Load Data

Load the Japanese Vowels data set, as described in [1] and [2]. The predictors are cell arrays containing sequences of varying length with a feature dimension of 12. The labels are categorical vectors of labels 1,2,...,9.

[XTrain,YTrain] = japaneseVowelsTrainData;
[XValidation,YValidation] = japaneseVowelsTestData;

View the sizes of the first few training sequences. The sequences are matrices with 12 rows (one row for each feature) and a varying number of columns (one column for each time step).

XTrain(1:5)

ans=5×1 cell array
    {12×20 double}
    {12×26 double}
    {12×22 double}
    {12×20 double}
    {12×21 double}

Define Network Architecture

Open Deep Network Designer.

deepNetworkDesigner

Select Blank Network.

Drag a sequenceInputLayer to the canvas and set the InputSize to 12, to match the feature dimension.

Then, drag an lstmLayer to the canvas. Set NumHiddenUnits to 100 and OutputMode to last.

Next, drag a fullyConnectedLayer onto the canvas and set OutputSize to 9, the number of classes.

Finally, drag a softmaxLayer and a classificationLayer onto the canvas. Connect your layers to create a series network.

Check Network Architecture

To check the network and examine more details of the layers, click Analyze. If the Deep Learning Network Analyzer reports zero errors, then the edited network is ready for training.

Export Network Architecture

To export the network architecture, on the Designer tab, click Export. Deep Network Designer saves the network as the variable layers_1.

You can also generate code to construct the network architecture by selecting Export > Generate Code.

Train Network

Specify the training options and train the network.

Because the mini-batches are small with short sequences, the CPU is better suited for training. Set 'ExecutionEnvironment' to 'cpu'. To train on a GPU, if available, set 'ExecutionEnvironment' to 'auto' (the default value).

miniBatchSize = 27;
options = trainingOptions('adam', ...
    'ExecutionEnvironment','cpu', ...
    'MaxEpochs',100, ...
    'MiniBatchSize',miniBatchSize, ...
    'ValidationData',{XValidation,YValidation}, ...
    'GradientThreshold',2, ...
    'Shuffle','every-epoch', ...
    'Verbose',false, ...
    'Plots','training-progress');

Train the network.

net = trainNetwork(XTrain,YTrain,layers_1,options);

Test Network

Classify the test data and calculate the classification accuracy. Specify the same mini-batch size as for training.

YPred = classify(net,XValidation,'MiniBatchSize',miniBatchSize);
acc = mean(YPred == YValidation)

acc = 0.9405

For next steps, you can try improving the accuracy by using bidirectional LSTM (BiLSTM) layers or by creating a deeper network. For more information, see Long Short-Term Memory Networks.

For an example showing how to use convolutional networks to classify sequence data, see Speech Command Recognition Using Deep Learning.

References