Invalid training data. For classification tasks, responses must be a vector of categorical responses. For regression tasks, responses must be a vector, a matrix, or a 4-D arra
6 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Nathaniel Porter
am 4 Mär. 2022
Kommentiert: Nathaniel Porter
am 7 Mär. 2022
clc; clear all; close all;
load Projectdata.mat
% Split Data Glucose
GlucoseReadings_T = GlucoseReadings';
GlucoseReadings_train = GlucoseReadings_T;
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
train_GR_output = GR_output(1:17);
%Split Data Insulin
InsulinReadings_T = InsulinReadings';
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:17);
% Data Batch Glucose
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,84]));
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_GR_output = GR_output(85:102);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,18]));
test_GlucoseReadings =GlucoseReadings_train(103:120,:);
test_GR_output = GR_output(103:120);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,18]));
numFeatures = size(GlucoseReadings_T,2);
%Data Batch Insulin
InsulinReadingsTrain=(reshape(train_InsulinReadings', [1758,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,18]));
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,18]));
numFeatures1 = size(InsulinReadings_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 120;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(GR_output)));
numClasses1 = length(categories(categorical(INS_output)));
layers = [ ...
sequenceInputLayer(numFeatures)
%dropoutLayer(0.5)
instanceNormalizationLayer
bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
fullyConnectedLayer(numClasses)
instanceNormalizationLayer
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'GradientThreshold',1, ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal, val_GR_output},...
'ValidationData',{InsulinReadingsVal, val_INS_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'Plots','training-progress');
% Train
whos
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
net1 = trainNetwork(InsulinReadingsTrain,train_INS_output,layers,options);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest,...
'MiniBatchSize',miniBatchSize,...
'Environment','cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
acc1 = mean(INS_outputPred(:) == categorical(test_INS_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));
figure
t1 = confusionchart(categorical(test_INS_output(:)),INS_outputPred(:));
2 Kommentare
KSSV
am 4 Mär. 2022
Please note that, you have to close/ acknowledge the already posted question and go for other question.
Akzeptierte Antwort
Walter Roberson
am 4 Mär. 2022
net = trainNetwork(GlucoseReadingsTrain, categorical(train_GR_output), layers,options);
2 Kommentare
Walter Roberson
am 4 Mär. 2022
train_GR_output = GR_output(1:17);
That response should only be used with an input of size 17.
You should not be training on data only from one class: you should be training on data from all of your classes.
Weitere Antworten (1)
yanqi liu
am 7 Mär. 2022
clc; clear all; close all;
load Projectdata.mat
% Split Data Glucose
GR_output=categorical(GR_output);
INS_output=categorical(INS_output);
GlucoseReadings_T = GlucoseReadings';
GlucoseReadings_train = GlucoseReadings_T;
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
train_GR_output = GR_output(1:84);
%Split Data Insulin
InsulinReadings_T = InsulinReadings';
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:84);
% Data Batch Glucose
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,84]));
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_GR_output = GR_output(85:102);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,18]));
test_GlucoseReadings =GlucoseReadings_train(103:120,:);
test_GR_output = GR_output(103:120);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,18]));
numFeatures = size(GlucoseReadings_T,2);
%Data Batch Insulin
InsulinReadingsTrain=(reshape(train_InsulinReadings', [1758,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,18]));
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,18]));
numFeatures1 = size(InsulinReadings_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 120;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(GR_output)));
numClasses1 = length(categories(categorical(INS_output)));
layers = [ ...
sequenceInputLayer(numFeatures)
dropoutLayer(0.5)
%instanceNormalizationLayer
bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
fullyConnectedLayer(numClasses)
%instanceNormalizationLayer
dropoutLayer(0.5)
softmaxLayer
classificationLayer];
layers1 = [ ...
sequenceInputLayer(numFeatures1)
dropoutLayer(0.5)
%instanceNormalizationLayer
bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
fullyConnectedLayer(numClasses)
%instanceNormalizationLayer
dropoutLayer(0.5)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'GradientThreshold',1, ...
'Verbose',false, ...
'ValidationData',{GlucoseReadingsVal, val_GR_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'Plots','training-progress');
options1 = trainingOptions('adam', ...
'MaxEpochs',100, ...
'GradientThreshold',1, ...
'Verbose',false, ...
'ValidationData',{InsulinReadingsVal, val_INS_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'Plots','training-progress');
% Train
% whos
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
net1 = trainNetwork(InsulinReadingsTrain,train_INS_output,layers1,options1);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest,...
'MiniBatchSize',miniBatchSize,...
'ExecutionEnvironment','cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
INS_outputPred = classify(net1,InsulinReadingsTest,...
'MiniBatchSize',miniBatchSize,...
'ExecutionEnvironment','cpu');
acc1 = mean(INS_outputPred(:) == categorical(test_INS_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));
figure
t1 = confusionchart(categorical(test_INS_output(:)),INS_outputPred(:));
Siehe auch
Kategorien
Mehr zu Sequence and Numeric Feature Data Workflows finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!