Trying to get 80% and greater accuracy from network. Can someone help in editing my code to reach to 80% or close too?

clc; clear all; close all;
load generated_data.mat
% 2289*180
% 6 classes
X1_T = X1';
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = categorical(Y1(ind));
% Split Data
X1_train = X1_T;
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
% Data Batch
XTrain=(reshape(train_X1', [2289,120]));
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,30]));
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,30]));
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)
    dropoutLayer(0.1)
    bilstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    instanceNormalizationLayer
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',150, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{XVal, val_Y1},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
% Train
net = trainNetwork(XTrain,train_Y1,layers,options);
% Test
miniBatchSize = 27;
YPred = classify(net,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
acc = mean(YPred(:) == categorical(test_Y1(:)))
figure
t = confusionchart(categorical(test_Y1(:)),YPred(:));

[KSSV]

An explanation about the problem will help us to help you.

[Nathaniel Porter]

My validation accuracy at highest(without the InstanceNormalizationLayer) is 67.77% but I am currently trying to improve it to roughly 80%. Just asking if my code be manipulated currently to achive this number ?

Answer 1

[yanqi liu]

yes，sir，may be use

clc; clear all; close all;
load generated_data.mat
% 2289*180
% 6 classes
rand('seed', 0)
X1_T = X1';
YC1 = categorical(Y1);
CS = categories(YC1);
train_index = []; val_index = []; test_index = [];
for i = 1 : length(CS)
    indi = find(YC1==CS{i});
    % Shuffling data
    indi = indi(randperm(length(indi)));    
    % 2/3---train, 1/6---val, 1/6---test
    index1 = round(length(indi)*2/3);
    index2 = round(length(indi)*(2/3+1/6));
    train_index = [train_index indi(1:index1)];
    val_index = [val_index indi(1+index1:index2)];
    test_index = [test_index indi(1+index2:end)];
end
ind = [train_index val_index test_index];
X1_T = X1_T(ind, :);
Y1 = categorical(Y1(ind));
% Split Data
X1_train = X1_T;
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
% Data Batch
XTrain=(reshape(train_X1', [2289,120]));
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,30]));
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,30]));
numFeatures = size(X1_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 500;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(Y1)));
layers = [ ...
    sequenceInputLayer(numFeatures)
    %dropoutLayer(0.5)
    instanceNormalizationLayer
    bilstmLayer(numHiddenUnits,'OutputMode','sequence')    
    %dropoutLayer(0.5)
    instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    instanceNormalizationLayer
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{XVal, val_Y1},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
% Train
net = trainNetwork(XTrain,train_Y1,layers,options);
% Test
miniBatchSize = 27;
YPred = classify(net,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
acc = mean(YPred(:) == categorical(test_Y1(:)))
figure
t = confusionchart(categorical(test_Y1(:)),YPred(:));

acc =

0.9667

>>

[Nathaniel Porter]

Hey I would like to know what was exactly done at the begining of the code with the 'for' loop?

[yanqi liu]

yes，sir，we know it is 6 classes，so just for every class，we choose 2/3、1/6、1/6 as train、val、test data，through this method，we can get the data split index