Trying reduce overfitting of training plot

Previoulsy tried running network with two sets of data however was not succesful. Achieved progres with running one per dataset however want to know how I can reduce any overfitting even though the validation accuracy seems good.

clc; clear all; close all;
%Import/Upload data
load Projectdata.mat
% change to label vector
CS1 = categories(categorical(INS_output));
Z2 = [];
for i = 1 : length(INS_output)
    Z2(i,1) = find(INS_output(i)==CS1);
end
Yo2 = INS_output;
INS_output = Z2;
%transposing insulin data
InsulinReadings_T = InsulinReadings';
rand('seed', 0)
ind = randperm(size(InsulinReadings_T, 1));
InsulinReadings_T = InsulinReadings_T(ind, :);
INS_output = INS_output(ind);
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:84);
InsulinReadingsTrain=(reshape(train_InsulinReadings',[1758,1,1,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,1,1,18]));
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
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',500, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{InsulinReadingsVal,val_INS_output,},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);

%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc1 = train_INS_output(:);
net2 = trainNetwork(InsulinReadingsTrain,yc1,layers,opts);
%% Compare against testing Data
INS_outputpredicted = predict(net2, InsulinReadingsTest)
predictionError = test_INS_output - INS_outputpredicted;
squares = predictionError.^2;
rmse = sqrt(mean(squares))
figure
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]

Why do you think there was a over fit?

[Nathaniel Porter]

Due to the significant gap found between the validation line and training line.

[Ive J]

Your train and validation sets should be non-overlapping, and if this is the case here (as should be), your model is protected against overfitting simply because train/validation sets never met each other.

Answer 1

[yanqi liu]

may be set dropoutLayer(value) to reduce more parameters during training

[Nathaniel Porter]

Is a 0.5 dropout layer value good or should increase and determine if it helps