Number of observations in X and Y disagree. For convolution neural network

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Nathaniel Porter
Nathaniel Porter on 5 Dec 2021
Commented: yanqi liu on 13 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,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1, [30,1,1,2289])); %Train data
%Separating and partioning for test data
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1, [30,1,1,2289])); %Train data
%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
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([120 1 1]) % Creating the image layer
convolution2dLayer([102 1],3,'Stride',1)
maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
% convolution2dLayer([24 1],10,'Stride',1)
% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
% convolution2dLayer([11 1],10,'Stride',1)
% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
% convolution2dLayer([9 1],10,'Stride',1)
% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
fullyConnectedLayer(6)
%fullyConnectedLayer(6)
%fullyConnectedLayer(6)
softmaxLayer
classificationLayer];
% Specify training options.
opts = trainingOptions('adam', ...
'MaxEpochs',50, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{XVal,val_Y1},...
'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
net1 = trainNetwork(XTrain,categorical(train_Y1),layers,opts);
%% Compare against testing Data
miniBatchSize =27;
YPred = classify(net1,test_X1, ...
MiniBatchSize=miniBatchSize, ...
SequencePaddingDirection="left");
acc = mean(YPred == categorical(test_Y1));
figure
t = confusionchart(categorical(test_Y1),YPred);
  1 Comment
Rik
Rik on 8 Dec 2021
Have a read here and here. It will greatly improve your chances of getting an answer.
Posting duplicates does not help, so please don't.

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Accepted Answer

yanqi liu
yanqi liu on 9 Dec 2021
yes,sir,may be upload generated_data.mat to make some analysis
  11 Comments
yanqi liu
yanqi liu on 13 Dec 2021
yes,sir,i think
ind = randperm(size(X1_T, 1));
may be get different train、test、val
so,i think the data split should make prepare,and use the 6 classies data

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