ghliu / mean-field-fcdnn

Mean Field Fully-Connected DNN

Overview

Official experiment implementation of the following paper:

Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective.

The repo reproduces some of the results from the Deep Information Propagation paper. However, it is not the official release of DIP. Most APIs in meanfield.py are consistent with the mean-field-cnns repo.

Contribution is wellcomed. Plase cite the following BibTex if you find this repo helpful.

@article{Liu2019DeepLT,
  title={Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective},
  author={Liu, Guan-Horng and Theodorou, Evangelos A.},
  journal={arXiv preprint arXiv:1908.10920},
  year={2019}
}

Dependencies

• Python3
• Pytorch

Run

Phase Diagram

python3 plot-phase-diagram.py

• Result:

Trainability and Depth Scale on MNIST

python3 plot-trainability.py \
        --depth-scale-file 'depth_scale.npz' \
        --train-acc-file 'train_acc.npz'

• You can specify the argument --{depth-scale,train-acc}-file to load the pre-computed results; otherwise it will run the full experiment from scratch.

• Other arguments for the hyper-parameters (e.g. learning rate, batch size... etc) of the training process can be founded in plot-trainability.py

• Result: