Data in 4D array but still getting error and network issue

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);

[Voss]

Please upload the .mat file so that others can run the code. Also, if running the code generates an error message, please specify the full text of that error message; if not, please explain why you say there is an error (e.g., results not as expected).

[Nathaniel Porter]

This is the error being produced:

Error using trainNetwork (line 184)

The training sequences are of feature dimension 180 but the input layer expects sequences of feature

dimension 2289.

Error in RNN_CW3 (line 52)

net = trainNetwork(X1_train,categorical(Y1),layers,options);

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);

Error using trainNetwork (line 184)
The training sequences are of feature dimension 180 but the input layer expects sequences of feature dimension 2289.

And my dat file is also attached. Any help will be appreciated

Answer 1

[yanqi liu]

yes，sir，may be it the same data and problem，so here use target split data to train and test，such as

clc; clear all; close all;
load generated_data.mat
% 2289*180
% 6 classes
X1_T = X1';
X2_T = X2';
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
% Split Data
X1_train = X1_T;
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
% Data Batch
XTrain=(reshape(train_X1', [2289,1,1,120]));
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,1,1,30]));
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,1,1,30]));
% CNN
layers = [imageInputLayer([2289 1 1])  % Creating the image layer
    convolution2dLayer([102 1],3,'Stride',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    dropoutLayer
    fullyConnectedLayer(6)
    softmaxLayer
    classificationLayer];

% Specify training options.
opts = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XVal,categorical(val_Y1)},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);

% Train
yc = categorical(train_Y1);
net1 = trainNetwork(XTrain,yc,layers,opts);

% Test
miniBatchSize = 27;
YPred = classify(net1,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');

acc = mean(YPred(:) == categorical(test_Y1(:)))
figure
t = confusionchart(categorical(test_Y1(:)),YPred(:));

acc =

0.9667

Answer 2

[Sahil Jain]

Hi Nathaniel. I am assuming your data consists of 180 sequences of length 2289 each. For vectors, the "sequenceInputLayer" expects inputs of size (num_features x sequence_length). For this case, the number of features is 1 so you should reshape "XTrain" to a cell array containing 120 cells where each cell is a 1x2289 double. This can be done by replacing your definition of "XTrain" with the following:

XTrain=(reshape(train_X1, [120,2289])); %Train data
XTrain = num2cell(XTrain, 2);

Correspondingly, set the value of variable "numFeatures" to 1. As your ouput variables are classes, you should replace the "regressionLayer" with a "classificationLayer". Also, make sure that the corresponding Y variable is a categorical column. This can be done using

YTrain = categorical(train_Y1')

Lastly, pass the newly created "XTrain" and "YTrain" to the the trainNetwork function.