Keras implementation for ICML-2016 paper:

• Junyuan Xie, Ross Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. ICML 2016.

1. Install Keras v2.0, scikit-learn and git
   sudo pip install keras scikit-learn
sudo apt-get install git

2. Clone the code to local.
   git clone https://github.com/XifengGuo/DEC.git DEC

3. Prepare datasets.

    cd DEC/data/usps   
    bash ./download_usps.sh   
    cd ../reuters  
    bash ./get_data.sh   
    cd ../..
   

4. Run experiment on MNIST.
   python DEC.py mnist
   or (if there's pretrained autoencoder weights)
   python DEC.py mnist --ae_weights ae_weights/mnist_ae_weights.h5
   The DEC model will be saved to "results/DEC_model_final.h5".

5. Run experiment on USPS.
   python DEC.py usps --update_interval 30
   or
   python DEC.py usps --ae_weights ae_weights/usps_ae_weights --update_interval 30

6. Run experiment on REUTERSIDF10K.
   python DEC.py reutersidf10k --n_clusters 4 --update_interval 20
   or
   python DEC.py reutersidf10k --ae_weights ae_weights/reutersidf10k_ae_weights --n_clusters 4 --update_interval 20

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