Neural network module

New in the forthcoming version 2.2: docs being written...

Functions

deer_lib_gen.m

Generates a library of distance distributions and corresponding simulated DEER traces for use in neural network training and validation.

deer_resample.m

Resample 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 experimental data - saved in the Bruker Elexsys format - 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

Constructs a network object, generates a training library (see deer_lib_gen.m), and runs the network training - following supplied parameters.

Netset folder structure

All in-built functions assume a standardised directory structure for the ensemble. The trained neural networks must be saved as individual *.mat files, numbered 1 to n:

	1.mat
	2.mat
	  .
	  . 
	n.mat

The directory must also contain the two supporting files:

	good_nets.mat   - 1 x m cell array (where m < n). The cells 
			  contain character arrays holding the names
			  of the networks selected by the curation
			  process (See: netset_curate.m).

	netset_params.m - function containing the netset training
			  parameters, described here.

Training parameters structure

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

Training set parameters:

  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.ntraces     - number of traces you wish to generate

  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.