How to improve the validation accuracy of the CNN network in deep learing ?
6 ビュー (過去 30 日間)
古いコメントを表示
How to increase the validation accuracy to more than 90%?
Used Layers and options are following:
layers = [
imageInputLayer([227 227 3],"Name","data")
convolution2dLayer([11 11],94,"Name","conv1","BiasLearnRateFactor",2,"Stride",[4 4])
reluLayer("Name","relu1")
crossChannelNormalizationLayer(5,"Name","norm1","K",1)
maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])
groupedConvolution2dLayer([5 5],94,2,"Name","conv2","BiasLearnRateFactor",2,"Padding",[2 2 2 2])
reluLayer("Name","relu2")
crossChannelNormalizationLayer(5,"Name","norm2","K",1)
maxPooling2dLayer([3 3],"Name","pool2","Stride",[2 2])
convolution2dLayer([3 3],94,"Name","conv3","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu3")
groupedConvolution2dLayer([2 2],64,2,"Name","conv4","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu4")
groupedConvolution2dLayer([3 3],128,2,"Name","conv5","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu5")
maxPooling2dLayer([3 3],"Name","pool5","Stride",[2 2])
fullyConnectedLayer(500,"Name","fc6","BiasLearnRateFactor",2)
reluLayer("Name","relu6")
dropoutLayer(0.5,"Name","drop6")
fullyConnectedLayer(500,"Name","fc7","BiasLearnRateFactor",2)
reluLayer("Name","relu7")
dropoutLayer(0.5,"Name","drop7")
fullyConnectedLayer(100,"Name","fc8","BiasLearnRateFactor",2)
fullyConnectedLayer(4,"Name","new fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)
softmaxLayer("Name","prob")
classificationLayer("Name","classoutput")];
miniBatchSize =25; % 128
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',36, ... %32
'MaxEpochs',10, ...
'InitialLearnRate',0.001, ... %0.01
'LearnRateDropFactor',0.1, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',valFrequency, ...
'ValidationPatience',4,'Verbose',false, ...
'Plots','training-progress');
0 件のコメント
回答 (1 件)
Mahesh Taparia
2020 年 12 月 14 日
Hi
By looking at the loss curve, it seems the loss is not saturated. So you can train with more epochs and check the performance. Also try with adam optimizer, it may improve the performance. Moreover, you can experiment with network architecture and hyperparameters to check if there can be some improvement. For example, add 1-2 more fully connected layers (after layer with 100 nodes). Hope it will help!
2 件のコメント
Mahesh Taparia
2020 年 12 月 14 日
By looking at this curve, it seems training and validation accuracy improved by 5% (approax). Train with more epochs as the curve is not saturated yet or try with other network architecture.
参考
カテゴリ
Help Center および File Exchange で Image Data Workflows についてさらに検索
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!