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

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])

convolution2dLayer(5,50)

reluLayer()

maxPooling2dLayer(2,'Stride',2)

fullyConnectedLayer(size(categories(trainAngle)))

softmaxLayer

classificationLayer];

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?

[MatlabUserN]

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.

Answer 1

[Salma Hassan]

hi sir did you find any solution for your problem , i have the same on