Metadata-Version: 1.0
Name: tensorpack
Version: 0.1.7
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|
        
        Tutorials are not finished. 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>`__
        -  `InceptionV3 on ImageNet <examples/Inception/inceptionv3.py>`__
        -  `Fully-convolutional Network for Holistically-Nested Edge
           Detection(HED) <examples/HED>`__
        -  `Spatial Transformer Networks on MNIST
           addition <examples/SpatialTransformer>`__
        -  `Visualize Saliency Maps by Guided ReLU <examples/Saliency>`__
        -  `Similarity Learning on MNIST <examples/SimilarityLearning>`__
        
        Reinforcement Learning:
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        -  `Deep Q-Network(DQN) variants on Atari
           games <examples/DeepQNetwork>`__
        -  `Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI
           Gym <examples/A3C-Gym>`__
        
        Unsupervised Learning:
        ~~~~~~~~~~~~~~~~~~~~~~
        
        -  `Generative Adversarial Network(GAN) variants <examples/GAN>`__,
           including DCGAN, InfoGAN, Conditional GAN, WGAN, Image to Image.
        
        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:
        ---------
        
        Describe your training task with three components:
        
        1. **DataFlow**. process data in Python, with ease and speed.
        
           -  Allows you to process data in Python without blocking the
              training, by multiprocess prefetch & TF Queue prefetch.
           -  All data producer has a unified interface, you can compose and
              reuse them to perform complex preprocessing.
        
        2. **Callbacks**, like ``tf.train.SessionRunHook``, plugins, or
           extensions. Write a callback to implement everything you want to do
           apart from the training iterations, such as:
        
           -  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
        
        3. **Model**, or graph. ``models/`` has some scoped abstraction of
           common models, but you can just use symbolic functions in tensorflow
           or slim/tflearn/tensorlayer/etc. ``LinearWrap`` and ``argscope``
           simplify large models (e.g. `vgg
           example <https://github.com/ppwwyyxx/tensorpack/blob/master/examples/load-vgg16.py>`__).
        
        With the above components defined, tensorpack trainer runs the training
        iterations for you. Even on a small CNN example, the training runs `2x
        faster <https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6>`__
        than the equivalent Keras code.
        
        Multi-GPU training is off-the-shelf by simply switching the trainer. You
        can also define your own trainer for non-standard training (e.g. GAN).
        
        Install:
        --------
        
        Dependencies:
        
        -  Python 2 or 3
        -  TensorFlow >= 1.0.0
        -  Python bindings for OpenCV
        
           ::
        
               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
