Reset the network state between sequence predictions.

LoadJapaneseVowelsNet, a pretrained long short-term memory (LSTM) network trained on the Japanese Vowels data set as described in [1] and [2]. This network was trained on the sequences sorted by sequence length with a mini-batch size of 27.

loadJapaneseVowelsNet

View the network architecture.

net.Layers

ans = 5x1 Layer array with layers: 1 'sequenceinput' Sequence Input Sequence input with 12 dimensions 2 'lstm' LSTM LSTM with 100 hidden units 3 'fc' Fully Connected 9 fully connected layer 4 'softmax' Softmax softmax 5 'classoutput' Classification Output crossentropyex with '1' and 8 other classes

Load the test data.

[XTest,YTest] = japaneseVowelsTestData;

Classify a sequence and update the network state. For reproducibility, setrngto'shuffle'.

rng('shuffle') X = XTest{94}; [net,label] = classifyAndUpdateState(net,X); label

label =categorical3

Classify another sequence using the updated network.

X = XTest{1}; label = classify(net,X)

label =categorical7

Compare the final prediction with the true label.

trueLabel = YTest(1)

trueLabel =categorical1

The updated state of the network may have negatively influenced the classification. Reset the network state and predict on the sequence again.

net = resetState(net); label = classify(net,XTest{1})

label =categorical1