How to fix the error: Error using trainNetwork, Input data indices must be nonnegative integers.

I am working on "wave segmentaion using deep learning" which can be found in the page: https://www.mathworks.com/help/signal/ug/waveform-segmentation-using-deep-learning.html#WaveformSegmentationUsingDeepLearningExample-15

This is a problem of sequence-to-sequnce classification. (e.g., input: (0.5, -5, 3, 10, 40, ...); prediction: (P, T, T, T, n/a,...))

I apply Tranformer encoder based on the code by Ben (Matlab staff, https://www.mathworks.com/matlabcentral/answers/2014811-is-there-any-documentation-on-how-to-build-a-transformer-encoder-from-scratch-in-matlab ), and replace LSTM layer by a Transformer encoder. The modified code by me is given at the bottom.

When I run the section of network training, I got an error message as follows, and hopefully could get some help to fix the problem.

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Error in waveExtractionTest_TransEnc (line ...)

filteredNet = trainNetwork(filteredTrainSignalss,trainLabels,net,options);

Caused by:

Error using nnet.internal.cnn.layer.util.EmbeddingDAGNetworkBaseStrategy/embedData

Input data indices must be nonnegative integers.

--------------------------------------------------------------------------------------------------------------------------------------------------------------------

%% Download the data
dataURL = 'https://www.mathworks.com/supportfiles/SPT/data/QTDatabaseECGData1.zip';
dirQT = pwd;
datasetFolder = fullfile(dirQT,'QTDataset');
zipFile = fullfile(dirQT,'QTDatabaseECGData.zip');
if ~exist(datasetFolder,'dir')
     websave(zipFile,dataURL);
     unzip(zipFile,dirQT);
end

%%
sds = signalDatastore(datasetFolder,'SignalVariableNames',["ecgSignal","signalRegionLabels"])

sds = 
  signalDatastore with properties:

                       Files:{
                             '/users/mss.system.g87Cwy/QTDataset/ecg1.mat';
                             '/users/mss.system.g87Cwy/QTDataset/ecg10.mat';
                             '/users/mss.system.g87Cwy/QTDataset/ecg100.mat'
                              ... and 207 more
                             }
                     Folders: {'/users/mss.system.g87Cwy/QTDataset'}
    AlternateFileSystemRoots: [0×0 string]
                    ReadSize: 1
         SignalVariableNames: ["ecgSignal"    "signalRegionLabels"]
       ReadOutputOrientation: "column"

%%
rng default
[trainIdx,~,testIdx] = dividerand(numel(sds.Files),0.8,0,0.2);
trainDs = subset(sds,trainIdx);
testDs = subset(sds,testIdx);

%%
trainDs = transform(trainDs, @getmask);
testDs = transform(testDs, @getmask);

%%
trainDs = transform(trainDs,@resizeData);
testDs = transform(testDs,@resizeData);

%%
% Bandpass filter design
hFilt = designfilt('bandpassiir', 'StopbandFrequency1',0.4215,'PassbandFrequency1', 0.5, ...
    'PassbandFrequency2',40,'StopbandFrequency2',53.345,...
    'StopbandAttenuation1',60,'PassbandRipple',0.1,'StopbandAttenuation2',60,...
    'SampleRate',250,'DesignMethod','ellip');

% Create tall arrays from the transformed datastores and filter the signals
tallTrainSet = tall(trainDs);

Starting parallel pool (parpool) using the 'Processes' profile ...
Parallel pool using the 'Processes' profile is shutting down.

Error using tall
Could not create a mapreduce execution environment from the default parallel cluster.

Caused by:
Error using gcp
Parallel pool failed to start with the following error. For more detailed information, validate the profile 'Processes' in the Cluster Profile Manager.
Error using parallel.internal.pool.AbstractInteractiveClient>iThrowWithCause
Failed to initialize the interactive session.
Error using parallel.internal.pool.AbstractInteractiveClient>iThrowIfBadParallelJobStatus
The interactive communicating job failed with no message.

tallTestSet = tall(testDs);

filteredTrainSignals = gather(cellfun(@(x)filter(hFilt,x),tallTrainSet(:,1),'UniformOutput',false));
trainLabels = gather(tallTrainSet(:,2));
filteredTestSignals = gather(cellfun(@(x)filter(hFilt,x),tallTestSet(:,1),'UniformOutput',false));
testLabels = gather(tallTestSet(:,2));

%% Create model
% We will use 2 encoder layers.
numHeads = 1;
numKeyChannels = 20;
feedforwardHiddenSize = 100;
modelHiddenSize = 20;
% Since the values in the sequence can be 1,2, ..., 10 the "vocabulary" size is 10.
vocabSize = 100000; % the size of input sequence of one sample-training-data is 5000
inputSize = 1;
encoderLayers = [
    sequenceInputLayer(1,Name="in")                                % input
    wordEmbeddingLayer(modelHiddenSize,vocabSize,Name="embedding") % embedding
    positionEmbeddingLayer(modelHiddenSize,vocabSize)              % position embedding
    additionLayer(2,Name="embed_add")                              % add the data and position embeddings
    selfAttentionLayer(numHeads,numKeyChannels)                    % encoder block 1
    additionLayer(2,Name="attention_add")                          %
    layerNormalizationLayer(Name="attention_norm")                 %
    fullyConnectedLayer(feedforwardHiddenSize)                     %
    reluLayer                                                      %
    fullyConnectedLayer(modelHiddenSize)                           %
    additionLayer(2,Name="feedforward_add")                        %
    layerNormalizationLayer(Name="encoder1_out")                   %
    selfAttentionLayer(numHeads,numKeyChannels)                    % encoder block 2
    additionLayer(2,Name="attention2_add")                         %
    layerNormalizationLayer(Name="attention2_norm")                %
    fullyConnectedLayer(feedforwardHiddenSize)                     %
    reluLayer                                                      %
    fullyConnectedLayer(modelHiddenSize)                           %
    additionLayer(2,Name="feedforward2_add")                       %
    layerNormalizationLayer()                                      %
    % indexing1dLayer                                                %
    % fullyConnectedLayer(inputSize)
    fullyConnectedLayer(4)
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classification")
    ];                               % output head
%
net = layerGraph(encoderLayers);
net = connectLayers(net,"embed_add","attention_add/in2");
net = connectLayers(net,"embedding","embed_add/in2");
net = connectLayers(net,"attention_norm","feedforward_add/in2");
net = connectLayers(net,"encoder1_out","attention2_add/in2");
net = connectLayers(net,"attention2_norm","feedforward2_add/in2");
% net = initialize(net);
% analyze the network to see how data flows through it
analyzeNetwork(net)
%
%%
options = trainingOptions("adam", ...
    MaxEpochs = 10, ...
    MiniBatchSize = 50, ...
    Plots="training-progress", ...
    Shuffle="every-epoch", ...
    InitialLearnRate=1e-2, ...
    LearnRateDropFactor=0.9, ...
    LearnRateDropPeriod=3, ...
    LearnRateSchedule="piecewise");
%%
filteredNet = trainNetwork(filteredTrainSignals,trainLabels,net,options);
%
%
%
%
%%  You need the function below, getmask,
function outputCell = getmask(inputCell)
%GETMASK Convert region labels to a mask of labels of size equal to the
%size of the input ECG signal.
%
%   inputCell is a two-element cell array containing an ECG signal vector
%   and a table of region labels. 
%
%   outputCell is a two-element cell array containing the ECG signal vector
%   and a categorical label vector mask of the same length as the signal. 

% Copyright 2020 The MathWorks, Inc.

sig = inputCell{1};
roiTable = inputCell{2};
L = length(sig);
M = signalMask(roiTable);

% Get categorical mask and give priority to QRS regions when there is overlap
mask = catmask(M,L,'OverlapAction','prioritizeByList','PriorityList',[2 1 3]);

% Set missing values to "n/a"
mask(ismissing(mask)) = "n/a";

outputCell = {sig,mask};
end
%
%
%
%
function outputCell = resizeData(inputCell)
%RESIZEDATA Break input ECG signal and label mask into segments of length
%5000.
%
%   inputCell is a two-element cell array containing an ECG signal and a
%   label mask.
%
%   outputCell is a two-column cell array containing as many 5000-long
%   signal segments and label masks that were possible to generate from the
%   input data.

% Copyright 2019 The MathWorks, Inc.

    targetLength = 5000;
    sig = inputCell{1};
    mask = inputCell{2};
    
    % Get number of chunks
    numChunks = floor(size(sig,1)/targetLength);
    
    % Truncate signal and mask to integer number of chunks
    sig = sig(1:numChunks*targetLength);
    mask = mask(1:numChunks*targetLength);
    
    % Create a cell array containing signal chunks
    sigOut = reshape(sig,targetLength,numChunks)';
    sigOut = num2cell(sigOut,2);
    
    % Create a cell array containing mask chunks
    lblOut = reshape(mask,targetLength,numChunks)';
    lblOut = num2cell(lblOut,2);
    
    % Output a two-column cell array with all chunks
    outputCell = [sigOut, lblOut];

end

[Matt J]

You would need to provide a stand-alone modification of your code, which we could run for ourselves and reproduce the error.

[Mibang]

Thank you, Matt,

I modified and attached two funcstion that are required, i.e., "getmask" and "resizeData".

Now, you should ba able to run successfully up to the line before "training section"

Thank you.

p.s. I am using nvidia A100 GPU. If you have error for using "tall", you may want to try "MATLAB Online" where the error may not occur related with "tall".

[Walter Roberson]

It looks like the signals are completely the wrong size for the network.

At some point it tries to shape one column of a 20 by 100001 to be 20 by 50 by 5000

[Mibang]

Thank you, Walter, it is helpful. I will look into.