Metadata-Version: 1.0
Name: tensorpack
Version: 0.3.0
Summary: Neural Network Toolbox on TensorFlow
Home-page: https://github.com/ppwwyyxx/tensorpack
Author: TensorPack contributors
Author-email: ppwwyyxxc@gmail.com
License: Apache
Description: tensorpack
        ==========
        
        Neural Network Toolbox on TensorFlow.
        
        |Build Status| |badge|
        
        See some `examples <examples>`__ to learn about the framework:
        
        Vision:
        ~~~~~~~
        
        -  `DoReFa-Net: train binary / low-bitwidth CNN on
           ImageNet <examples/DoReFa-Net>`__
        -  `Train ResNet on ImageNet / Cifar10 / SVHN <examples/ResNet>`__
        -  `Generative Adversarial Network(GAN) variants <examples/GAN>`__,
           including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN,
           Image to Image, CycleGAN.
        -  `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>`__
        
        The examples are not only for demonstration of the framework -- you can
        train them and reproduce the results in papers.
        
        Features:
        ---------
        
        It's Yet Another TF wrapper, but different in: 1. Not focus on models. +
        There are already too many symbolic function wrappers. Tensorpack
        includes only a few common models, and helpful tools such as
        ``LinearWrap`` to simplify large models. But you can use any other
        wrappers within tensorpack, such as
        sonnet/Keras/slim/tflearn/tensorlayer/....
        
        2. Focus on **training speed**.
        
           -  Tensorpack trainer is almost always faster than ``feed_dict``
              based wrappers. Even on a tiny CNN example, the training runs `2x
              faster <https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6>`__
              than the equivalent Keras code.
        
           -  Data-parallel multi-GPU training is off-the-shelf to use. It is as
              fast as Google's `benchmark
              code <https://github.com/tensorflow/benchmarks>`__.
        
           -  Data-parallel distributed training is off-the-shelf to use. It is
              as slow as Google's `benchmark
              code <https://github.com/tensorflow/benchmarks>`__.
        
        3. Focus on large datasets.
        
           -  It's painful to read/preprocess data from TF. Use **DataFlow** to
              load large datasets (e.g. ImageNet) in **pure Python** with
              multi-process prefetch.
           -  DataFlow has a unified interface, so you can compose and reuse
              them to perform complex preprocessing.
        
        4. Interface of extensible **Callbacks**. Write a callback to implement
           everything you want to do apart from the training iterations, and
           enable it with one line of code. Common examples include:
        
           -  Change hyperparameters during training
           -  Print some tensors of interest
           -  Run inference on a test dataset
           -  Run some operations once a while
           -  Send loss to your phone
        
        Install:
        --------
        
        Dependencies:
        
        -  Python 2 or 3
        -  TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)
        -  Python bindings for OpenCV (Optional, but required by a lot of
           features)
        
           ::
        
               pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
               # or add `--user` to avoid system-wide installation.
        
        .. |Build Status| image:: https://travis-ci.org/ppwwyyxx/tensorpack.svg?branch=master
           :target: https://travis-ci.org/ppwwyyxx/tensorpack
        .. |badge| image:: https://readthedocs.org/projects/pip/badge/?version=latest
           :target: http://tensorpack.readthedocs.io/en/latest/index.html
        
Keywords: tensorflow,deep learning,neural network
Platform: UNKNOWN
