shubhampachori12110095/dec-keras-1

Keras implementation for Deep Embedding Clustering (DEC)

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>=2.0.9, scikit-learn

pip install keras scikit-learn   

2. Clone the code to local.

git clone https://github.com/XifengGuo/DEC-keras.git DEC
cd DEC

3. 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

4. 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".

5. Other usages.

Use python DEC.py -h for help.

Results

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	--

Autoencoder model

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