tensorpack 0.3.0

Neural Network Toolbox on TensorFlow

Neural Network Toolbox on TensorFlow.

See some examples to learn about the framework:

Vision:

• DoReFa-Net: train binary / low-bitwidth CNN on ImageNet

• Train ResNet on ImageNet / Cifar10 / SVHN

• 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)

• Spatial Transformer Networks on MNIST addition

• Visualize CNN saliency maps

• Similarity learning on MNIST

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:

• LSTM-CTC for speech recognition

• char-rnn for fun

• LSTM language model on 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 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.

   • Data-parallel distributed training is off-the-shelf to use. It is as slow as Google’s benchmark code.

3. Focus on large datasets.

   • It’s painful to read/preprocess data from TF. Use DataFlow to load large datasets (e.g. ImageNet) in pure Python with multi-process prefetch.

   • DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.

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 (>=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.