Metadata-Version: 2.1
Name: tensorpack
Version: 0.9.4
Summary: Neural Network Toolbox on TensorFlow
Home-page: https://github.com/tensorpack/tensorpack
Author: TensorPack contributors
Author-email: ppwwyyxxc@gmail.com
License: Apache
Description: ![Tensorpack](.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`](http://tensorpack.readthedocs.io/tutorial/extend/input-source.html#tensorflow-reader-cons).
            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. 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. (Optional, 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
        ```
        
        ## 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}
        }
        ```
        
Keywords: tensorflow,deep learning,neural network
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: all
