How to create LSTM network of multiple dimension

Question

Vinay Kulkarni 2023-3-10

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https://ww2.mathworks.cn/matlabcentral/answers/1926405-how-to-create-lstm-network-of-multiple-dimension

评论： Vinay Kulkarni 2023-3-15

Hi,

My input dimension for neural network is : 653956x32x16. Out of this matrix dimension, 32x16 is Input size, that i have given for layer formation as below :

layers=[

sequenceInputLayer(input_size_network)

flattenLayer

lstmLayer(32,'Name','LSTM1')

layerNormalizationLayer('Name','LSTM1_layernorm')

lstmLayer(16,'Name','LSTM2')

layerNormalizationLayer('Name','LSTM2_layernorm')

lstmLayer(8,'Name','LSTM3')

layerNormalizationLayer('Name','LSTM3_layernorm')

flattenLayer('Name','Flatten_layer')

fullyConnectedLayer(48,'Name','FC1')

fullyConnectedLayer(16,'Name','FC2')

fullyConnectedLayer(3,'Name','FC3')

softmaxLayer

classificationLayer

]

options = trainingOptions("adam","MaxEpochs",50,"SequencePaddingDirection","left",InitialLearnRate=0.001,Shuffle="every-epoch",Plots="training-progress",Verbose=0);

cv = cvpartition(size(X,1),'HoldOut',0.2);

idx = cv.test;

XTrain = X(~idx,:,:);

YTrain =Y(~idx,:);

XTest = X(idx,:,:);

YTest =Y(idx,:);

net =trainNetwork(XTrain,YTest,layers,options);

But when i try giving XTrain, which is of the same dimension 653956x32x16 , for training network i get following error.

Error using trainNetwork (line 184)

The training sequences are of feature dimension 653956 32 but the input layer expects sequences of feature

dimension 32 16.

Can you please help me on how i can pass input, as i wanted to pass LSTM with 16 as feature and 32 as time sequence.

Note : X or Xtrain is pure Matrix array and not cell.

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Answer 1

Ben 2023-3-13

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It appears your data is in (Batch) x (Sequence) x (Features) format. For trainNetwork you need to represent you sequence data as a (Batch) x 1 cell array where each entry is a (Features) x (Sequence) array. For example here is how to transform an X of the size you have to what is necessary:

batchSize = 653956;

sequenceLength = 32;

numFeatures = 16;

X = randn(batchSize,sequenceLength,numFeatures);

% trainNetwork needs each sequence to be in (Features) x (Sequence) shape, so permute these dimensions to the front.

X = permute(X,[3,2,1]);

% trainNetwork needs sequence data to be represented as a cell-array

X = num2cell(X,[1,2]);

X = X(:);

As a side note, since you are using sequences of vectors as input, the flattenLayer-s are unnecessary.

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Ben 2023-3-14

> the assumption of time sequence is automatically taken care

Yes, you do not need to specify the sequence length in sequenceInputLayer - in fact you can have variable sequence lengths in your data and sequenceInputLayer handles this.

>LSTM cell's hidden units need to pass return sequences

This is the default for us: lstmLayer(32,"Name","LSTM1") will return the sequence of hidden states, it is setting the "OutputMode" to the default value "sequence".

I notice your Keras LSTM also has return_state=True. We support this as well via the "HasStateOutputs" name-value pair, so you might want something like:

layer = lstmLayer(32,"Name","LSTM1","HasStateOutputs",true);

This will output the states from the 2nd output of the layer.

Vinay Kulkarni 2023-3-15