Metadata-Version: 1.0
Name: tensorpack
Version: 0.1.8
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](https://travis-ci.org/ppwwyyxx/tensorpack.svg?branch=master)](https://travis-ci.org/ppwwyyxx/tensorpack)
        [![badge](https://readthedocs.org/projects/pip/badge/?version=latest)](http://tensorpack.readthedocs.io/en/latest/index.html)
        
        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.
        + [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), 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 large datasets.
        	+ __DataFlow__ allows you to process large datasets such as ImageNet in Python without blocking the training.
        	+ DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.
        
        3. Focus on training speed.
        	+	Tensorpack trainer is almost always faster than `feed_dict` based wrappers.
        	  Even on a small CNN example, the training runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
        	  More improvements to come later.
        
        	+ Data-Parallel Multi-GPU training is off-the-shelf to use.
        	You can also define your own trainer for different style of training (e.g. GAN) without losing the efficiency.
        
        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
        + Python bindings for OpenCV
        ```
        pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
        # or add `--user` to avoid system-wide installation.
        ```
        
Keywords: tensorflow,deep learning,neural network
Platform: UNKNOWN
