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how to normalize CNN-Data?

Asked by Osama Tabbakh on 9 Apr 2019
I got always NaN as output from my network and it might be possible that the network parameters diverge during training. Could somebody help me to fix this problem? This is my code:
X(:,:,3,60) = rand(500);
Y=randn(1,1,250000,60);
layers = [...
imageInputLayer([500 500 3])
convolution2dLayer(51,6)
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(20,9)
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(10,12)
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(6,12)
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
batchNormalizationLayer
convolution2dLayer(6,12)
reluLayer
maxPooling2dLayer(2,'Stride',2)
fullyConnectedLayer(250000)
regressionLayer;
];
options = trainingOptions('sgdm','InitialLearnRate',0.001, ...
'MiniBatchSize',miniBatchSize, ...
'MaxEpochs',15,'ExecutionEnvironment','cpu');
net = trainNetwork(X,Y,layers,options);

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