Neural Network Toolbox on TensorFlow
Project description
Neural Network Toolbox on TensorFlow.
See some examples to learn about the framework:
Vision:
Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
Fully-convolutional Network for Holistically-Nested Edge Detection(HED)
Reinforcement Learning:
Deep Q-Network(DQN) variants on Atari games, including DQN, DoubleDQN, DuelingDQN.
Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym
Speech / NLP:
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/….
Focus on training speed.
Speed comes for free with tensorpack. Even on a tiny CNN example, the training runs 1.6x faster than the equivalent Keras code.
Data-parallel multi-GPU training is off-the-shelf to use. It is as fast as Google’s official benchmark.
Data-parallel distributed training is off-the-shelf to use. It is as slow as Google’s official benchmark.
Focus on large datasets.
It’s painful to read/preprocess data through TF. Use DataFlow to load large datasets (e.g. ImageNet) in pure Python with autoparallelization.
DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.
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
See tutorials 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.
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