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

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?

Answer 1

[Von Duesenberg]

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();

[Jack Xiao]

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) ;