ssh-shashi / sparse-evolutionary-artificial-neural-networks

sparse neural networks before training, sparse evolutionary artificial neural networks, scalable deep learning, very high dimensional data, complex networks

sparse-evolutionary-artificial-neural-networks

• Proof of concept implementations of various sparse artificial neural network models trained with the Sparse Evolutionary Training (SET) procedure.
• The following implementations are distributed in the hope that they may be useful, but without any warranties; Their use is entirely at the user's own risk.

Implementation 1 - SET-MLP with Keras and Tensorflow (SET-MLP-Keras-Weights-Mask)

• Proof of concept implementation of Sparse Evolutionary Training (SET) for Multi Layer Perceptron (MLP) on CIFAR10 using Keras and a mask over weights.
• This implementation can be used to test SET in varying conditions, using the Keras framework versatility, e.g. various optimizers, activation layers, tensorflow.
• Also it can be easily adapted for Convolutional Neural Networks or other models which have dense layers.
• Variants of this implementation have been used to perform the experiments from Reference 1 with MLP and CNN.
• However, due the fact that the weights are stored in the standard Keras format (dense matrices), this implementation can not scale properly.
• If you would like to build an SET-MLP with over 100000 neurons, please use Implementation 2.

Implementation 2 - SET-MLP using just sparse data structures from pure Python 3 (SET-MLP-Sparse-Python-Data-Structures)

• Proof of concept implementation of Sparse Evolutionary Training (SET) for Multi Layer Perceptron (MLP) on lung dataset using Python, SciPy sparse data structures, and (optionally) Cython.
• This implementation was developed just in the last stages of the reviewing process, and we are briefly discussing about it in the "Peer Review File" which can be downloaded from Reference 1 website.
• This implementation can be used to create SET-MLP with hundred of thousands of neurons on a standard laptop. It was made starting from the vanilla fully connected MLP implementation of Ritchie Vink (https://www.ritchievink.com/) and we would like to acknowledge his work and thank him. Also, we would like to thank Thomas Hagebols for analyzing the performance of SciPy sparse matrix operations. We thank also to Amarsagar Reddy Ramapuram Matavalam from Iowa State University ([email protected]), who provided us a faster implementation of the "weightsEvolution" method, after the initial release of this code.
• If you would like to try large SET-MLP models, below are the expected running times measured on my laptop (16 GB RAM) using the original implementation of the "weightsEvolution" method. I have used exactly the model and the dataset from the file "set_mlp_sparse_data_structures.py" and I just changed the number of hidden neurons per layer:
  • 3,000 neurons/hidden layer, 12,317 neurons in total
    0.3 minutes/epoch
  • 30,000 neurons/hidden layer, 93,317 neurons in total
    3 minutes/epoch
  • 300,000 neurons/hidden layer, 903,317 neurons in total
    49 minutes/epoch
  • 600,000 neurons/hidden layer, 1,803,317 neurons in total
    112 minutes/epoch
• If you would like to try out SET-MLP with various activation functions, optimization methods and so on (in the detriment of scalability) please use Implementation 1.

Implementation 3 - SET-RBM using just sparse data structures from pure Python 3 (SET-RBM-Sparse-Python-Data-Structures)

• Proof of concept implementation of Sparse Evolutionary Training (SET) for Restricted Boltzmann Machine (RBM) on COIL20 dataset using Python, SciPy sparse data structures, and (optionally) Cython.
• This implementation can be used to create SET-RBM with hundred of thousands of neurons on a standard laptop and was developed just before the publication of Reference 1.

References

For an easy understanding of these implementations please read the following articles. Also, if you use parts of this code in your work, please cite them:

1. @article{Mocanu2018SET, author = {Mocanu, Decebal Constantin and Mocanu, Elena and Stone, Peter and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, journal = {Nature Communications}, title = {Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science}, year = {2018}, doi = {10.1038/s41467-018-04316-3}, url = {https://www.nature.com/articles/s41467-018-04316-3 }}

2. @article{Mocanu2016XBM, author={Mocanu, Decebal Constantin and Mocanu, Elena and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, title={A topological insight into restricted Boltzmann machines}, journal={Machine Learning}, year={2016}, volume={104}, number={2}, pages={243--270}, doi={10.1007/s10994-016-5570-z}, url={https://doi.org/10.1007/s10994-016-5570-z }}

3. @phdthesis{Mocanu2017PhDthesis, title = {Network computations in artificial intelligence}, author = {D.C. Mocanu}, year = {2017}, isbn = {978-90-386-4305-2}, publisher = {Eindhoven University of Technology} }

SET shows that large sparse neural networks can be built if topological sparsity is created from the design phase, before training. There are many algorithmic and implementation improvements which can be made. If you find this work interesting, please share the links to this Github page and to Reference 1. For any question, suggestion, feedback please feel free to contact me by email.

Many thanks,
Decebal