as the document said:https://cn.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
'nndatasets','DigitDataset');
digitData = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
trainingNumFiles = 750;
rng(1)
[trainDigitData,testDigitData] = splitEachLabel(digitData, ...
trainingNumFiles,'randomize');
layers = [imageInputLayer([28 28 1])
convolution2dLayer(5,20)
reluLayer
maxPooling2dLayer(2,'Stride',2)
fullyConnectedLayer(10)
softmaxLayer
classificationLayer()];
options = trainingOptions('sgdm','MaxEpochs',15, ...
'InitialLearnRate',0.0001);
convnet = trainNetwork(trainDigitData,layers,options);
the data format of the image is 0¬255, but most cnn they trains with data [0,1] so before training, they should normalize the data by data/255.
but in this demo code, the data input of this function is only the index of the image. so I can't do anything to pre-processing the image before.
convnet = trainNetwork(trainDigitData,layers,options);
the only thing I can do to normalize the training data is in the layer
layers = [imageInputLayer([28 28 1])
but the only normalization option this Layer class can support is zero-center (data-mean of data).
what should I do when i want to normalize the input? Or there is no need for a cnn to normalize the training data?
