Number of observations in X and Y disagree.

clc; clear all; close all;
%Import/Upload data
load Projectdata.mat
% change to label vector
CS = categories(categorical(GR_output));
CS1 = categories(categorical(INS_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
    Z1(i,1) = find(GR_output(i)==CS);
end
for i = 1 : length(INS_output)
    Z2(i,1) = find(INS_output(i)==CS1);
end
Yo1 = GR_output;
Yo2 = INS_output;
GR_output= Z1;
INS_output = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%transposing insulin data
InsulinReadings_T = InsulinReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
ind = randperm(size(InsulinReadings_T, 1));
InsulinReadings_T = InsulinReadings_T(ind, :);
INS_output = INS_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
InsulinReadings_train = InsulinReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
train_InsulinReadings = InsulinReadings_train(1:84,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:17);
train_INS_output = INS_output(1:84);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,1,1,84]));
InsulinReadingsTrain=(reshape(train_InsulinReadings',[1758,1,1,84]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_InsulinReadings = InsulinReadings_train(85:102,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(85:102);
val_INS_output = INS_output(85:102);
%reshaping data into 4D array
whos

  Name                          Size                  Bytes  Class     Attributes

  CS                            2x1                     248  cell                
  CS1                           2x1                     248  cell                
  GR_output                   120x1                     960  double              
  GlucoseReadings            1749x120               1679040  double              
  GlucoseReadingsTrain       1749x1x1x84            1175328  double              
  GlucoseReadings_T           120x1749              1679040  double              
  GlucoseReadings_train       120x1749              1679040  double              
  INS_output                  120x1                     960  double              
  InsulinReadings            1758x120               1687680  double              
  InsulinReadingsTrain       1758x1x1x84            1181376  double              
  InsulinReadings_T           120x1758              1687680  double              
  InsulinReadings_train       120x1758              1687680  double              
  Yo1                           1x120                  7296  string              
  Yo2                           1x120                  7296  string              
  Z1                          120x1                     960  double              
  Z2                          120x1                     960  double              
  i                             1x1                       8  double              
  ind                           1x120                   960  double              
  train_GR_output              17x1                     136  double              
  train_GlucoseReadings        84x1749              1175328  double              
  train_INS_output             84x1                     672  double              
  train_InsulinReadings        84x1758              1181376  double              
  val_GR_output                18x1                     144  double              
  val_GlucoseReadings          18x1749               251856  double              
  val_INS_output               18x1                     144  double              
  val_InsulinReadings          18x1758               253152  double              

GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,1,1,18]));
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,1,1,18]));
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(103:120,:);
test_InsulinReadings = InsulinReadings_train(103:120,:);
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(103:120);
test_INS_output = INS_output(103:120);
%reshaping data into 4D array
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,1,1,18])); 
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,1,1,18]));
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1758 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',1000, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal,val_GR_output,},...
    'ValidationData',{InsulinReadingsVal,val_INS_output,},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
yc1 = train_INS_output(:);
net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);

Error using trainNetwork (line 184)
Number of observations in X and Y disagree.

net2 = trainNetwork(InsulinReadingsTrain,yc1,layers,opts);
%% Compare against testing Data
GR_outputpredicted = predict(net1, GlucoseReadingsTest)
INS_outputpredicted = predict(net1, InsulinReadingsTest)
predictionError = test_GR_output - GR_outputpredicted;
predictionError1 = test_INS_output - INS_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--')

scatter(INS_outputpredicted, test_INS_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')

Answer 1

[KSSV]

Your yc i.e. target for the respective input in the line:

net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);

is of dimension 17x1. Where as input is 1749x1x1x84. So there is a miss match and you got error. Where as the variables yc1 is of dimension 84x1 and this should work. This line should not through any error:

net2 = trainNetwork(InsulinReadingsTrain,yc1,layers,opts);

So you have to use net2. You may delete the line net1.

[Nathaniel Porter]

Okay i understand to change the dimensions but I am trainig two sets of data once i fix the error will it run both?

[KSSV]

To run you need to have compatible dimensions betwwen input and output.

[Nathaniel Porter]

The validation images are of size 1758×1×1 but the input layer expects images of size 1749×1×1.

clc; clear all; close all;
%Import/Upload data
load Projectdata.mat
% change to label vector
CS = categories(categorical(GR_output));
CS1 = categories(categorical(INS_output));
Z1 = []; Z2 = [];
for i = 1 : length(GR_output)
    Z1(i,1) = find(GR_output(i)==CS);
end
for i = 1 : length(INS_output)
    Z2(i,1) = find(INS_output(i)==CS1);
end
Yo1 = GR_output;
Yo2 = INS_output;
GR_output= Z1;
INS_output = Z2;
%transposing glucose data
GlucoseReadings_T = GlucoseReadings';
%transposing insulin data
InsulinReadings_T = InsulinReadings';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(GlucoseReadings_T, 1));
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = GR_output(ind);
ind = randperm(size(InsulinReadings_T, 1));
InsulinReadings_T = InsulinReadings_T(ind, :);
INS_output = INS_output(ind);
%Separating data in training, validation and testing data
GlucoseReadings_train = GlucoseReadings_T;
InsulinReadings_train = InsulinReadings_T;
%Partioning data for training 70%
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
train_InsulinReadings = InsulinReadings_train(1:84,:);
%Corresponding X(input) data to Y(output) data
train_GR_output = GR_output(1:84);
train_INS_output = INS_output(1:84);
%reshaping data into 4D array
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,1,1,84]));
InsulinReadingsTrain=(reshape(train_InsulinReadings',[1758,1,1,84]));
%Separating and partioning for validation data 15%
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_InsulinReadings = InsulinReadings_train(85:102,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(85:102);
val_INS_output = INS_output(85:102);
%reshaping data into 4D array
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,1,1,18]));
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,1,1,18]));
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(103:120,:);
test_InsulinReadings = InsulinReadings_train(103:120,:);
%Corresponding X(input) data to Y(output) data
test_GR_output = GR_output(103:120);
test_INS_output = INS_output(103:120);
%reshaping data into 4D array
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,1,1,18])); 
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,1,1,18]));
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([1749 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];
layers1 = [imageInputLayer([1758 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',1000, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal,val_GR_output,},...
    'ValidationData',{InsulinReadingsVal,val_INS_output,},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_GR_output(:);
yc1 = train_INS_output(:);
whos

  Name                          Size                  Bytes  Class                           Attributes

  CS                            2x1                     248  cell                                      
  CS1                           2x1                     248  cell                                      
  GR_output                   120x1                     960  double                                    
  GlucoseReadings            1749x120               1679040  double                                    
  GlucoseReadingsTest        1749x1x1x18             251856  double                                    
  GlucoseReadingsTrain       1749x1x1x84            1175328  double                                    
  GlucoseReadingsVal         1749x1x1x18             251856  double                                    
  GlucoseReadings_T           120x1749              1679040  double                                    
  GlucoseReadings_train       120x1749              1679040  double                                    
  INS_output                  120x1                     960  double                                    
  InsulinReadings            1758x120               1687680  double                                    
  InsulinReadingsTest        1758x1x1x18             253152  double                                    
  InsulinReadingsTrain       1758x1x1x84            1181376  double                                    
  InsulinReadingsVal         1758x1x1x18             253152  double                                    
  InsulinReadings_T           120x1758              1687680  double                                    
  InsulinReadings_train       120x1758              1687680  double                                    
  Yo1                           1x120                  7296  string                                    
  Yo2                           1x120                  7296  string                                    
  Z1                          120x1                     960  double                                    
  Z2                          120x1                     960  double                                    
  i                             1x1                       8  double                                    
  ind                           1x120                   960  double                                    
  layers                        8x1                    4018  nnet.cnn.layer.Layer                      
  layers1                       8x1                    4018  nnet.cnn.layer.Layer                      
  opts                          1x1                  253757  nnet.cnn.TrainingOptionsSGDM              
  test_GR_output               18x1                     144  double                                    
  test_GlucoseReadings         18x1749               251856  double                                    
  test_INS_output              18x1                     144  double                                    
  test_InsulinReadings         18x1758               253152  double                                    
  train_GR_output              84x1                     672  double                                    
  train_GlucoseReadings        84x1749              1175328  double                                    
  train_INS_output             84x1                     672  double                                    
  train_InsulinReadings        84x1758              1181376  double                                    
  val_GR_output                18x1                     144  double                                    
  val_GlucoseReadings          18x1749               251856  double                                    
  val_INS_output               18x1                     144  double                                    
  val_InsulinReadings          18x1758               253152  double                                    
  yc                           84x1                     672  double                                    
  yc1                          84x1                     672  double                                    

net1 = trainNetwork(GlucoseReadingsTrain,yc,layers,opts);

Error using trainNetwork (line 184)
The validation images are of size 1758×1×1 but the input layer expects images of size 1749×1×1.

net2 = trainNetwork(InsulinReadingsTrain,yc1,layers1,opts);
%% Compare against testing Data
GR_outputpredicted = predict(net1, GlucoseReadingsTest)
INS_outputpredicted = predict(net2, InsulinReadingsTest)

predictionError = test_GR_output - GR_outputpredicted;
predictionError1 = test_INS_output - INS_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--')

scatter(INS_outputpredicted, test_INS_output,'+')
title ('True value vs Predicted Value')
xlabel ("Predicted Value")
ylabel ("True Value")
hold on
plot([-3 3], [-7 7], 'b--')

[KSSV]

Now you messed up with the input: GlucoseReadingsTrain

It should be 1758x1x1x84, but it is 1749x1x1x84.

[Nathaniel Porter]

GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1758,1,1,84]));

Changed it but got this:

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.

Answer 2

[Walter Roberson]

net2 = trainNetwork(train_GlucoseReadings,yc1,layers1,opts);

Your code uses a series of variables that are not well distinguished from each other in meaning, so it is easy to get wrong which ones you are talking about.

[Nathaniel Porter]

No well this is suppose to be my net two which are my insulin readings

net2 = trainNetwork(InsulinReadingsTrain,yc1,layers1,opts);