Preparing input data for classification using LSTM

I am interested in classifying graphs (senquence) data to category labels. I saw that I could use LSTM however, I would like know how the primary sequence data is store for inputing into the LSTM, I also want to know how to attach know labels to each graph for purpose of training.

https://www.mathworks.com/help/deeplearning/ug/classify-sequence-data-using-lstm-networks.html

In this there is a variable / struture called waveform, how was it constructed?

Please assist

[Cris LaPierre]

The format is described at the top of the linked example.

You can also find it described at the top of this example: Sequence-to-One Regression Using Deep Learning

[Ernest Modise - Kgamane]

Hi Cris

I am looking at your response, I am trying to understand it, please see my code and input file and explain where I went wrong

label = strings(997,1);

label(1:200) = 'graphtype1';

label(201:399) = 'graphtype2';

label(400:598) = 'graphtype3';

label(599:798) = 'graphtype4';

label(799:997) = 'graphtype5';

className = categorical(label);

className2 = categories(className);

Datain = xlsread('C:\Users\ernes\OneDrive\Documents\MATLAB\LSTMdataIn.xlsx');

% Above Datain has 897 graphs each with 100 samples

% E.g for graphs Datain(1:200,:) - graphtype 1

% graphs Datain(201:399) - graphtype 2

%So my objective is to train my LSTM using the graphs to labels

numObservations = 997;

[idxTrain,idxTest] = trainingPartitions(numObservations,[0.9 0.1]);

XTrain = Datain(idxTrain,:);% in Xtrain - there are 897 graphs each with 100 values, so

% Xtrain is 897 x 100,

TTrain = className(idxTrain,:);

numHiddenUnits = 120;

numClasses = 5;

layers = [

sequenceInputLayer(100) % I am not sure about this input, because my data comes in 1 by 100 arrys of a seq

%,with 1 - 100 ms timestamps

bilstmLayer(numHiddenUnits,OutputMode="last")

fullyConnectedLayer(numClasses)

softmaxLayer]

options = trainingOptions("adam", ...

MaxEpochs=200, ...

InitialLearnRate=0.002,...

GradientThreshold=1, ...

Shuffle="never", ...

Plots="training-progress", ...

Metrics="accuracy", ...

Verbose=false);

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

Answer 1

[Cris LaPierre]

It is a mat file. This is a way of saving variables in MATLAB to a file (see save). It loads 3 variables to the Workspace

• data - a 1000x1 cell array. Each cell contains an nx3 array of signal data
• freq - 1000x1 array. This is the frequency of the corresponding observation
• labels - a 1000x1 categorical array containg the waveform label for the corresponding observation

You don't need to create a mat file. You just need to organze your data into a numObservations-by-1 cell array of sequences as the input data.

Each sequence (cell of data) is a numTimeSteps-by-numChannels numeric array, where numTimeSteps is the number of time steps of the sequence and numChannels is the number of channels of the sequence.

The label data is a numObservations-by-1 categorical vector.

You do not need to use freq for the example you are using.

[Ernest Modise - Kgamane]

Hi, are you going to be able to assist on the follow up question?

[Ernest Modise - Kgamane]

The above answer did not help me. Regards.

[Cris LaPierre]

Thank you for adding your data. That makes it easier to help.

If you separate your data into a cell array where each cell contains 3 signals of the same type (a 100x3 matrix), you can the use the example code. The challenging part is that you do not have an exact multiple of 3 of all your signals. That makes the actual code a little more complicated. Still, something like this should work.

Datain = readmatrix('LSTMdataIn.xlsx');
% orient the data to be time x sample
Datain = Datain';

% split the data into an numObservatoins x 1 cell array
% Each cell contains a 100x3 matrix. All 3 signals are of the same type
% Also create
data = {};
labels = {};
s=2;

idx = [0 200 399 598 798 997];
sig = 1:3:997;
L = {'graphtype1' 'graphtype2' 'graphtype3' 'graphtype4' 'graphtype5'};
for c = 2:length(sig)
    if sig(c)>idx(s-1) && sig(c)+2<=idx(s)
        data(end+1,:) = {Datain(:,sig(c-1):sig(c)-1)};
        labels(end+1,:) = L(s-1);
    elseif sig(c)==idx(s)
        data(end+1,:) = {Datain(:,sig(c-1):sig(c)-1)};
        labels(end+1,:) = L(s-1);
        s=s+1;
    else
        s=s+1;
        % skip cells that would contain a mix of signal types
        continue
    end
end

You can then pick up using the code from the example

numChannels = size(data{1},2);

idx = [3 4 5 12];
figure
tiledlayout(2,2)
for i = 1:4
    nexttile
    stackedplot(data{idx(i)},DisplayLabels="Channel "+string(1:numChannels))
    
    xlabel("Time Step")
    title("Class: " + string(labels(idx(i))))
end

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

classNames = 5x1 cell array
    {'graphtype1'}
    {'graphtype2'}
    {'graphtype3'}
    {'graphtype4'}
    {'graphtype5'}

numObservations = numel(data);
[idxTrain,idxTest] = trainingPartitions(numObservations,[0.9 0.1]);
XTrain = data(idxTrain);
TTrain = labels(idxTrain);

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

The only change you must make is numClasses = 5;

You must open the LSTM example locally and set that as your current folder in order to get the helper function trainingPartitions.