This is an introduction of the code developed for the Deep Clustering Network (DCN). Please direct your emails to Bo Yang, yang4173@umn.edu if you have troubles running the code, or find any bugs. Here is the paper: arxiv: https://arxiv.org/pdf/1610.04794v1.pdf Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos and Mingyi Hong "Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering" ============================================== Main files run_rcv1.py : Script to reproduce our results on RCV1 dataset (Table 1). run_20News.py : Script to reproduce our results on 20Newsgroup dataset (Table 2). run_raw_mnist.py : Script to reproduce our results on raw-MNIST dataset (Table 3). run_pre_mnist.py : Script to reproduce our results on pre-processed MNIST dataset (Table 4). run_pendigits.py : Script to reproduce our results on Pendigits dataset (Table 5). multi_layer_km.py : Main file for defining the network, as well as various utility functions. -- More documentations can be found inside each of the above files. -- There are some additional python source files in the repository, which were developed for trying out various ideas. They are kept for possible future use, but are less documented (unfortunately...). ============================================== Data preparation The data file should be named like 'something.pkl.gz', i.e., it should be pickled and compressed by gzip, using python code as follow: """ with gzip.open('something.pkl.gz', 'wb') as f: cPickle.dump([train_x, train_y], f, protocol = 0) """ where train_x and train_y are numpy ndarray with shape train_x: (n_samples, n_features) train_y: (n_samples, ) The path of data files should be made available to the programs by modifying the 'path' local variable in each of the 'run_' python source files. ============================================== Dependencies Theano scikit-learn numpy scipy matplotlib Theanon deep learning tutorial code (can be download from http://deeplearning.net/tutorial/).