how to copy layers and connections from an existing neural network?

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Jack Xiao
Jack Xiao am 11 Jul. 2018
Bearbeitet: Jack Xiao am 12 Jul. 2018
just installed the latest version and find the support for deep learning is better and better. here, i have a question. I want to create a new network, but i do not want to write from scratch. take googlenet for example, I want to create a new network which can be a little like googlenet. but not all the same. maybe I need to refer some layers or structure. so how to copy layers and connections from an existing neural network?

Antworten (1)

Von Duesenberg
Von Duesenberg am 11 Jul. 2018
Something along the lines (I had an exemple withe Alexnet, but the basic principle should be identical; here, I just resize the input layers because I have gray, not RGB, images, and I have 45 classes):
net = alexnet;
layers = net.Layers;
layers(1) = imageInputLayer([227, 227,1]);
layers(2) = convolution2dLayer([11, 11], 96,'Padding',0, 'Stride', 4, 'BiasLearnRateFactor',2);
layers(23) = fullyConnectedLayer(45);
layers(24) = softmaxLayer();
layers(25) = classificationLayer();
  1 Kommentar
Jack Xiao
Jack Xiao am 12 Jul. 2018
Bearbeitet: Jack Xiao am 12 Jul. 2018
thanks, I make a mistake previously. my copy operation makes a struct not a Layer. maybe it is a common operation in matlab. take " layersKept = net.Layers(1:25) ; " for example, it is for copying. and for connection copy: connectionKept = net.Connections(1:27,1:2) ;

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