Error when trying lstm regression not sure of the issue

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Nathaniel Porter
Nathaniel Porter on 9 Dec 2021
Commented: Nathaniel Porter on 15 Dec 2021
clc; clear all; close all;
%Import/Upload data
load generated_data.mat
%transposing glucose data
X1_T = X1';
%transposing insulin data
X2_T = X2';
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
%DataParts = zeros(size(Train_inputX1,1), size(Train_inputX1,2),1,2); %(4500,400,1,2)
%DataParts(:,:,:,1) = real(cell2mat(Train_inputX1));
%DataParts(:,:,:,2) = imag(cell2mat(Train_inputX1)) ;
XTrain=(reshape(train_X1, [120,1,1,2289])); %Train data
%Separating and partioning for validation data
val_X1 = X1_train(121:150,:);
XVal=(reshape(val_X1, [30,1,1,2289])); %Train data
%Separating and partioning for test data
test_X1 = X1_train(151:180,:);
%Xtest=(reshape(test_X1, [120,1,1,2289])); %Train data
%Separating data in training, validation and testing data
%X2_train = X2_T;
%Partioning data for training
%train_X2 = X2_train(1:120,:);
%Separating and partioning for validation data
%val_X2 = X2_train(121:150,:);
%Separating and partioning for test data
%test_X2 = X2_train(151:180,:);
%The number of features chosen to be two representing both glucose and
%insulin
numFeatures = size(X1_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 180;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(Y1)));
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(numClasses)
softmaxLayer
regressionLayer];
options = trainingOptions('adam', ...
'MaxEpochs',60, ...
'GradientThreshold',2, ...
'Verbose',0, ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',5, ...
'Plots','training-progress');
net = trainNetwork(X1_train',categorical(Y1),layers,options);
  1 Comment
KSSV
KSSV on 9 Dec 2021
You have to specify the error. We don't have your data and also we don't know your error to help you.

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Answers (1)

yanqi liu
yanqi liu on 13 Dec 2021
yes,sir,may be change the layers,such as
clc; clear all; close all;
%Import/Upload data
load generated_data.mat
%transposing glucose data
X1_T = X1';
%transposing insulin data
X2_T = X2';
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
numFeatures = size(X1_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 180;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(Y1)));
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(100,'OutputMode','sequence')
dropoutLayer(0.3)
lstmLayer(50,'OutputMode','sequence')
dropoutLayer(0.2)
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs',3000, ...
'GradientThreshold',0.2, ...
'Verbose',0, ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',5, ...
'Plots','training-progress');
net = trainNetwork(X1_train',categorical(Y1),layers,options);
  1 Comment
Nathaniel Porter
Nathaniel Porter on 15 Dec 2021
Okay, but im trying to use the lstm for a regression so will it still operate as such even though on eof the layers are classifcation layer

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