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train_DEERNet
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train_DEERNet
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% Let's generate a bunch of deer data for descramblement
% Load the training set parameters
parameters=library_dd();
% Specify number of traces to produce
parameters.ntraces=1e6;
parameters.training_input = 'noisy_deer';
% Set the training database name
file_name='example_set_2layer_17dec22_ker4_ang3.mat';
% Generate the training library
% [time_grid,dist_grid,dist_distr_lib,...
% deer_ffact_lib,background_lib,deer_trace_lib,...
% deer_clean_lib,exchange_lib,parameters]=deer_lib_gen(file_name,parameters);
sample = deer_lib_gen(file_name,parameters);
save(file_name, 'sample', '-v7.3')
%% train one net using parameters
train_one_net(sample.parameters, 1e6, 2040102)
% file_number=303;
% parameters = sample.parameters;
% layers=dist_net(parameters.npoints); % get layers
% train_opts=trainingOptions('adam','MaxEpochs',50,'Verbose',true,'Shuffle','never',...
% 'InitialLearnrate',0.01,'LearnRateSchedule','piecewise',...
% 'LearnRateDropPeriod',5,'LearnRateDropFactor',0.50,...
% 'MiniBatchSize',1e3,'ExecutionEnvironment','auto',...
% 'L2Regularization',1e-6,'ResetInputNormalization',false,...
% 'Plots','none','DispatchInBackground',true); % get opts
% [net,train_stats]=trainNetwork(sample.deer_noisy_lib', sample.dist_distr_lib',layers,train_opts);
% save([num2str(file_number) '.mat'],'net','train_stats','train_opts','parameters');