Keras implementation for ICML-2016 paper:
- Junyuan Xie, Ross Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. ICML 2016.
- Install Keras>=2.0.9, scikit-learn
pip install keras scikit-learn
- Clone the code to local.
git clone https://github.com/XifengGuo/DEC-keras.git DEC
cd DEC
- Prepare datasets.
Download STL:
cd data/stl
bash get_data.sh
cd ../..
MNIST and Fashion-MNIST (FMNIST) can be downloaded automatically when you run the code.
Reuters and USPS: If you cannot find these datasets yourself, you can download them from:
https://pan.baidu.com/s/1hsMQ8Tm (password: 4ss4) for Reuters, and
https://pan.baidu.com/s/1skRg9Dr (password: sc58) for USPS
-
Run experiment on MNIST.
python DEC.py --dataset mnist
or (if there's pretrained autoencoder weights)
The DEC model will be saved to "results/DEC_model_final.h5". -
Other usages.
Use python DEC.py -h for help.
python run_exp.py
Table 1. Mean performance over 10 trials. See results.csv for detailed results for each trial.
| kmeans | AE+kmeans | DEC | paper | ||
|---|---|---|---|---|---|
| mnist | acc | 53 | 88 | 91 | 84 |
| nmi | 50 | 81 | 87 | -- | |
| fmnist | acc | 47 | 61 | 62 | -- |
| nmi | 51 | 64 | 65 | -- | |
| usps | acc | 67 | 71 | 76 | -- |
| nmi | 63 | 68 | 79 | -- | |
| stl | acc | 70 | 79 | 86 | -- |
| nmi | 71 | 72 | 82 | -- | |
| reuters | acc | 52 | 76 | 78 | 72 |
| nmi | 31 | 52 | 57 | -- |
Original code (Caffe): https://github.com/piiswrong/dec
MXNet implementation: https://github.com/dmlc/mxnet/blob/master/example/dec/dec.py
Keras implementation without pretraining code: https://github.com/fferroni/DEC-Keras