Normalizing input data for DeepLearning Trainer interface takes very long time
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Hey guys,
trying to training a network on R2022a with the help of network trainer, on which a customed datastore is set as training input. The ds has ~50000 observations(~0.6MB each) with some simple augmentation methods integrated.
The problem is, it takes a very long time(30min or so) stucking in "normalizing input data" stage every time after I start training progress. Why is that? How to improve it?
回答（2 个）
Richard
2022-12-7
0 个投票
You can set the ResetInputNormalization training option to false to prevent the input statistics being recomputed every time. You will need to provide an input layer which has the appropriate values set - if you have an output network from an earlier training attempt then you can extract its input layer and put it in place of the input layer in the layer graph you are training.
This is how to do it for the code example that is in the trainNetwork help:
[XTrain, YTrain] = digitTrain4DArrayData;
layers = [
imageInputLayer([28 28 1], 'Name', 'input')
convolution2dLayer(5, 20, 'Name', 'conv_1')
reluLayer('Name', 'relu_1')
convolution2dLayer(3, 20, 'Padding', 1, 'Name', 'conv_2')
reluLayer('Name', 'relu_2')
convolution2dLayer(3, 20, 'Padding', 1, 'Name', 'conv_3')
reluLayer('Name', 'relu_3')
additionLayer(2,'Name', 'add')
fullyConnectedLayer(10, 'Name', 'fc')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'classoutput')];
lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph, 'relu_1', 'add/in2');
% Perform a single epoch of training which will initialize the input layer
initOptions = trainingOptions('sgdm', 'Plots', 'training-progress', 'MaxEpochs', 1);
[net,info] = trainNetwork(XTrain, YTrain, lgraph, initOptions);
% Transfer the initialized input layer to the untrained layergraph
input = net.Layers(1);
lgraph = replaceLayer(lgraph, input.Name, input);
% Perform training without reinitializing the input layer
options = trainingOptions('sgdm', 'Plots', 'training-progress', 'ResetInputNormalization', false);
[net,info] = trainNetwork(XTrain, YTrain, lgraph, options);
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