# tensorpack

**Repository Path**: srwpf/tensorpack

## Basic Information

- **Project Name**: tensorpack
- **Description**: A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-09-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.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)
[![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__, 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.
		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](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`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).
    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](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.

## Examples:

We refuse toy examples.
Instead of showing tiny CNNs trained on MNIST/Cifar10,
we provide training scripts that reproduce well-known papers.

We refuse low-quality implementations.
Unlike most open source repos which only __implement__ papers,
[Tensorpack examples](examples) faithfully __reproduce__ papers,
demonstrating its __flexibility__ for actual research.

### Vision:
+ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet.
+ [Train Mask/Faster R-CNN 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, < 2. (Not required 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
```

Please note that tensorpack is not yet stable.
If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.

## 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}
}
```