
# 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 such deep learning code that "implements" but does not "reproduce" methods.
See [Unawareness of Deep Learning Mistakes](https://medium.com/@ppwwyyxx/unawareness-of-deep-learning-mistakes-d5b5774da0ba).

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](basics/mnist-convnet.py)
+ Tensorpack supports any symbolic libraries. See the same MNIST example written with [tf.layers](basics/mnist-tflayers.py), [tf-slim](basics/mnist-tfslim.py), and [with weights visualizations](basics/mnist-visualizations.py)
+ A tiny [Cifar ConvNet](basics/cifar-convnet.py) and [SVHN ConvNet](basics/svhn-digit-convnet.py)
+ If you've used Keras, check out [Keras+Tensorpack examples](keras)
+ [A boilerplate file to start with, for your own tasks](boilerplate.py)

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

## Reinforcement Learning:
| Name | Performance |
| ---  | --- |
| [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including <br/> DQN, DoubleDQN, DuelingDQN.  | reproduce paper |
| [Asynchronous Advantage Actor-Critic(A3C) on Atari games](A3C-Gym) | reproduce paper |

## Speech / NLP:
| Name | Performance |
| ---  | --- |
| [LSTM-CTC for speech recognition](CTC-TIMIT) | reproduce paper |
| [char-rnn for fun](Char-RNN) | fun |
| [LSTM language model on PennTreebank](PennTreebank) | reproduce reference code |
