How can I only train replacing layer in transfer learning rather than re-training all network?

This website give an example how to realize the transfer learning https://uk.mathworks.com/help/deeplearning/examples/transfer-learning-using-alexnet.html;jsessionid=b3db20d33f3d4b2dec586eb40cd9 but there is a problem, it will re-training all network of transfer learning, it would take a lot of time to train it. If we can only train the replace layer and reserve the transfer layer, it would cost low energy. layers = [ layersTransfer fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20) softmaxLayer classificationLayer]; where we can see the layersTransfer layer is the transfer layer and the parameters of it have been trained. If I can transfer these layerstransfer's parameters and only train the other 3 new layers, it may save a lot of time. How can I do it? I know there is a activation function could extract features from any layer of CNN, how can I use these features extracted from layersTansfer and train the 3 new layers, it may be feasible. How can I realize it?

Thank you for your answer!