Avoid training certain neurons
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Using the Deep Learning Toolbox, I wish to construct a simple feed-forward network for a simulation, however assume I have already trained one of the hidden neurons (out of several) with the correct weights and biases and I don't want them to change during training. How can I make this single specific neuron be "constant" and not get retrained with new wights and biases while the rest of the network is being trained?
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Hiro Yoshino
2019-12-23
There is an option to keep specific layers' learning rates low so you can fix them as they are.
for example
fullyConnectedLayer(<outputsize>, 'WeightLearnRateFactor', 0, 'BiasLearnRateFactor', 0)
This way, you would multiply zero to the global learning rate, which is set via trainingOptions function, and thus the learning rates of the weights in the fully-connected-layer are set as zero.
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