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Validation Accuracy on Neural network

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Andrik Rampun
Andrik Rampun el 18 de Feb. de 2019
Respondida: Sevda Kemba el 6 de Jun. de 2022
Hello..I wonder if any of you who have used deep learning on matlab can help me to troubleshoot my problem. I don't understand why I got a sudden drop of my validation accuracy at the end of the graph? It's a simple network with one convolution layer to classify cases with low or high risk of having breast cancer. After the final iteration it displays a validation accuracy of above 80% but then suddenly it dropped to 73% without an iteration. I don't understand that.
matlab_per2.png
Here's my code
%set training dataset folder
digitDatasetPath = fullfile('C:\Users\UOS\Documents\Desiree Data\Run
2\dataBreast\training2');
%training set
imdsTrain = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
%set validation dataset folder
validationPath = fullfile('C:\Users\UOS\Documents\Desiree Data\Run
2\dataBreast\validation2');
%testing set
imdsValidation = imageDatastore(validationPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
%create a clipped ReLu layer
layer = clippedReluLayer(10,'Name','clip1');
% define network architecture
layers = [
imageInputLayer([256 256 1]);
% conv_1
convolution2dLayer(3,32,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
%fc
fullyConnectedLayer(100)
dropoutLayer(0.7,'Name','drop1');
%fc
fullyConnectedLayer(25)
dropoutLayer(0.8,'Name','drop2');
% fc layer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
% specify training option
options = trainingOptions('adam', ...
'InitialLearnRate',0.001, ...
'MaxEpochs',15, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'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;
%calculate accuracy
accuracy = sum(YPred == YValidation)/numel(YValidation);
  8 comentarios
Sridharan K
Sridharan K el 10 de Mzo. de 2021
i got 100% accuracy. thanks for this program.
Santhosh Surya Kiran
Santhosh Surya Kiran el 1 de Jul. de 2021
What u got is wrong..

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Respuestas (4)

Andrik Rampun
Andrik Rampun el 19 de Feb. de 2019
Some updates. I got similar results (sudden drop) at the end of the graph. I find this really strange.
% set training dataset folder
digitDatasetPath = fullfile('C:\Users\UOS\Documents\Desiree Data\Run 2\dataBreast\training2');
imdsTrain = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
% set validation dataset folder
validationPath = fullfile('C:\Users\UOS\Documents\Desiree Data\Run 2\dataBreast\validation2');
imdsValidation = imageDatastore(validationPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
% create a clipped ReLu layer
layer = clippedReluLayer(10,'Name','clip1');
% define network architecture
layers = [
imageInputLayer([256 256 1], 'Normalization', 'none')
% conv_1
convolution2dLayer(3,16,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% conv_2
convolution2dLayer(3,16,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% conv_3
convolution2dLayer(3,32,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% conv_4
convolution2dLayer(3,64,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% conv_5
convolution2dLayer(3,128,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% conv_6
convolution2dLayer(3,256,'Stride',1)
batchNormalizationLayer
clippedReluLayer(10);
maxPooling2dLayer(2,'Stride',2)
% fc5
fullyConnectedLayer(500)
dropoutLayer(0.5,'Name','drop1');
% fc5
fullyConnectedLayer(250)
dropoutLayer(0.5,'Name','drop2');
% fc5
fullyConnectedLayer(50)
dropoutLayer(0.5,'Name','drop3');
% fc layer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
% soecify training option
options = trainingOptions('adam', ...
'InitialLearnRate',0.001, ...
'MaxEpochs',30, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'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)
%YPred = predict(net,X);
analyzeNetwork(net)
matlab_per3.png
  18 comentarios
Andrik Rampun
Andrik Rampun el 26 de Feb. de 2019
Hi Don,
When you said shirink batchsize do you mean my MinibatchSize?
Another question is I can even reduce my hidden units in my FC layer?
Thanks
Don Mathis
Don Mathis el 26 de Feb. de 2019
Yes, MiniBatchSize. And I meant the outputSize of your fullyConnectedLayers could be reduced to something smaller than 3136:
fullyConnectedLayer(3136)

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Saira
Saira el 15 de Jun. de 2020
Hi,
I have 5600 training images. I have extracted features using Principal Component Analysis (PCA). Then I am applying CNN on extracted features. My training accuracy is 30%. How to increase training accuracy?
Feature column vector size: 640*1
My training code:
% Convolutional neural network architecture
layers = [
imageInputLayer([1 640 1]);
reluLayer
fullyConnectedLayer(7);
softmaxLayer();
classificationLayer()];
options = trainingOptions('sgdm', 'Momentum',0.95, 'InitialLearnRate',0.0001, 'L2Regularization', 1e-4, 'MaxEpochs',5000, 'MiniBatchSize',8192, 'Verbose', true);

Sevda Kemba
Sevda Kemba el 6 de Jun. de 2022
@Andrik Rampun Hello. In Matlab, we load the data set with code and limit it in deep learning. But when we train, validation accuracy stays between 40-50%. What can we do to increase it to 90%? We would be very happy if you could help.

Sevda Kemba
Sevda Kemba el 6 de Jun. de 2022
@Saira Hello. In Matlab, we load the data set with code and limit it in deep learning. But when we train, validation accuracy stays between 40-50%. What can we do to increase it to 90%? We would be very happy if you could help.

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