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dec-keras's Introduction

Deep Embedding Clustering (DEC)

Keras implementation for ICML-2016 paper:

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

Usage

  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

Other implementations

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

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