detection](examples/FasterRCNN)
+ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
+ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
+ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
+ [Visualize CNN saliency maps](examples/Saliency)
+ [Similarity learning on MNIST](examples/SimilarityLearning)
### Reinforcement Learning:
+ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
### Speech / NLP:
+ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
+ [char-rnn for fun](examples/Char-RNN)
+ [LSTM language model on PennTreebank](examples/PennTreebank)
## Install:
Dependencies:
+ Python 2.7 or 3.3+. Python 2.7 is supported until [it retires in 2020](https://pythonclock.org/).
+ Python bindings for OpenCV (Optional, but required by a lot of features)
+ TensorFlow >= 1.3. (If you only want to use `tensorpack.dataflow` alone as a data processing library, TensorFlow is not needed)
```
pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
# or add `--user` to avoid system-wide installation.
```
## 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}
}
```
Tensorpack is a neural network training interface based on TensorFlow.
[![Build Status](https://travis-ci.org/tensorpack/tensorpack.svg?branch=master)](https://travis-ci.org/tensorpack/tensorpack)
[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io/en/latest/index.html)
[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
## Features:
It's Yet Another TF high-level API, with __speed__, __readability__ 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](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.
+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.
It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for
some benchmark scripts.
2. Focus on __large datasets__.
+ [You don't usually need `tf.data`](http://tensorpack.readthedocs.io/tutorial/input-source.html#tensorflow-reader-cons).
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](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.
## [Examples](examples):
We refuse toy examples.
Instead of showing you 10 arbitrary networks trained on toy datasets,
[Tensorpack examples](examples) faithfully replicate papers and care about reproducing numbers,
demonstrating its flexibility for actual research.
### Vision:
+ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet.
+ [Train Faster-RCNN / Mask-RCNN on COCO object detection](examples/FasterRCNN)
+ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
+ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
+ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
+ [Visualize CNN saliency maps](examples/Saliency)
+ [Similarity learning on MNIST](examples/SimilarityLearning)
### Reinforcement Learning:
+ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
### Speech / NLP:
+ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
+ [char-rnn for fun](examples/Char-RNN)
+ [LSTM language model on PennTreebank](examples/PennTreebank)
## Install:
Dependencies:
+ Python 2.7 or 3.3+. Python 2.7 is supported until [it retires in 2020](https://pythonclock.org/).
+ Python bindings for OpenCV (Optional, but required by a lot of features)
+ TensorFlow >= 1.3. (If you only want to use `tensorpack.dataflow` alone as a data processing library, TensorFlow is not needed)
```
pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
# or add `--user` to avoid system-wide installation.
```
## 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}
}
```
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
tensorpack-0.8.9.tar.gz
(191.9 kB
view hashes)
Built Distribution
tensorpack-0.8.9-py2.py3-none-any.whl
(254.6 kB
view hashes)
Close
Hashes for tensorpack-0.8.9-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3840b9047c2d54bbfffcda24cc4baf5c7d0ff1b41261b2c3807900e7b37b0012 |
|
MD5 | eb978a384e8f8b56105f2e3677a967d4 |
|
BLAKE2b-256 | e0d08df08216e6703ddd1b9f1274a52cfa333834220b85733d36577747efa6c9 |