Error creating a CNN-LSTM network with sequences.

Hello:

I'm writting here because I have the following issue:

I want to train a CNN-LSTM deep network in order to classify sequences into 2 labels.

I have 252 sequences of 45 seconds of 1891 channels, 80% of then (201) are used as training, 10% (25) are used in validation and the other 10% (26) are used in testing.

I created the neural network in Deep Network Designer as follows:

When I try to train the network, this error message is shown:

Error using trainnet (line 46)

Number of observations in predictors (201) and targets (1) must match. Check that the data and network are

consistent.

Te workspace generated is this:

Could somebody help me find where is the problem please?

Thank you so much.

This is the code:

rng('shuffle')

load('datos.mat');
data_ordenado = cell(252,1);
mylabels_ordenado = cell(252,1);
data_desordenado = cell(252,1);
mylabels_desordenado = cell(252,1);

for i = 1:252
    if (i<=131)
        data_ordenado{i} = cxy1(:,:,i)';
        mylabels_ordenado{i} = 'Pre_experiment';
    else
        data_ordenado{i} = cxy17(:,:,i-131)';
        mylabels_ordenado{i} = 'Post_experiment';
    end
end

idx = randperm(252);

for i = 1:252
    data_desordenado{i} = data_ordenado{idx(i)};
    mylabels_desordenado{i} = mylabels_ordenado{idx(i)};
end

labels = categorical(mylabels_desordenado);
classNames = categories(labels);

numObservations = numel(data_desordenado);
[idxTrain,idxValidation,idxTest] = trainingPartitions(numObservations,[0.8 0.1 0.1]);

XTrain = data_desordenado(idxTrain);
TTrain = labels(idxTrain);

XValidation = data_desordenado(idxValidation);
TValidation = labels(idxValidation);

XTest = data_desordenado(idxTest);
TTest = labels(idxTest);



options = trainingOptions("adam", ...
    MaxEpochs=500, ...
    InitialLearnRate=0.0005, ...
    GradientThreshold=1, ...
    ValidationData={XValidation,TValidation}, ...
    Shuffle = "every-epoch", ...
    Plots="training-progress", ...
    Metrics="accuracy", ...
    Verbose=false);

net = trainnet(XTrain,TTrain',net_1,"crossentropy",options);

scores = minibatchpredict(net,XTest);
YTest = scores2label(scores,classNames);

acc = mean(YTest == TTest)
figure
confusionchart(TTest,YTest)

Answer 1

[Alex Wu]

Number of observations in predictors (201) and targets (1) must match.

This means during training, your input data X and target data T have different number of observations.

net = trainnet(XTrain,TTrain',net_1,"crossentropy",options);

Your TTrain matrix has dimensions 201x1. However, transposing it results in dimensions of 1x201, which is interpreted as a single observation. Try removing the transpose operation here.