Neural Network Toolbox on TensorFlow
Project description
.. figure:: .github/tensorpack.png
:alt: Tensorpack
Tensorpack
Tensorpack is a training interface based on TensorFlow.
|Build Status| |ReadTheDoc| |Gitter chat| |model-zoo|
Features:
---------
It's Yet Another TF wrapper, but different in:
1. Focus on **training speed**.
- Speed comes for free with tensorpack -- it uses TensorFlow in the
**efficient way** with no extra overhead. On various CNNs, it runs
1.5~1.7x faster than the equivalent Keras code.
- Data-parallel multi-GPU training is off-the-shelf to use. It runs
as fast as Google's `official
benchmark <https://www.tensorflow.org/performance/benchmarks>`__.
- See
`tensorpack/benchmarks <https://github.com/tensorpack/benchmarks>`__
for the benchmark scripts.
2. 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.
3. It's not a model wrapper.
- There are too many symbolic function wrappers. Tensorpack includes
only a few common models. You can use any symbolic function
library inside tensorpack, including
tflayers/Keras/slim/tflearn/tensorlayer/....
See
`tutorials <http://tensorpack.readthedocs.io/en/latest/tutorial/index.html>`__
to know more about these features.
`Examples <examples>`__:
------------------------
Instead of showing you 10 random networks with random accuracy,
`tensorpack examples <examples>`__ faithfully replicate papers and care
about performance. And everything runs on multiple GPUs. Some
highlights:
Vision:
~~~~~~~
- `Train ResNet on ImageNet <examples/ResNet>`__
- `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
- Python bindings for OpenCV (Optional, but required by a lot of
features)
- TensorFlow >= 1.3.0 (Optional if you only want to use
``tensorpack.dataflow`` alone as a data processing library)
::
# install git, then:
pip install -U git+https://github.com/ppwwyyxx/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}
}
.. |Build Status| image:: https://travis-ci.org/ppwwyyxx/tensorpack.svg?branch=master
:target: https://travis-ci.org/ppwwyyxx/tensorpack
.. |ReadTheDoc| image:: https://readthedocs.org/projects/tensorpack/badge/?version=latest
:target: http://tensorpack.readthedocs.io/en/latest/index.html
.. |Gitter chat| image:: https://badges.gitter.im/gitterHQ/gitter.png
:target: https://gitter.im/tensorpack/users
.. |model-zoo| image:: https://img.shields.io/badge/model-zoo-brightgreen.svg
:target: http://models.tensorpack.com
:alt: Tensorpack
Tensorpack
Tensorpack is a training interface based on TensorFlow.
|Build Status| |ReadTheDoc| |Gitter chat| |model-zoo|
Features:
---------
It's Yet Another TF wrapper, but different in:
1. Focus on **training speed**.
- Speed comes for free with tensorpack -- it uses TensorFlow in the
**efficient way** with no extra overhead. On various CNNs, it runs
1.5~1.7x faster than the equivalent Keras code.
- Data-parallel multi-GPU training is off-the-shelf to use. It runs
as fast as Google's `official
benchmark <https://www.tensorflow.org/performance/benchmarks>`__.
- See
`tensorpack/benchmarks <https://github.com/tensorpack/benchmarks>`__
for the benchmark scripts.
2. 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.
3. It's not a model wrapper.
- There are too many symbolic function wrappers. Tensorpack includes
only a few common models. You can use any symbolic function
library inside tensorpack, including
tflayers/Keras/slim/tflearn/tensorlayer/....
See
`tutorials <http://tensorpack.readthedocs.io/en/latest/tutorial/index.html>`__
to know more about these features.
`Examples <examples>`__:
------------------------
Instead of showing you 10 random networks with random accuracy,
`tensorpack examples <examples>`__ faithfully replicate papers and care
about performance. And everything runs on multiple GPUs. Some
highlights:
Vision:
~~~~~~~
- `Train ResNet on ImageNet <examples/ResNet>`__
- `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
- Python bindings for OpenCV (Optional, but required by a lot of
features)
- TensorFlow >= 1.3.0 (Optional if you only want to use
``tensorpack.dataflow`` alone as a data processing library)
::
# install git, then:
pip install -U git+https://github.com/ppwwyyxx/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}
}
.. |Build Status| image:: https://travis-ci.org/ppwwyyxx/tensorpack.svg?branch=master
:target: https://travis-ci.org/ppwwyyxx/tensorpack
.. |ReadTheDoc| image:: https://readthedocs.org/projects/tensorpack/badge/?version=latest
:target: http://tensorpack.readthedocs.io/en/latest/index.html
.. |Gitter chat| image:: https://badges.gitter.im/gitterHQ/gitter.png
:target: https://gitter.im/tensorpack/users
.. |model-zoo| image:: https://img.shields.io/badge/model-zoo-brightgreen.svg
:target: http://models.tensorpack.com
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