How to change a subset of ANN weights while keep others weights unchanged?
I am using the neural network toolbox 2012a in my project. I have created a feed-forward-net with 2 layers(inputs are not counted as a layer as conventionalized in the users’ guide), and I want to update some of the input weights (IW{1,1}) while keep other input weights in IW{1,1} and the first-to-second-layer weights(LW{2,1}) fixed. To be short, I want to change a subset of IW{1,1} while remain all the other weights fixed. Let me refer this as my optimal goal here.
If the optimal goal is impossible, a sub-optimal goal is also acceptable. That is,update the entire IW{1,1} and keep the whole LW{2,1} fixed.
I already figured out how to achieve the sub-optimal goal. My solution is to use the command ‘adapt’ and set the learning rate to 0 for LW{2,1}. But I do not like this solution since ‘adapt’ is an over-simplified function lacking parameters and features(eg. min-grad, plotperform, etc.) of other training functions/algorithms(eg. trainlm,traingd,etc.) Therefore it is harder to control the training process and check on the results.
So, first, I want to know if there is a way to achieve the optimal goal rather than the sub-optimal.
Second, if the optimal goal is not possible (besides composing everything from scratch), I wonder if I can achieve the sub-optimal goal by taking advantage of some training functions instead of using ‘adpat’. I have already looked through ‘trainlm’ and ‘traingd’ but I do not think they are helpful to either of my goals.
I will really appreciate it if anyone can help me with this issue.
ANSWER
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First, let me clarify my train of thought. I was comparing training continuously using net.trainParam.epochs = 100 with training 10 consecutive times in a loop using net.trainParam.epochs = 10 ( or, say, 100 consecutive times in a loop using net.trainParam.epochs = 1). To eliminate complications, do not train with a validation set. For example, train candidates using net.divideFcn = ‘’. Then use a holdout validation set to choose the best designs.
There is a way to obtain the same result ( I am pretty sure that I did it yrs ago with the 2004 MATLAB 6.5 version of NEWFF). Given the same initial weights at epoch 0, the results will be the same at epoch 10. However, when the second example starts the 11th epoch, it has to call TRAIN again. When TRAIN starts again, it is not in the same state that it would have been in the 11th epoch of the continuous training example.
The task then is to quantify the state of TRAIN at the close of epoch 10 and to guarantee that it is in that state after it is called at the beginning of epoch 11.
Extending this strategy you can interrupt training at any time and assign your specified weights. However, now I understand that you would like some of those weights to remain fixed throughout further training.
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