How to increase the training and testing accuracy in CNN training?

5 vues (au cours des 30 derniers jours)
Ravish Raj
Ravish Raj le 20 Juin 2017
I am using MATLAB for CNN training. I have a data set of 27,000 images and angles corresponding to that images. My sample code is : %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
layers = [imageInputLayer([32 32 1])
options = trainingOptions('sgdm', 'MaxEpochs', 50,'InitialLearnRate', 0.0003);
convnet = trainNetwork(trainZ, trainAngle, layers,options);
% trainZ is my 4D matrix of images and trainAngle is 2D array of angles corresponding to images!
resultant_Train = classify(convnet,trainZ); %Training data
resultant_Valid = classify(convnet,validZ); %Validation data
My training accuracy is 70%
but test accuracy is only 2%;
I am completely blank what to do next. Do you have any suggestion? How can I improve my test accuracy?
Can someone also suggest how can i use adam in place of sgdm in optimizer?
  1 commentaire
MatlabUserN le 21 Juin 2017
Well increase the number of layers. minimum number of network layers should be 7. Make the network denser as the name suggest deep CNN. increase the number of epochs.

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Salma Hassan
Salma Hassan le 20 Nov 2017
hi sir did you find any solution for your problem , i have the same on


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