Trying to get validation and test accuracy on plot

Question

Nathaniel Porter 2021-12-16

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1611970-trying-to-get-validation-and-test-accuracy-on-plot

评论： Nathaniel Porter 2021-12-16

generated_data.mat

clc; clear all; close all;
%Import/Upload data
load generated_data.mat
Error using load
Unable to find file or directory 'generated_data.mat'.
%transposing glucose data
X1_T = X1';
%Shuffling data to take randomly
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training 70%
train_X1 = X1_train(1:120,:);
%Corresponding X(input) data to Y(output) data
train_Y1 = Y1(1:120);
%reshaping data into 4D array
XTrain=(reshape(train_X1, [120,1,1,2289]));
%Separating and partioning for validation data 15%
val_X1 = X1_train(121:150,:);
%Corresponding X(input) data to Y(output) data
val_Y1 = Y1(121:150);
%reshaping data into 4D array
XVal=(reshape(val_X1', [2289,1,1,30])); %Train data
%Separating and partioning for test data 15%
test_X1 = X1_train(151:180,:);
%Corresponding X(input) data to Y(output) data
test_Y1 = Y1(151:180);
%reshaping data into 4D array
XTest=(reshape(test_X1', [2289,1,1,30])); %Train data
%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)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',60, ...
    'GradientThreshold',2, ...
    'Verbose',false, ...
    'ValidationData',{XVal,val_Y1},...
    'TestData',{XTest,test_Y1},...
    'LearnRateDropFactor',0.2, ...
    'LearnRateDropPeriod',5, ...
    'Plots','training-progress');
net = trainNetwork(X1_train',categorical(Y1),layers,options);