training model neural network

if reduceDataset

numUniqueLabels = numel(unique(adsTrain.Labels));

% Reduce the dataset by a factor of 20

adsTrain = splitEachLabel(adsTrain,round(numel(adsTrain.Files) / numUniqueLabels / 20));

adsValidation = splitEachLabel(adsValidation,round(numel(adsValidation.Files)/ numUniqueLabels / 20));

end

error line 2 showing statement will never execute

Answer 1

[Rik]

This sounds like an mlint warning. The source would be reduceDataset being false, which would cause the entire if block to be skipped.

[Prashant Saini]

ads = audioDatastore(fullfile('datafiles','train'), ...

'IncludeSubfolders',true, ...

'FileExtensions','.wav', ...

'LabelSource','foldernames');

commands = categorical(["backward","forward","left","right","stop"]);

isCommand = ismember(ads.Labels,commands);

isUnknown = ~ismember(ads.Labels,[commands,"_background_noise_"]);

includeFraction = 0.2;

mask = rand(numel(ads.Labels),1) < includeFraction;

isUnknown = isUnknown & mask;

ads.Labels(isUnknown) = categorical("unknown");

adsTrain = subset(ads,isCommand|isUnknown);

countEachLabel(ads)

ads = audioDatastore(fullfile('datafiles', 'validation'), ...

'IncludeSubfolders',true, ...

'FileExtensions','.wav', ...

'LabelSource','foldernames');

isCommand = ismember(ads.Labels,commands);

isUnknown = ~isCommand;

includeFraction = 0.2;

mask = rand(numel(ads.Labels),1) < includeFraction;

isUnknown = isUnknown & mask;

ads.Labels(isUnknown) = categorical("unknown");

adsValidation = subset(ads,isCommand|isUnknown);

countEachLabel(adsValidation)

reduceceDataset=false;

if reduceDataset

numUniqueLabels = numel(unique(adsTrain.Labels));

% Reduce the dataset by a factor of 20

adsTrain = splitEachLabel(adsTrain,round(numel(adsTrain.Files) / numUniqueLabels / 20));

adsValidation = splitEachLabel(adsValidation,round(numel(adsValidation.Files)/ numUniqueLabels / 20));

end

fs = 16e3;

segmentDuration = 1;

frameDuration = 0.025;

hopDuration = 0.010;

segmentSamples = round(segmentDuration*fs);

frameSamples = round(frameDuration*fs);

hopSamples = round(hopDuration*fs);

overlapSamples = frameSamples - hopSamples;

FFTLength = 512;

numBands = 50;

afe = audioFeatureExtractor( ...

'SampleRate',fs, ...

'FFTLength',FFTLength, ...

'Window',hann(frameSamples,'periodic'), ...

'OverlapLength',overlapSamples, ...

'barkSpectrum',true);

setExtractorParams(afe,"barkSpectrum","NumBands",50);

x = read(adsTrain);

numSamples = size(x,1);

numToPadFront = floor( (segmentSamples - numSamples)/2 );

numToPadBack = ceil( (segmentSamples - numSamples)/2 );

xPadded = [zeros(numToPadFront,1,'like',x);x;zeros(numToPadBack,1,'like',x)];

features = extract(afe,xPadded);

[numHops,numFeatures] = size(features);

if ~isempty(ver('parallel')) && ~reduceDataset

pool = gcp;

numPar = numpartitions(adsTrain,pool);

else

numPar = 1;

end

parfor ii = 1:numPar

subds = partition(adsTrain,numPar,ii);

XTrain = zeros(numHops,numBands,1,numel(subds.Files));

for idx = 1:numel(subds.Files)

x = read(subds);

xPadded = [zeros(floor((segmentSamplessize(x,1))/2),1);x;zeros(ceil((segmentSamples-size(x,1))/2),1)];

XTrain(:,:,:,idx) = extract(afe,xPadded);

end

XTrainC{ii} = XTrain;

end

XTrain = cat(4,XTrainC{:});

[numHops,numBands,numChannels,numSpec] = size(XTrain);

epsil = 1e-6;

XTrain = log10(XTrain + epsil);

if ~isempty(ver('parallel'))

pool = gcp;

numPar = numpartitions(adsValidation,pool);

else

numPar = 1;

end

parfor ii = 1:numPar

subds = partition(adsValidation,numPar,ii);

XValidation = zeros(numHops,numBands,1,numel(subds.Files));

for idx = 1:numel(subds.Files)

x = read(subds);

xPadded = [zeros(floor((segmentSamplessize(x,1))/2),1);x;zeros(ceil((segmentSamples-size(x,1))/2),1)];XValidation(:,:,:,idx) = extract(afe,xPadded);

end

XValidationC{ii} = XValidation;

end

XValidation = cat(4,XValidationC{:});

XValidation = log10(XValidation + epsil);

YTrain = removecats(adsTrain.Labels);

YValidation = removecats(adsValidation.Labels);

specMin = min(XTrain,[],'all');

specMax = max(XTrain,[],'all');

idx = randperm(numel(adsTrain.Files),3);

figure('Units','normalized','Position',[0.2 0.2 0.6 0.6]);

for i = 1:3

[x,fs] = audioread(adsTrain.Files{idx(i)});

subplot(2,3,i)

plot(x)

axis tight

title(string(adsTrain.Labels(idx(i))))

subplot(2,3,i+3)

spect = (XTrain(:,:,1,idx(i))');

pcolor(spect)

caxis([specMin specMax])

shading flat

sound(x,fs)

pause(2)

end

adsBkg = audioDatastore(fullfile('datafiles', 'background'));

numBkgClips = 4000;

if reduceDataset

numBkgClips =numBkgClips/20;

end

volumeRange = log10([1e-4,1]);

numBkgFiles = numel(adsBkg.Files);

numClipsPerFile =histcounts(1:numBkgClips,linspace(1,numBkgClips,numBkgFiles+1));

Xbkg = zeros(size(XTrain,1),size(XTrain,2),1,numBkgClips,'single');

bkgAll = readall(adsBkg);

ind = 1;

for count = 1:numBkgFiles

bkg = bkgAll{count};

idxStart = randi(numel(bkg)-fs,numClipsPerFile(count),1);

idxEnd = idxStart+fs-1;

gain = 10.^((volumeRange(2)-volumeRange(1))*rand(numClipsPerFile(count),1) +volumeRange(1));

for j = 1:numClipsPerFile(count)

x = bkg(idxStart(j):idxEnd(j))*gain(j);

x = max(min(x,1),-1);

Xbkg(:,:,:,ind) = extract(afe,x);

if mod(ind,1000)==0

disp("Processed " + string(ind) + " background clips out of " +string(numBkgClips))

end

ind = ind + 1;

end

end

Xbkg = log10(Xbkg + epsil);

numTrainBkg = floor(0.85*numBkgClips);

numValidationBkg = floor(0.15*numBkgClips);

XTrain(:,:,:,end+1:end+numTrainBkg) = Xbkg(:,:,:,1:numTrainBkg);

YTrain(end+1:end+numTrainBkg) = "background";

XValidation(:,:,:,end+1:end+numValidationBkg) = Xbkg(:,:,:,numTrainBkg+1:end);

YValidation(end+1:end+numValidationBkg) = "background";

figure('Units','normalized','Position',[0.2 0.2 0.5 0.5])

subplot(2,1,1)

histogram(YTrain)

title("Training Label Distribution")

subplot(2,1,2)

histogram(YValidation)

title("Validation Label Distribution")

classWeights = 1./countcats(YTrain);

classWeights = classWeights/mean(classWeights);

numClasses = numel(categories(YTrain));

timePoolSize = ceil(numHops/8);

dropoutProb = 0.2;

numF = 12;

layers = [

imageInputLayer([numHops numBands])

convolution2dLayer(3,numF,'Padding','same')

batchNormalizationLayer

reluLayer

maxPooling2dLayer(3,'Stride',2,'Padding','same')

convolution2dLayer(3,2*numF,'Padding','same')

batchNormalizationLayer

reluLayer

maxPooling2dLayer(3,'Stride',2,'Padding','same')

convolution2dLayer(3,4*numF,'Padding','same')

batchNormalizationLayer

reluLayer

maxPooling2dLayer(3,'Stride',2,'Padding','same')

convolution2dLayer(3,4*numF,'Padding','same')

batchNormalizationLayer

reluLayer

convolution2dLayer(3,4*numF,'Padding','same')

batchNormalizationLayer

reluLayer

maxPooling2dLayer([timePoolSize,1])

dropoutLayer(dropoutProb)

fullyConnectedLayer(numClasses)

softmaxLayer

classificationLayer];

miniBatchSize = 128;

validationFrequency = floor(numel(YTrain)/miniBatchSize);

options = trainingOptions('adam', ...

'InitialLearnRate',3e-4, ...

'MaxEpochs',25, ...

'MiniBatchSize',miniBatchSize, ...

'Shuffle','every-epoch', ...

'Plots','training-progress', ...

'Verbose',false, ...

'ValidationData',{XValidation,YValidation}, ...

'ValidationFrequency',validationFrequency, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropFactor',0.1, ...

'LearnRateDropPeriod',20);

trainedNet = trainNetwork(XTrain,YTrain,layers,options);

if reduceDataset

load('commandNet.mat','trainedNet');

end

YValPred = classify(trainedNet,XValidation);

validationError = mean(YValPred ~= YValidation);

YTrainPred = classify(trainedNet,XTrain);

trainError = mean(YTrainPred ~= YTrain);

disp("Training error: " + trainError*100 + "%")

disp("Validation error: " + validationError*100 + "%")

figure('Units','normalized','Position',[0.2 0.2 0.5 0.5]);

cm = confusionchart(YValidation,YValPred);

cm.Title = 'Confusion Matrix for Validation Data';

cm.ColumnSummary = 'column-normalized';

cm.RowSummary = 'row-normalized';

this is complete code could you help me where is the mistake i'm doing

current error showing:

Error using wheel (line 87)

An UndefinedFunction error was thrown on the workers for 'segmentSamplessize'. This might be because the file containing

'segmentSamplessize' is not accessible on the workers. Use addAttachedFiles(pool, files) to specify the required files to

be attached. For more information, see the documentation for 'parallel.Pool/addAttachedFiles'.

Caused by:

Undefined function 'segmentSamplessize' for input arguments of type 'double'.