Difference between revisions of "Neural network module"
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Revision as of 16:11, 22 August 2018
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
- Use deer_lib_gen.m to build the training database.
- Use train_one_net.m repeatedly to create a netset.
- Use netset_curate.m to eliminate low-performance networks.
- 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
