Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.

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Nathaniel Porter
Nathaniel Porter 2022-2-22
评论： Walter Roberson 2022-2-22
clc; clear all; close all;
%Import/Upload data
load GlucoseReadings.mat
% change to label vector
CS = categories(categorical(GR_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
Z1(i,1) = find(GR_output(i)==CS);
end
%for i = 1 : length(Y2)
%Z2(i,1) = find(Y2(i)==CS);
%end
Yo1 = GR_output;
%Yo2 = Y2;
GR_output= Z1;
%Y2 = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:17);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,1,1,17]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(18:21);
%reshaping data into 4D array
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,1,1,18])); %Train data
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(19:24,:);
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(19:24);
%reshaping data into 4D array
GlucoseReadingsTest=(reshape(GlucoseReadings_X1', [1438,1,1,18])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1438 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(1)
regressionLayer];
% Specify training options.
opts = trainingOptions('sgdm', ...
'MaxEpochs',1500, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal,val_GR_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);
%% Compare against testing Data
Ypredicted = predict(net1, GlucoseReadingsTest)
predictionError = test_GR_output - GR_outputpredicted;
squares = predictionError.^2;
rmse = sqrt(mean(squares))
figure
scatter(GR_outputpredicted, test_GR_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')
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Walter Roberson
Walter Roberson 2022-2-22
Ran in:
fileurl = 'https://www.mathworks.com/matlabcentral/answers/uploaded_files/902755/GlucoseReadings.mat';
%Import/Upload data
tname = tempname() + ".mat";
urlwrite(fileurl, tname);
data = load(tname);
GR_output = data.GR_output;
GlucoseReadings = data.GlucoseReadings;
% change to label vector
CS = categories(categorical(GR_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
Z1(i,1) = find(GR_output(i)==CS);
end
%for i = 1 : length(Y2)
%Z2(i,1) = find(Y2(i)==CS);
%end
Yo1 = GR_output;
%Yo2 = Y2;
GR_output= Z1;
%Y2 = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:17);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,1,1,17]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
That is obviously 4 by something
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(18:21);
%reshaping data into 4D array
whos
Name Size Bytes Class Attributes CS 2x1 238 cell GR_output 24x1 192 double GlucoseReadings 1438x24 276096 double GlucoseReadingsTrain 1438x1x1x17 195568 double GlucoseReadings_T 24x1438 276096 double GlucoseReadings_train 24x1438 276096 double Yo1 1x26 1518 string Z1 26x1 208 double Z2 0x0 0 double ans 1x47 94 char data 1x1 277950 struct fileurl 1x89 178 char i 1x1 8 double ind 1x24 192 double tname 1x1 308 string train_GR_output 17x1 136 double train_GlucoseReadings 17x1438 195568 double val_GR_output 4x1 32 double val_GlucoseReadings 4x1438 46016 double
and checking we see that Yes, it is 4 x something
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,1,1,18])); %Train data
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.
The code is treating it as if it is 18 x something, not 4 x something.
But why 18 anyhow? You pulled off 17 columns to make GclusoseReadingsTrain, and you pulled off 4 columns to make val_GlucoseReadings, and columns 22 to 24 are not extracted from yet... but why pull out 18 ? It would make more sense if you were doing 18:end to pull out everything other than the first 17.
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(19:24,:);
Why are you re-using 19, 20, 21, as well as adding in 22, 23, 24, but at the same time ignoring 18 ??
I would recommend to you that you use variables to hold the number you are selecting for training, and then be consistent about using indices like 1:numtrain, numtrain+1:numtrain+numtest, numtrain+numtest+1:end
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(19:24);
%reshaping data into 4D array
GlucoseReadingsTest=(reshape(GlucoseReadings_X1', [1438,1,1,18])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1438 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(1)
regressionLayer];
% Specify training options.
opts = trainingOptions('sgdm', ...
'MaxEpochs',1500, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal,val_GR_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);
%% Compare against testing Data
Ypredicted = predict(net1, GlucoseReadingsTest)
predictionError = test_GR_output - GR_outputpredicted;
squares = predictionError.^2;
rmse = sqrt(mean(squares))
figure
scatter(GR_outputpredicted, test_GR_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')
  2 个评论
Nathaniel Porter
Nathaniel Porter 2022-2-22
Ran in:
clc; clear all; close all;
%Import/Upload data
load GlucoseReadings.mat
Error using load
Unable to find file or directory 'GlucoseReadings.mat'.
% change to label vector
CS = categories(categorical(GR_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
Z1(i,1) = find(GR_output(i)==CS);
end
%for i = 1 : length(Y2)
%Z2(i,1) = find(Y2(i)==CS);
%end
Yo1 = GR_output;
%Yo2 = Y2;
GR_output= Z1;
%Y2 = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:17);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,1,1,17]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(18:21);
%reshaping data into 4D array
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,1,1,4])); %Train data
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(19:24,:);
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(19:24);
%reshaping data into 4D array
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1438,1,1,4])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1438 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(1)
regressionLayer];
% Specify training options.
opts = trainingOptions('sgdm', ...
'MaxEpochs',1500, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal,val_GR_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);
%% Compare against testing Data
Ypredicted = predict(net1, GlucoseReadingsTest)
predictionError = test_GR_output - GR_outputpredicted;
squares = predictionError.^2;
rmse = sqrt(mean(squares))
figure
scatter(GR_outputpredicted, test_GR_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')
Is this what you meant I should fix by chance
Walter Roberson
Walter Roberson 2022-2-22
Ran in:
You are still making mistakes in selecting your training, validation, and test set.
Create a variable which is the number of training, and index 1:numtrain instead of 1:17.
Create a variable which is the number of validations, and index numtrain+1:numtrain+numval instead of 18:21
Create a variable which is the number of test, as size(GlocuseReadings_T,1) - numtrain - numval, and index with numtrain+numval+1:numtrain+numval+numtest instead of 19:24
And in code like [1438,1,1,17] replace the 17 with numtrain, and replace the 4's with numval
fileurl = 'https://www.mathworks.com/matlabcentral/answers/uploaded_files/902755/GlucoseReadings.mat';
%Import/Upload data
tname = tempname() + ".mat";
urlwrite(fileurl, tname);
data = load(tname);
GR_output = data.GR_output;
GlucoseReadings = data.GlucoseReadings;
% change to label vector
CS = categories(categorical(GR_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
Z1(i,1) = find(GR_output(i)==CS);
end
%for i = 1 : length(Y2)
%Z2(i,1) = find(Y2(i)==CS);
%end
Yo1 = GR_output;
%Yo2 = Y2;
GR_output= Z1;
%Y2 = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:17);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,1,1,17]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(18:21);
%reshaping data into 4D array
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,1,1,4])); %Train data
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(19:24,:);
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(19:24);
%reshaping data into 4D array
whos
Name Size Bytes Class Attributes CS 2x1 238 cell GR_output 24x1 192 double GlucoseReadings 1438x24 276096 double GlucoseReadingsTrain 1438x1x1x17 195568 double GlucoseReadingsVal 1438x1x1x4 46016 double GlucoseReadings_T 24x1438 276096 double GlucoseReadings_train 24x1438 276096 double Yo1 1x26 1518 string Z1 26x1 208 double Z2 0x0 0 double ans 1x47 94 char data 1x1 277950 struct fileurl 1x89 178 char i 1x1 8 double ind 1x24 192 double test_GR_output 6x1 48 double test_GlucoseReadings 6x1438 69024 double tname 1x1 308 string train_GR_output 17x1 136 double train_GlucoseReadings 17x1438 195568 double val_GR_output 4x1 32 double val_GlucoseReadings 4x1438 46016 double
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1438,1,1,4])); %Train data
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1438 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(1)
regressionLayer];
% Specify training options.
opts = trainingOptions('sgdm', ...
'MaxEpochs',1500, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal,val_GR_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);
%% Compare against testing Data
Ypredicted = predict(net1, GlucoseReadingsTest)
predictionError = test_GR_output - GR_outputpredicted;
squares = predictionError.^2;
rmse = sqrt(mean(squares))
figure
scatter(GR_outputpredicted, test_GR_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')

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