Programmatically determine which Deep Learning layer properties contain learnables
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In the Deep Learning Toolbox, there are a variety of layer object types. The display methods of these objects indicate which object properties contain learnable parameter data. For example, for LSTMLayer objects, the learnable parameters are stored in the properties "InputWeights", "RecurrentWeights", and "Bias" as shown below. My question is, is there a way, given a layer object, to programmatically determine the subset of its properties that are learnable?
layer = lstmLayer(100,'Name','lstm1')
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Pratyush Swain
il 14 Dic 2023
Hi Matt,
I understand you want to access the learnable properties of a deep learning layer object. There is no direct way fetch the trainable properties of a layer but a workaround can be to initialize a deep learning layer from the "lstm" layer object and then fetch its learnable parameters.
Please refer to the below example implementation:
% Define a lstm layer %
layer = lstmLayer(100,'Name','lstm1')
% Define a deep learning network %
net = dlnetwork(layer,Initialize=false);
% Retreive the learnable properties %
properties = net.Learnables;
% Display the learnable properties %
disp(properties)
As we can observe, the "dlnetwork" forms a single layer network named as "lstm1" and we have successfully retreived its learnable properties as a table.
For more information , please refer to:
Hope this helps.
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