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Loss of Convergence in Iterative Loop (iter.00) Despite Initial Convergence Rates
While using DeepKS, I observed an anomaly regarding convergence rates during the initial iteration (iter. init) and subsequent iterative loop (iter.00).
During the first iteration (iter. init), the convergence rates were satisfactory with a training set rate of 0.77 and a testing set rate of 0.88.
However, upon entering the iterative loop (iter.00), the convergence rates dropped to 0, rendering further computation impossible.
Another user reported this issue as well.
Additional Context: Software: DeepKS, abacus 3.5.3 The DeePKS water_single example can run correctly on my machine.
Thank you so much for your help in addressing this issue.
Thanks for the question. This happens occationally when the first iteration is trained so "hard" that it overfits the data. I would suggest reduce the number of training epoch for first iteration.
I tried changing the value of n_epoch from 500 to 10, 100, but it seems that the situation does not change. Are there any other solutions?
To clarify, you may want to reduce the training length for the init train (iter.init), which is controlled by paramteres in init_train, like here in the example. I think the one you are changing is for the following iterations.
I changed it in params.yaml, like the file below. Do I truly understand? Or do I need to add a file named args.yaml? params.yaml.zip
You may need to use the args.yaml. You can check the iter.init folder to see if the training has been redone with reduced epochs.
I‘m sure that the training has been redone with a reduced epoch, but the convergence rate is still 0. In this case, is there a recommended n_epoch value?
You may try symmetrizing the descriptors via modifying the init_train block in params.yaml as suggested here. (See last part of params.yaml)
I have tried to modify the params.yaml as suggested, but it still has the same problem. I changed n_epoch and start_lr.