Skip to main content

Neural Network Toolbox on TensorFlow

Project description


Tensorpack is a neural network training interface based on TensorFlow.

[![Build Status](](
[![Gitter chat](](
## 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]( 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](

+ See [tensorpack/benchmarks]( for
some benchmark scripts.

2. Focus on __large datasets__.
+ [You don't usually need ``](
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]( 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:


+ 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. (If you only want to use `tensorpack.dataflow` alone as a data processing library, TensorFlow is not needed)
pip install --upgrade 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:
author={Wu, Yuxin and others},

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
tensorpack-0.8.9-py2.py3-none-any.whl (254.6 kB) Copy SHA256 hash SHA256 Wheel 3.6 Sep 4, 2018
tensorpack-0.8.9.tar.gz (191.9 kB) Copy SHA256 hash SHA256 Source None Sep 4, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page