Unrecognized function or variable 'GR_outputpredicted'. Error in testcnn (line 75) predictionError = test_GR_output - GR_outputpredicted;

Question

Nathaniel Porter 2022-2-22

0
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https://ww2.mathworks.cn/matlabcentral/answers/1655570-unrecognized-function-or-variable-gr_outputpredicted-error-in-testcnn-line-75-predictionerror

回答： Richard 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(18:22,:);
%Corresponding X(input) data to Y(output) data
val_GR_output = GR_output(18:22);
%reshaping data into 4D array
whos
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,1,1,5])); %Train data
%Separating and partioning for test data 15%
test_GlucoseReadings = GlucoseReadings_train(21:24,:);
%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
GR_ouputpredicted = 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--')