Validation Accuracy on Neural network
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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](https://www.mathworks.com/matlabcentral/answers/uploaded_files/204659/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 Comments
Answers (4)
Andrik Rampun
on 19 Feb 2019
18 Comments
Don Mathis
on 26 Feb 2019
Yes, MiniBatchSize. And I meant the outputSize of your fullyConnectedLayers could be reduced to something smaller than 3136:
fullyConnectedLayer(3136)
Saira
on 15 Jun 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);
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Sevda Kemba
on 6 Jun 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.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1023145/image.png)
0 Comments
Sevda Kemba
on 6 Jun 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.
0 Comments
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