Emmitt asked . 2021-06-10

Deep Learning NNet accuracy doesn't looks good

digitalDatasetPath = fullfile('D:\MatLab2020\DeeplearningCNN\test');

imdsTrain = imageDatastore(digitalDatasetPath, ...
    'IncludeSubfolders', true,'FileExtensions','.jpeg','LabelSource','foldernames');
% set training dataset folder

% set validation dataset folder
validationPath = fullfile('D:\MatLab2020\DeeplearningCNN\train');

imdsValidation = imageDatastore(validationPath, ...
    'IncludeSubfolders',true,'FileExtensions','.jpeg','LabelSource','foldernames');

% create a clipped ReLu layer
layer = clippedReluLayer(10,'Name','clip1');

% define network architecture
layers = [
    %imageInputLayer([240 320 3], 'Normalization', 'none')
    imageInputLayer([300 300 3])
    % conv_1
    %convolution2dLayer(5,20,'Stride',1)
    convolution2dLayer(5,24)
    %batchNormalizationLayer
    %clippedReluLayer(10);
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    
    % fc layer
    fullyConnectedLayer(1)
    softmaxLayer
    classificationLayer];

% specify training option("adam_&_sgdm")
%options = trainingOptions('sgdm', ...
%    'MaxEpochs',20, ...
%    'InitialLearnRate',0.0001, ...
%    'MiniBatchSize',32, ...
%    'Shuffle','every-epoch', ...
%    'ValidationData',imdsValidation, ...
%    'ValidationFrequency',30, ...
%    'Verbose',false, ...
%    'Plots','training-progress');
options = trainingOptions('sgdm', ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Verbose', false, ...
    'Plots','training-progress')

% train network using training data
net = trainNetwork(imdsTrain,layers,options);

% classify validation images and compute accuracy
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation)  

matlab , signal , signal processing