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

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Mibang
Mibang 2023-12-12
评论： Mibang 2023-12-13
Ran in:
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
  4 个评论
显示 2更早的评论
Walter Roberson
Walter Roberson 2023-12-13
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

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