Metadata-Version: 1.0
Name: tensorpack
Version: 0.8.2
Summary: Neural Network Toolbox on TensorFlow
Home-page: https://github.com/ppwwyyxx/tensorpack
Author: TensorPack contributors
Author-email: ppwwyyxxc@gmail.com
License: Apache
Description-Content-Type: UNKNOWN
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
        
Keywords: tensorflow,deep learning,neural network
Platform: UNKNOWN
