how to normalize CNN-Data?

1 visualizzazione (ultimi 30 giorni)
Osama Tabbakh
Osama Tabbakh il 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);

Risposte (0)

Prodotti


Release

R2018b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by