SpectralNet

SpectralNet is a python library that performs spectral clustering with deep neural networks.

Link to the paper - SpectralNet

New PyTorch implementation

We recommend using our new (2023) well-maintained PyTorch implementation in the following link - PyTorch SpectralNet

requirements

To run SpectralNet, you'll need Python 3.x and the following python packages:

• scikit-learn
• tensorflow==1.15
• keras==2.3
• munkres
• annoy
• h5py

You will also need wget to download the Reuters dataset, which for MacOS can be installed with

brew install wget

downloading and preprocessing reuters

To run SpectralNet on the Reuters dataset, you must first download and preprocess it. This can be done by

cd path_to_spectralnet/data/reuters/; ./get_data.sh; python make_reuters.py

usage

To use SpectralNet on MNIST, Reuters, the nested 'C' dataset (as seen above), or the semi-supervised and noisy nested 'C' dataset, please run

cd path_to_spectralnet/src/applications; python run.py --gpu=gpu_num --dset=mnist|reuters|cc|cc_semisup

To use SpectralNet on a new dataset, simply pass a tuple to get_data (a function in src/core/data.py) containing four elements in the following order: (x_train, x_test, y_train, y_test). Then define the appropriate hyperparameters and call spectralnet.run(). (See example)

example script

import sys, os
# add directories in src/ to path
sys.path.insert(0, 'path_to_spectralnet/src/')

# import run_net and get_data
from spectralnet import run_net
from core.data import get_data

# define hyperparameters
params = {
    'dset': 'new_dataset',
    'val_set_fraction': ...,
    'siam_batch_size': ...,
    'n_clusters': ...,
    'affinity': ...,
    'n_nbrs': ...,
    'scale_nbrs': ...,
    'siam_k': ...,
    'siam_ne': ...,
    'spec_ne': ...,
    'siam_lr': ...,
    'spec_lr': ...,
    'siam_patience': ...,
    'spec_patience': ...,
    'siam_drop': ...,
    'spec_drop': ...,
    'batch_size': ...,
    'siam_reg': ...,
    'spec_reg': ...,
    'siam_n': ...,
    'siamese_tot_pairs': ...,
    'arch': [
        {'type': 'relu', 'size': ...},
        {'type': 'relu', 'size': ...},
        {'type': 'relu', 'size': ...},
        ],
    'use_approx': ...,
    }
    
# load dataset
x_train, x_test, y_train, y_test = load_new_dataset_data()
new_dataset_data = (x_train, x_test, y_train, y_test)

# preprocess dataset
data = get_data(params, new_dataset_data)

# run spectral net
x_spectralnet, y_spectralnet = run_net(data, params)

For more information on what each hyperparameter means, see src/applications/run.py