我想在MatlabResNet50中添加正则化(L2)
我正在使用CNN中的Resnet50训练我的数据,但数据拟合过度。我想减少过度装修。所以我想添加正则化L2。有人能告诉我如何在代码中添加L2吗?你可以在下面看到我的代码我想在MatlabResNet50中添加正则化(L2),matlab,machine-learning,image-processing,deep-learning,conv-neural-network,Matlab,Machine Learning,Image Processing,Deep Learning,Conv Neural Network,我正在使用CNN中的Resnet50训练我的数据,但数据拟合过度。我想减少过度装修。所以我想添加正则化L2。有人能告诉我如何在代码中添加L2吗?你可以在下面看到我的代码 clear all close all imds = imageDatastore("E:\test\data", ... 'IncludeSubfolders',true,'LabelSource','foldernames'); [imdsTrain,imdsValidation] = splitEachLab
clear all
close all
imds = imageDatastore("E:\test\data", ...
'IncludeSubfolders',true,'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize'); %70% for train 30% for test
net=resnet50; % for the first time,you have to download the package from Add-on explorer
%Replace Final Layers
numClasses = numel(categories(imdsTrain.Labels));
lgraph = layerGraph(net);
newFCLayer = fullyConnectedLayer(numClasses,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);
lgraph = replaceLayer(lgraph,'fc1000' ,newFCLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'ClassificationLayer_predictions',newClassLayer);
%Train Network
inputSize = net.Layers(1).InputSize;
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',20, ...
'InitialLearnRate',1e-3, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',5, ...
'Verbose',false, ...
'Plots','training-progress');
trainedNet = trainNetwork(augimdsTrain,lgraph,options);
YPred = classify(trainedNet,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels)
C = confusionmat(imdsValidation.Labels,YPred)
cm = confusionchart(imdsValidation.Labels,YPred);
cm.Title = 'Confusion Matrix for Validation Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
在ResNet50 CNN模型上实现L2正则化:
resnet_base = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))
alpha = 0.00002
for layer in resnet_base.layers:
if isinstance(layer, keras.layers.Conv2D) or isinstance(layer, keras.layers.Dense):
layer.add_loss(keras.regularizers.l2(alpha)(layer.kernel))
if hasattr(layer, 'bias_regularizer') and layer.use_bias:
layer.add_loss(keras.regularizers.l2(alpha)(layer.bias))
希望这有帮助 在ResNet50 CNN模型上实现L2正则化:
resnet_base = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))
alpha = 0.00002
for layer in resnet_base.layers:
if isinstance(layer, keras.layers.Conv2D) or isinstance(layer, keras.layers.Dense):
layer.add_loss(keras.regularizers.l2(alpha)(layer.kernel))
if hasattr(layer, 'bias_regularizer') and layer.use_bias:
layer.add_loss(keras.regularizers.l2(alpha)(layer.bias))
希望这有帮助 下一次,请花一分钟来看看如何正确格式化您的代码(这次为您完成了)。非常感谢您的编辑。下次我会处理好的。你能回答我的问题吗?下一次,请花一分钟看看如何正确格式化你的代码(这次为你完成了)。非常感谢你的编辑。下次我会处理好的。你能回答我的问题吗。