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DeepC4.m
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DeepC4.m
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%% Initialize
clear, clc, close
cd '/Users/joshuadimasaka/Desktop/PhD/GitHub/DeepC4'
%% Load Full Country Data
[ mask, maskR,...
label2rasterID, sub_label2rasterID,...
s1vv, s1vh, rgb, red1, red2, red3, red4, swir1, swir2, nir,...
dynProb, dynLabel, btype_label, label_height, bldgftprnt,...
Q,data...
] = loadCountryData();
%% Run [1] or Load [2] Training Data (30 sectors)
optloadTrainData = 2;
[ X_batch, tau_batch, tauH_batch, tauW_batch, ...
btype_label, label_height, ind_batch, nelem] = ...
loadTrainData(optloadTrainData, ...
mask, label2rasterID, sub_label2rasterID,...
s1vv, s1vh, rgb, red1, red2, red3, red4, swir1, swir2, nir,...
dynProb, dynLabel, btype_label, label_height, bldgftprnt,...
Q,data);
%% Deep Representation Learning
% Learning Parameters
learnRate = 1e-3;
numEpochs = 400;
gradDecay = 0.8;
sqGradDecay = 0.95;
% removed upon inspection to see if, at the sector level, learning exists
select_iter = [2:12 14:15 18:21 24 26 28];
nBatch = length(select_iter);
% Trailing Variables
trailingAvgE = [];
trailingAvgSqE = [];
trailingAvgD = [];
trailingAvgSqD = [];
gradientsE_prev = [];
gradientsD_prev = [];
% Enable Monitor Window
monitor = trainingProgressMonitor;
monitor.Metrics = [ "ReconstructionLoss", ...
"PredictionLoss", ...
"IterationTPpropR", ...
"IterationTPpropH", ...
"IterationTPpropW",...
"IterationTPpropR2", ...
"IterationTPpropH2", ...
"IterationTPpropW2"];
monitor.XLabel = "Iteration";
groupSubPlot(monitor,"ReconstructionLoss","ReconstructionLoss");
groupSubPlot(monitor,"PredictionLoss","PredictionLoss");
groupSubPlot(monitor,"IterationTPpropR","IterationTPpropR");
groupSubPlot(monitor,"IterationTPpropH","IterationTPpropH");
groupSubPlot(monitor,"IterationTPpropW","IterationTPpropW");
groupSubPlot(monitor,"IterationTPpropR2","IterationTPpropR2");
groupSubPlot(monitor,"IterationTPpropH2","IterationTPpropH2");
groupSubPlot(monitor,"IterationTPpropW2","IterationTPpropW2")
% Loop over epochs.
netE_history = cell(numEpochs,nBatch);
netD_history = cell(numEpochs,nBatch);
xTPpropR_history = zeros(numEpochs,nBatch);
xTPpropH_history = zeros(numEpochs,nBatch);
xTPpropW_history = zeros(numEpochs,nBatch);
xTPpropR2_history = zeros(numEpochs,nBatch);
xTPpropH2_history = zeros(numEpochs,nBatch);
xTPpropW2_history = zeros(numEpochs,nBatch);
ReconstructionLoss_history = zeros(numEpochs,nBatch);
PredictionLoss_history = zeros(numEpochs,nBatch);
% Train
epoch = 0; iter = 0; xIter = 0;
[netE,netD] = createAE();
while epoch < numEpochs && ~monitor.Stop
epoch = epoch + 1
for j = 1:length(select_iter) %1:nBatch
iter = select_iter(j)
% Evaluate loss and gradients.
[ lossP,lossR,...
xTPpropR,xTPpropH,xTPpropW,...
xTPpropR2,xTPpropH2,xTPpropW2,...
gradientsE,gradientsD] = ...
dlfeval(@modelLoss,...
netE,netD,...
dlarray(X_batch{iter}, 'BC'), ...
tau_batch{iter},...
tauH_batch{iter},...
tauW_batch{iter},...
btype_label,...
label_height,...
ind_batch{iter}, ...
gradientsE_prev, gradientsD_prev, ...
true);
gradientsE_prev = gradientsE;
gradientsD_prev = gradientsD;
xTPpropR_history(epoch,j) = xTPpropR;
xTPpropH_history(epoch,j) = xTPpropH;
xTPpropW_history(epoch,j) = xTPpropW;
xTPpropR2_history(epoch,j) = xTPpropR2;
xTPpropH2_history(epoch,j) = xTPpropH2;
xTPpropW2_history(epoch,j) = xTPpropW2;
ReconstructionLoss_history(epoch,j) = lossR;
PredictionLoss_history(epoch,j) = lossP;
% Update learnable parameters.
[netE,trailingAvgE,trailingAvgSqE] = adamupdate(netE, ...
gradientsE,trailingAvgE,trailingAvgSqE,...
(epoch-1).*nBatch+j,learnRate,gradDecay,sqGradDecay);
netE_history{epoch,j} = netE;
[netD, trailingAvgD, trailingAvgSqD] = adamupdate(netD, ...
gradientsD,trailingAvgD,trailingAvgSqD,...
(epoch-1).*nBatch+j,learnRate,gradDecay,sqGradDecay);
netD_history{epoch,j} = netD;
recordMetrics(monitor, ...
(epoch-1).*nBatch+j, ...
ReconstructionLoss=lossR, ...
PredictionLoss=lossP, ...
IterationTPpropR=xTPpropR, ...
IterationTPpropH=xTPpropH, ...
IterationTPpropW=xTPpropW, ...
IterationTPpropR2=xTPpropR2, ...
IterationTPpropH2=xTPpropH2, ...
IterationTPpropW2=xTPpropW2);
end
end
% global
% DeepC4 - MinCostFlow
save("output/20241111_DeepC4/global/outputTrainedModels.mat",...
"netE_history","netD_history",...
"xTPpropR_history","xTPpropH_history","xTPpropW_history",...
"xTPpropR2_history","xTPpropH2_history","xTPpropW2_history",...
"ReconstructionLoss_history","PredictionLoss_history")