Neural network module

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DEERNet is a collection of functions that make and use deep neural networks for processing DEER data. The approach is described in detail in

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 a 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


Generates a library of simulated DEER data for use in neural network training and validation.
Resamples a DEER trace to fit the number of digitsation points expected by the neural network.
Uses a curated ensemble of neural networks to extract the distance distribution from DEER data.
Uses a curated ensemble of neural networks to extract the background signal from DEER data.
Prepares standard Bruker Elexsys datasets for input into the deernet.m function.
Evaluates an ensemble of neural networks and decides which ones are best.
Runs DEER data processing using a specified neural network file.
Trains a single neural network using supplied parameters.

Version 2.2, authors: Ilya Kuprov, Steve Worswick