Neural Network Toolbox on TensorFlow
Neural Network Toolbox on TensorFlow.
See some examples to learn about the framework:
- Multi-GPU training of ResNet on ImageNet
- Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
- DoReFa-Net: train binary / low-bitwidth CNN on ImageNet
- Fully-convolutional Network for Holistically-Nested Edge Detection(HED)
- Spatial Transformer Networks on MNIST addition
- Visualize CNN saliency maps
- Similarity learning on MNIST
- 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.
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.
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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|tensorpack-0.4.0-py2.py3-none-any.whl (215.3 kB) Copy SHA256 Checksum SHA256||3.6||Wheel||Aug 10, 2017|
|tensorpack-0.4.0.tar.gz (152.6 kB) Copy SHA256 Checksum SHA256||–||Source||Aug 10, 2017|