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 - examples are provided in examples/deernet directory.

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