LSTM実装に関するcsvファイル読み込み方法に関して

前提・実現したいこと

ここに質問の内容を詳しく書いてください。
matlabの”LSTMLayer”のツールを用いて、深層学習長短期記憶(LSTM)ネットワークを作成しています。データとしてcsvファイルを使用したいのですが、以下のエラーが発生しました。

発生している問題・エラーメッセージ

出力引数が多すぎます。

エラー: LSTMLayer (行 14)
[XTrain,YTrain] = ('data.csv');

該当のソースコード

matlab
1layer = lstmLayer(100,'Name','lstm1')
2
3inputSize = 12;
4numHiddenUnits = 100;
5numClasses = 9;
6
7layers = [ ...
8    sequenceInputLayer(inputSize)
9    lstmLayer(numHiddenUnits)
10    fullyConnectedLayer(numClasses)
11    softmaxLayer
12    classificationLayer]
13
14[XTrain,YTrain] = ('data.csv');
15
16figure
17plot(XTrain{1}')
18title("Training Observation 1")
19numFeatures = size(XTrain{1},1);
20legend("Feature " + string(1:numFeatures),'Location','northeastoutside')
21
22inputSize = 12;
23numHiddenUnits = 100;
24numClasses = 9;
25
26layers = [ ...
27    sequenceInputLayer(inputSize)
28    lstmLayer(numHiddenUnits,'OutputMode','last')
29    fullyConnectedLayer(numClasses)
30    softmaxLayer
31    classificationLayer]
32
33maxEpochs = 70;
34miniBatchSize = 27;
35
36options = trainingOptions('adam', ...
37    'ExecutionEnvironment','cpu', ...
38    'MaxEpochs',maxEpochs, ...
39    'MiniBatchSize',miniBatchSize, ...
40    'GradientThreshold',1, ...
41    'Verbose',false, ...
42    'Plots','training-progress');
43
44net = trainNetwork(XTrain,YTrain,layers,options);
45[XTest,YTest] = ('data.csv');
46YPred = classify(net,XTest,'MiniBatchSize',miniBatchSize);
47acc = sum(YPred == YTest)./numel(YTest)
48
49numFeatures = 12;
50numHiddenUnits = 100;
51numClasses = 9;
52layers = [ ...
53    sequenceInputLayer(numFeatures)
54    lstmLayer(numHiddenUnits,'OutputMode','last')
55    fullyConnectedLayer(numClasses)
56    softmaxLayer
57    classificationLayer];
58
59numFeatures = 12;
60numHiddenUnits = 100;
61numClasses = 9;
62layers = [ ...
63    sequenceInputLayer(numFeatures)
64    lstmLayer(numHiddenUnits,'OutputMode','sequence')
65    fullyConnectedLayer(numClasses)
66    softmaxLayer
67    classificationLayer];
68
69numFeatures = 12;
70numHiddenUnits = 125;
71numResponses = 1;
72
73layers = [ ...
74    sequenceInputLayer(numFeatures)
75    lstmLayer(numHiddenUnits,'OutputMode','last')
76    fullyConnectedLayer(numResponses)
77    regressionLayer];
78
79numFeatures = 12;
80numHiddenUnits = 125;
81numResponses = 1;
82
83layers = [ ...
84    sequenceInputLayer(numFeatures)
85    lstmLayer(numHiddenUnits,'OutputMode','sequence')
86    fullyConnectedLayer(numResponses)
87    regressionLayer];
88
89numFeatures = 12;
90numHiddenUnits1 = 125;
91numHiddenUnits2 = 100;
92numClasses = 9;
93layers = [ ...
94    sequenceInputLayer(numFeatures)
95    lstmLayer(numHiddenUnits1,'OutputMode','sequence')
96    dropoutLayer(0.2)
97    lstmLayer(numHiddenUnits2,'OutputMode','last')
98    dropoutLayer(0.2)
99    fullyConnectedLayer(numClasses)
100    softmaxLayer
101    classificationLayer];
102
103numFeatures = 12;
104numHiddenUnits1 = 125;
105numHiddenUnits2 = 100;
106numClasses = 9;
107layers = [ ...
108    sequenceInputLayer(numFeatures)
109    lstmLayer(numHiddenUnits1,'OutputMode','sequence')
110    dropoutLayer(0.2)
111    lstmLayer(numHiddenUnits2,'OutputMode','sequence')
112    dropoutLayer(0.2)
113    fullyConnectedLayer(numClasses)
114    softmaxLayer
115    classificationLayer];
116

試したこと

writecsvまたはreadtable('filename.csv');を使ってcsvの読み込みを試みました。

補足情報（FW/ツールのバージョンなど）

ツール：mathworks
用いているcsvデータはファイル名："data.csv"、データ数：30 × 1000データを使用しています。また、参考にしたURLを以下に示します。
参考URL : https://jp.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.lstmlayer.html