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

1. Use deer_lib_gen.m to build the training database.
2. Use train_one_net.m repeatedly to create a netset.
3. Use netset_curate.m to eliminate low-performance networks.
4. Use the resulting netset with deernet.m or deernet_bckg.m

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 suuplied 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 a distance distribution from primary DEER data.

elexsys2deernet.m

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

netset_curate.m

Evaluates an ensemble of neural networks and decides which ones are best.

process_using.m

Runs the DEER data processing using a specified neural network file.

train_one_net.m

Trains a single neural network using supplied parameters.

netset_params.m

Adds the training set and network design parameters for a netset to the MATLAB workspace.

Version 2.2, authors: Ilya Kuprov, Steve Worswick