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.

Training parameters structure

All fields required in the "parameters" structure used throughout the neural network module are listed below.

Training set parameters:

  parameters.ntraces     - number of traces you wish to generate

  parameters.min_dist    - lower limit of distance distributions, 
                           Angstrom

  parameters.max_dist    - upper limit of distance distributions, 
                           Angstrom

  parameters.max_time    - DEER trace duration, seconds

  parameters.max_exch    - maximum exchange coupling, MHz
                          (NMR convention)

  parameters.min_exch    - minimum exchange coupling, MHz
                          (NMR convention)

  parameters.ndistmax    - maximum number of skewed gaussians 
                           in the distance distribution

  parameters.npoints     - number of digitisation points in the
                           DEER trace and the distance distribu-
                           tion

  parameters.noise_lvl   - RMS noise level as a fraction of the
                           modulation depth

  parameters.min_fwhm    - minimum FWHM for a skewed gaussian in
                           the distance distribution, fraction of
                           distance
 
  parameters.max_fwhm    - maximum FWHM for a skewed gaussian in
                           the distance distribution, fraction of
                           distance
 
  parameters.min_skew    - minimum shape parameter for a skewed 
                           gaussian in the distance distribution

  parameters.max_skew    - maximum shape parameter for a skewed 
                           gaussian in the distance distribution
  
  parameters.max_mdep    - minimum DEER modulation depth

  parameters.min_mdep    - maximum DEER modulation depth

  parameters.max_brate   - maximum background signal decay
                           rate, s^-1
 
  parameters.min_brate   - minimum background signal decay 
                           rate, s^-1
 
  parameters.min_bdim    - minimum background dimensionality
 
  parameters.max_bdim    - maximum background dimensionality

Network design parameters:

  parameters.layer_sizes - number of neurons per layer, expected
			   as a horizontal vector where the number
			   of elements is the number of layers
			   desired.

  parameters.lastlayer   - activation function to use in the 
			   neural network output layer. 

  parameters.method      - neural network training algorithm 
			   selection.