MATLAB: Spliting ground truth data into 70% for training, 30% for Validation

deep learningmachine learning

Hi
i want to know how to split Data ( training, validation) from ground truth file generated by ground truth labeler application i need for example 70% for training 30% for validation
This is in order to implement validationData in training option to get validation loose curve
in my code , i was able only to do TrainingData, but i cant make Validation to be avaliable for ValidationData in training option
Herein the code
load('gTruth.mat')
socialdistencedetection = selectLabels(gTruth,'cars');
if isfolder(fullfile('TrainingData'))
    cd TrainingData
else
    mkdir TrainingData
end 
addpath('TrainingData');
inputLayer = imageInputLayer([224 224 3],'Name','input','Normalization','none');
filterSize = [3 3];
middleLayers = [
    convolution2dLayer(filterSize, 16, 'Padding', 1,'Name','conv_1',...
    'WeightsInitializer','narrow-normal')
    batchNormalizationLayer('Name','BN1')
    reluLayer('Name','relu_1')
    maxPooling2dLayer(2, 'Stride',2,'Name','maxpool1')
    convolution2dLayer(filterSize, 32, 'Padding', 1,'Name', 'conv_2',...
    'WeightsInitializer','narrow-normal')
    batchNormalizationLayer('Name','BN2')
    reluLayer('Name','relu_2')
    maxPooling2dLayer(2, 'Stride',2,'Name','maxpool2')
    convolution2dLayer(filterSize, 64, 'Padding', 1,'Name','conv_3',...
    'WeightsInitializer','narrow-normal')
    batchNormalizationLayer('Name','BN3')
    reluLayer('Name','relu_3')
    maxPooling2dLayer(2, 'Stride',2,'Name','maxpool3')
    convolution2dLayer(filterSize, 128, 'Padding', 1,'Name','conv_4',...
    'WeightsInitializer','narrow-normal')
    batchNormalizationLayer('Name','BN4')
    reluLayer('Name','relu_4')
    maxPooling2dLayer(2, 'Stride',2,'Name','maxpoo4')
    convolution2dLayer(filterSize, 256, 'Padding', 1,'Name','conv_5',...
    'WeightsInitializer','narrow-normal')
    batchNormalizationLayer('Name','BN5')
    reluLayer('Name','relu_5')   
    ];
lgraph = layerGraph([inputLayer; middleLayers]);
imageSize = [224 224 3];
Anchors = [
     
  
   102    15
   170    29
   191    41
   122    29
    45    11
    96    21
   137    21
];
options = trainingOptions('sgdm', ...
        'InitialLearnRate',0.01, ...
        'Verbose',true,'MiniBatchSize',16,'L2Regularization',0.06,'MaxEpochs',80,...
        'Shuffle','every-epoch','VerboseFrequency',50, ...
        'DispatchInBackground',true,...
        'ExecutionEnvironment','auto','ValidationData',Validation);
trainingData = objectDetectorTrainingData(socialdistencedetection,'SamplingFactor',1,...
'WriteLocation','TrainingData');
numClasses = size(trainingData,2)-1;
lgraph = yolov2Layers([224 224 3],numClasses,Anchors,lgraph,'relu_5');
analyzeNetwork(lgraph);
[detectorYolo2, info] = trainYOLOv2ObjectDetector(trainingData,lgraph,options);    
save('detectorYolo2.mat','detectorYolo2');
% For Training Loss
x = 1:size(info.TrainingLoss,2);
y = info.TrainingLoss;
figure
plot(x,y)
title('Training Phase')
xlabel('Iteration')
ylabel('Mini Batch Training Loss')

Best Answer

Hi Abdussalam Elhanashi
It is my understand that you want to create a dataset from existing data for validation purpose. You may find the documentation Save and Load Parts of Variables in MAT-Files useful in creating new mat file which is a part of existing one.
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