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How to combine multiple net in LSTM

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Luc Xuan Bui
Luc Xuan Bui 2024 年 4 月 8 日
コメント済み: Luc Xuan Bui 2024 年 4 月 14 日
I intend to train three sequences using LSTM, then combine them into one 'net' for prediction to speed up the training process. However, I'm facing difficulties in achieving this.

回答 (1 件)

Ben
Ben 2024 年 4 月 9 日
You can combine 3 separate LSTM-s into one network by adding them to a dlnetwork object and hooking up the outputs. Note that if the LSTM-s have OutputMode="sequence" then either you need all input sequences to have the same length, or have some layer(s) that can manage the data with different sequence lengths.
Here's an example with OutputMode="last"
inputSizes = [1,2,3];
outputSize = 4;
lstmHiddenSize = 5;
hiddenSize = 10;
sequenceLengths = [6,7,8];
x1 = dlarray(rand(inputSizes(1),sequenceLengths(1)),"CT");
x2 = dlarray(rand(inputSizes(2),sequenceLengths(2)),"CT");
x3 = dlarray(rand(inputSizes(3),sequenceLengths(3)),"CT");
layers = [
sequenceInputLayer(inputSizes(1))
lstmLayer(lstmHiddenSize,OutputMode="last")
concatenationLayer(1,3,Name="cat")
fullyConnectedLayer(hiddenSize)
reluLayer
fullyConnectedLayer(outputSize)];
net = dlnetwork(layers,Initialize=false);
net = addLayers(net,[sequenceInputLayer(inputSizes(2));lstmLayer(lstmHiddenSize,OutputMode="last",Name="lstm2")]);
net = addLayers(net,[sequenceInputLayer(inputSizes(3));lstmLayer(lstmHiddenSize,OutputMode="last",Name="lstm3")]);
net = connectLayers(net,"lstm2","cat/in2");
net = connectLayers(net,"lstm3","cat/in3");
net = initialize(net);
y = predict(net,x1,x2,x3)
  1 件のコメント
Luc Xuan Bui
Luc Xuan Bui 2024 年 4 月 14 日
Sorry for making it seem like I didn't state the question clearly. I have a data string 0-t, but I want to split this string into 3 strings 0-t1, t1-t2, t2-t. Then I train these 3 sequences with 3 different parameters on the LSTM network, but I want to make sure the output is only 1. That is, I have 3 inputs separated from 1 continuous sequence, the first 3 nets. output after training, then combine the information learned from these 3 nets into 1 for prediction. Your method is 3 inputs predicting 3 outputs. Sorry for this misunderstanding.

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