Tensorpack is a neural network training interface based on TensorFlow.
[](http://tensorpack.readthedocs.io)
[](https://gitter.im/tensorpack/users)
[](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)
+ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo)
+ [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 3.3+.
+ Python bindings for OpenCV. (Optional, but required by a lot of features)
+ TensorFlow ≥ 1.5, < 2
* TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library
* TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed)
```
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}
}
```
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"description": "\n\nTensorpack is a neural network training interface based on TensorFlow.\n\n[](http://tensorpack.readthedocs.io)\n[](https://gitter.im/tensorpack/users)\n[](http://models.tensorpack.com)\n## Features:\n\nIt's Yet Another TF high-level API, with __speed__, and __flexibility__ built together.\n\n1. Focus on __training speed__.\n\t+ Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.\n\t On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.\n\t\tYour training can probably gets faster if written with Tensorpack.\n\n\t+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.\n It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).\n\n\t+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for\n some benchmark scripts.\n\n2. Focus on __large datasets__.\n\t+ [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).\n Symbolic programming often makes data processing harder.\n\t Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.\n\n3. It's not a model wrapper.\n\t+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.\n\t But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....\n\nSee [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.\n\n## Examples:\n\nWe refuse toy examples.\nInstead of showing tiny CNNs trained on MNIST/Cifar10,\nwe provide training scripts that reproduce well-known papers.\n\nWe refuse low-quality implementations.\nUnlike most open source repos which only __implement__ papers,\n[Tensorpack examples](examples) faithfully __reproduce__ papers,\ndemonstrating its __flexibility__ for actual research.\n\n### Vision:\n+ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet\n+ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN)\n+ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo)\n+ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN\n+ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)\n+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)\n+ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)\n+ [Visualize CNN saliency maps](examples/Saliency)\n+ [Similarity learning on MNIST](examples/SimilarityLearning)\n\n### Reinforcement Learning:\n+ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.\n+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)\n\n### Speech / NLP:\n+ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)\n+ [char-rnn for fun](examples/Char-RNN)\n+ [LSTM language model on PennTreebank](examples/PennTreebank)\n\n## Install:\n\nDependencies:\n\n+ Python 3.3+.\n+ Python bindings for OpenCV. (Optional, but required by a lot of features)\n+ TensorFlow \u2265 1.5, < 2\n * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library\n * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed)\n```\npip install --upgrade git+https://github.com/tensorpack/tensorpack.git\n# or add `--user` to install to user's local directories\n```\n\nPlease note that tensorpack is not yet stable.\nIf you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.\n\n## Citing Tensorpack:\n\nIf you use Tensorpack in your research or wish to refer to the examples, please cite with:\n```\n@misc{wu2016tensorpack,\n title={Tensorpack},\n author={Wu, Yuxin and others},\n howpublished={\\url{https://github.com/tensorpack/}},\n year={2016}\n}\n```\n\n\n",
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