Metadata-Version: 1.0
Name: tensorpack
Version: 0.7.1
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: tensorpack
        ==========
        
        A neural net training interface based on TensorFlow.
        
        |Build Status| |ReadTheDoc| |Gitter chat|
        
        See some `examples <examples>`__ to learn about the framework.
        Everything runs on multiple GPUs, because why not?
        
        Vision:
        ~~~~~~~
        
        -  `Train ResNet/SE-ResNet on ImageNet <examples/ResNet>`__
        -  `Train Faster-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>`__
        
        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. It's not a model
        wrapper. + There are already too many symbolic function wrappers.
        Tensorpack includes only a few common models, but you can use any other
        model wrappers within tensorpack, such as
        sonnet/Keras/slim/tflearn/tensorlayer/....
        
        2. Focus on **training speed**.
        
           -  Speed comes for free with tensorpack -- it uses TensorFlow in the
              **correct way**. On various CNNs, it runs 1.5~1.7x faster than the
              equivalent Keras code.
        
           -  Data-parallel multi-GPU/distributed training is off-the-shelf to
              use. It is as fast as Google's `official
              benchmark <https://www.tensorflow.org/performance/benchmarks>`__.
        
           -  See
              `tensorpack/benchmarks <https://github.com/tensorpack/benchmarks>`__
              for some benchmark scripts.
        
        3. 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. It also naturally works with TF Queues or
              tf.data.
        
        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
           -  Monitor GPU utilization
           -  Send error rate to your phone
        
        See
        `tutorials <http://tensorpack.readthedocs.io/en/latest/tutorial/index.html>`__
        to know more about these features.
        
        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.
        
           If you only want to use ``tensorpack.dataflow`` alone as a data
           processing library, TensorFlow is also optional.
        
        .. |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
        
Keywords: tensorflow,deep learning,neural network
Platform: UNKNOWN
