I want to train a network and save the weights. Then use a new data set and resume training with the weights from the previous data set.
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It seems train always randomizes the weights when called. I can't seem to maintain the weights from the first training set.
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Accepted Answer
Sonam Gupta
on 28 Mar 2018
You can continue the training from weights obtained by previous data set by extracting the layers from the network's "Layers" property, and then passing it to "trainNetwork", as follows:
if true
% Train a network
net = trainNetwork(XTrain, YTrain, layers, options);
% Extract layers from the trained network
newLayers = net.Layers;
% Retrain the network, but start from where we left off
newNet = trainNetwork(XTrain, YTrain, newLayers, options);
'trainNetwork' will always use the weights that are stored in the layers which you pass in for training.
3 Comments
Moh. Saadat
on 29 Aug 2022
There is a small caveat to this: check that whether your output 'net' is a LayerGraph or a DAGNetwork. If it is not a LayerGraph, use layerGraph(net) instead of net.Layers.
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