Neural network module

DEERNet is a collection of functions that make and use deep neural networks for processing DEER data. The approach is described in detail in https://dx.doi.org/10.1126/sciadv.aat5218

Using DEERNet

Simply feed your DEER data to deernet.m or download DeerAnalysis2018, which is easy to use and has DEERNet integrated.

Training your own networks

Training your own DEERNets would require powerful hardware - IK's workstation has 32 Xeon cores, 1 TB of RAM, and three NVidia Titan V cards.

1. Go into experiments/deernet and edit library_params.m file.
2. Delete all networks from ./background and ./distances directories
3. Edit training_script.m file to match your hardware configuration.
4. Launch training_script.m file and hope that your computer holds up.

Functions

deer_lib_gen.m

Generates a library of simulated DEER data for use in neural network training and validation.

deer_resample.m

Resamples a DEER trace to fit the number of digitsation points expected by the neural network.

deernet.m

Uses a curated ensemble of neural networks to extract the distance distribution from DEER data.

deernet_wrapper.m

Compilable DEERNet wrapper for use outside Matlab and Spinach.

descramble.m

Weight matrix descrambling using Tikhonov smoothness criterion.

dist_range.m

Distance range estimation for a given time grid.

elexsys2deernet.m

Prepares standard Bruker Elexsys datasets for input into the deernet.m function.

left_diag.m

Weight matrix descrambling using maximum diagonality criterion.

process_using.m

Runs DEER data processing using a specified neural network file.

train_one_net.m

Trains a single neural network using supplied parameters.

Version 2.5, authors: Ilya Kuprov, Steve Worswick, Jake Amey, Jake Keeley, Tajwar Choudhury