Hi Bhavick,
You may follow the steps below to prepare your data as input for the LSTM algorithm,
- Load the dataset from the “vtl.xlsx” file.
- Review the dataset and ensure it contains columns for time, voltage and class labels.
- Convert your data to a cell array for input to the network.
- Split the data.
- Create an LSTM architecture.
- Set Training options.
- Train the Network.
- Evaluate the Model.
To begin, please find the code for inputting the data into the LSTM provided below:"
data = readtable('vtl.xlsx');
% Extract voltage and binary response
voltageData = data.voltage;
labels = categorical(data.binaryresponse);
classNames = categories(labels);
% Convert to cell array for input
inputSequences = num2cell(voltageData, 2);
% Split data into training and validation sets
numObservations = numel(inputSequences);
idx = randperm(numObservations);
numTrain = floor(0.8 * numObservations);
% Get the train and validation inputs
trainInputs = inputSequences(idx(1:numTrain));
trainLabels = labels(idx(1:numTrain));
valInputs = inputSequences(idx(numTrain+1:end));
valLabels = labels(idx(numTrain+1:end));
% Define LSTM network architecture
layers = [
sequenceInputLayer(1)
lstmLayer(5, 'OutputMode', 'last')
fullyConnectedLayer(2)
softmaxLayer];
% Set training options
options = trainingOptions('adam', ...
'MaxEpochs', 10, ...
'GradientThreshold', 1, ...
'InitialLearnRate', 0.001, ...
'ValidationData', {valInputs, valLabels}, ...
'Plots', 'training-progress', ...
'Verbose', false);
% Train your network using “trainNetwork” and classify the data using “classify” function
You can refer to the resources below for more information:
