loading and training an existing network.

2024 10 7
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更新时间：2024 10 8
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I am trying define a network, then train it in multiple sessions. The problem is that I can't get the load or read of the network to work in the second session. The code is:
layers = [ ...
sequenceInputLayer(270)
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer]
options = trainingOptions("adam", ...
InitialLearnRate=0.002,...
MaxEpochs=15, ...
Shuffle="never", ...
GradientThreshold=1, ...
Verbose=false, ...
ExecutionEnvironment="gpu", ...
Plots="training-progress");
clabels=categorical(labels);
numLables=numel(clabels)
load("savednet.mat","layers");
net = trainNetwork(data,clabels,layers,options);
save("savednet","net");
I have tried many variations of the load command and it always gives an error on the second argument:
Warning: Variable 'layers' not found.
Exactly what should that look like and then how should it be used as input to the trainNetwork routine?

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Walter Roberson
Walter Roberson 2024-10-7
layers = [ ...
sequenceInputLayer(270)
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer]
You define a variable named layers
load("savednet.mat","layers");
You try to overwrite the variable named layers with the content of the variable layers stored in savednet.mat but that variable does not exist in that .mat
save("savednet","net");
Notice you do not save layers in savednet.mat
Mark Hubelbank
Mark Hubelbank 2024-10-7
Thank you for the quick reply,
Then two questions. What should the save call look like? I had copied this from anothers reply. Then exactly what should the load command look like? It is very unclear what is being saved in the savenet.mat file.
Walter Roberson
Walter Roberson 2024-10-7
Remove
load("savednet.mat","layers");
Change
save("savednet","net");
to
save("savednet","net","layers");
Afterwards, to do additional work on the saved network,
load("savednet","net","layers");
Mark Hubelbank
Mark Hubelbank 2024-10-7
I see says the blind man!! This seems very logical. None of the examples that I could find appeared so clean.
Still, if I may dig into this a bit more, then the the call:
net = trainNetwork(data,clabels,layers,options);
would appear to be using generating the network from scratch each time. What tells it to use the read in network as the starting point?
Walter Roberson
Walter Roberson 2024-10-7
filename = "savednet.mat";
if isfile(filename)
clear layers net
try
load(filename, "net", "layers");
catch ME
end
end
if ~exist("layers", "var") || ~exist("net", "var")
layers = [ ...
sequenceInputLayer(270)
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer]
options = trainingOptions("adam", ...
InitialLearnRate=0.002,...
MaxEpochs=15, ...
Shuffle="never", ...
GradientThreshold=1, ...
Verbose=false, ...
ExecutionEnvironment="gpu", ...
Plots="training-progress");
clabels=categorical(labels);
numLables=numel(clabels);
net = trainNetwork(data,clabels,layers,options);
save(filename, "net", "layers");
end
The above code loads layers and net from the file if possible, and if that fails then it creates and trains the network and saves it.
Mark Hubelbank
Mark Hubelbank 2024-10-7
移动：Walter Roberson 2024-10-7
Perhaps I don't understand how one can train in stages then. The idea is that the training will be continued in the second and subsequent sessions. Sort of a continuing transfer learning. The idea is that over time the network keeps improving. perhaps I should be using trainnet instead of trainnetwork. Then it would appear the call is:
load(filename,"net1","layers");
net=trainnet(data,clabels,net1,"crossentropy",options);
Is this the correct direction?
Walter Roberson
Walter Roberson 2024-10-7
Probably
net1 = trainnet(data,clabels,net1,"crossentropy",options);

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Matt J
Matt J 2024-10-7

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previous = load("savednet","net","layers");
net = trainNetwork(data,clabels,previous.net,options);

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Matt J
Matt J 2024-10-7
编辑：Matt J 2024-10-8
perhaps I should be using trainnet instead of trainnetwork.
It would be better, since trainnet is newer and has more flexibility. However, it won't make a difference as far as how to resume the training of a pre-existing network..

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