examples

Tensorpack Examples

Training examples with reproducible performance.

The word "reproduce" should always mean reproduce performance. With the magic of SGD, wrong deep learning code often appears to work, especially if you try it on toy datasets. Github is full of deep learning code that "implements" but does not "reproduce" methods, and you'll not know whether the implementation is actually correct. See Unawareness of Deep Learning Mistakes.

We refuse toy examples. Instead of showing you 10 arbitrary networks trained on toy datasets with random final performance, tensorpack examples try to faithfully replicate experiments and performance in the paper, so you're confident that they are correct.

Getting Started:

These are all the toy examples in tensorpack. They are supposed to be just demos.

• An illustrative MNIST example with explanation of the framework
• Tensorpack supports any symbolic libraries. See the same MNIST example written with tf.layers, tf-slim, and with weights visualizations
• A tiny Cifar ConvNet and SVHN ConvNet
• If you've used Keras, check out Keras+Tensorpack examples
• A boilerplate file to start with, for your own tasks

Vision:

Name	Performance
Train ResNet, ShuffleNet and other models on ImageNet	reproduce paper
Train Mask/Faster R-CNN on COCO	reproduce paper
Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN	visually reproduce
DoReFa-Net: training binary / low-bitwidth CNN on ImageNet	reproduce paper
Fully-convolutional Network for Holistically-Nested Edge Detection(HED)	visually reproduce
Spatial Transformer Networks on MNIST addition	reproduce paper
Visualize CNN saliency maps	visually reproduce
Similarity learning on MNIST
Single-image super-resolution using EnhanceNet
Learn steering filters with Dynamic Filter Networks	visually reproduce
Load a pre-trained AlexNet, VGG, or Convolutional Pose Machines
Load a pre-trained FlowNet2-S, FlowNet2-C, FlowNet2

Reinforcement Learning:

Name	Performance
Deep Q-Network(DQN) variants on Atari games, including DQN, DoubleDQN, DuelingDQN.	reproduce paper
Asynchronous Advantage Actor-Critic(A3C) on Atari games	reproduce paper

Speech / NLP:

Name	Performance
LSTM-CTC for speech recognition	reproduce paper
char-rnn for fun	fun
LSTM language model on PennTreebank	reproduce reference code