How do I use trainnetwork() to retrain a pre-trained model?

Question

Pin Hsueh Chen 2022-10-6

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1818605-how-do-i-use-trainnetwork-to-retrain-a-pre-trained-model

评论： Pin Hsueh Chen 2022-10-14

How can I replace the decoder and regression layers in my pretrained CAE model with fully connected layers, softmax layers and classification layers to retrain the model into a classifier?

This is the model I created.

lgraph = layerGraph();
tempLayers = [
    imageInputLayer([224 224 3],"Name","imageinput")
    convolution2dLayer([3 3],256,"Name","conv_1","Padding","same","Stride",[2 2])
    reluLayer("Name","relu_1")
    maxPooling2dLayer([1 1],"Name","maxpoolForUnpool_3","HasUnpoolingOutputs",true,"Padding","same")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    convolution2dLayer([3 3],128,"Name","conv_2","Padding","same","Stride",[2 2])
    reluLayer("Name","relu_2")
    maxPooling2dLayer([1 1],"Name","maxpoolForUnpool_2","HasUnpoolingOutputs",true,"Padding","same")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    convolution2dLayer([3 3],64,"Name","conv_3","Padding","same","Stride",[2 2])
    reluLayer("Name","relu_3")
    maxPooling2dLayer([1 1],"Name","maxpoolForUnpool_1","HasUnpoolingOutputs",true,"Padding","same")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    transposedConv2dLayer([3 3],64,"Name","transposed-conv_1","Cropping","same")
    reluLayer("Name","relu_4")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxUnpooling2dLayer("Name","maxunpool_1")
    transposedConv2dLayer([3 3],128,"Name","transposed-conv_2","Cropping","same","Stride",[2 2])
    reluLayer("Name","relu_5")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxUnpooling2dLayer("Name","maxunpool_2")
    transposedConv2dLayer([3 3],256,"Name","transposed-conv_3","Cropping","same","Stride",[2 2])
    reluLayer("Name","relu_6")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxUnpooling2dLayer("Name","maxunpool_3")
    transposedConv2dLayer([3 3],3,"Name","transposed-conv_4","Cropping","same","Stride",[2 2])
    reluLayer("Name","relu_7")
    regressionLayer("Name","regressionoutput")];
lgraph = addLayers(lgraph,tempLayers);
% clean up helper variable
clear tempLayers;
lgraph = connectLayers(lgraph,"maxpoolForUnpool_3/out","conv_2");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_3/indices","maxunpool_3/indices");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_3/size","maxunpool_3/size");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_2/out","conv_3");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_2/indices","maxunpool_2/indices");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_2/size","maxunpool_2/size");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_1/out","transposed-conv_1");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_1/indices","maxunpool_1/indices");
lgraph = connectLayers(lgraph,"maxpoolForUnpool_1/size","maxunpool_1/size");
lgraph = connectLayers(lgraph,"relu_4","maxunpool_1/in");
lgraph = connectLayers(lgraph,"relu_5","maxunpool_2/in");
lgraph = connectLayers(lgraph,"relu_6","maxunpool_3/in");

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Rahul 2022-10-13

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1818605-how-do-i-use-trainnetwork-to-retrain-a-pre-trained-model#answer_1073438

Go the Apps --> Deep Network Designer --> Blank Network.

Once you create your network by dragging and dropping the layers and connecting them, click on Export --> Generate Code. This should create your model in a very simple way. If you are still unsure, please send the entire architecture, I will create the network for you.

5 个评论
显示 3更早的评论隐藏 3更早的评论

Rahul 2022-10-14

在 MATLAB Online 中打开

Below code is just the demo CNN architecture. You can refer this to build your own CNN architecture.

layers = [ ...
    imageInputLayer([28 28 1]) % image input layer
    convolution2dLayer(5,20) % 2D convolutional layer
    reluLayer("Name","relu1") % ReLU activation layer
    maxPooling2dLayer(2,'Stride',2) % 2D max pooling layer
    fullyConnectedLayer(2048,"Name","FC1") % Fully connected layer 1
    reluLayer("Name","relu2") % ReLU activation layer
    fullyConnectedLayer(1024,"Name","FC2") % Fully connected layer 2
    reluLayer("Name","relu3") % ReLU activation layer
    fullyConnectedLayer(10) % Fully connected layer 3 
    % (10 represented number of classes)
    softmaxLayer % Softmax activation layer to calculate class probability
    classificationLayer] 
% Classification layer to let the system know that it is a classification
% task.

Pin Hsueh Chen 2022-10-14

You may have misunderstood what I mean, I want to use the model trained at the beginning, the feature to extract the encoder part, and then use the feature to train a classifier, otherwise it will only be a general CNN, not a convolutional autoencoder.

请先登录，再进行评论。