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.
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.
Tensorpack trainer is almost always faster than feed_dict based wrappers. Even on a tiny CNN example, the training runs 2x faster 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.
Focus on large datasets.
DataFlow allows you to process large datasets such as ImageNet in pure Python without blocking the training.
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
Install:
Dependencies:
Python 2 or 3
TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)
Python bindings for OpenCV
pip install -U git+https://github.com/ppwwyyxx/tensorpack.git # or add `--user` to avoid system-wide installation.
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