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Consider initiating generator with synthesizer #4
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Both have advantages. With the parameters in fit, you can more easily change out the training parameters and try different things. But your point is very valid as well. |
I like the idea behind your suggestion, @kevinykuo , of moving the However, doing this is a bit more complex than it looks, because the Generator instance needs to be passed the This means that we cannot simply move the creation of this instance to the One option would be still create all the model instances inside the |
Got it, sounds like there a couple ways to proceed, dictated by what you think a "model" represents, i.e., if it should be identified with the metadata of a dataset.
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This is now mostly solved, correct? I think this can be closed. |
Not too familiar with pytorch so let me know if this makes sense...
It seems like we're instantiating the model each time
fit()
is called https://github.com/DAI-Lab/CTGAN/blob/7aa29685045ffdba84bd87432354c133e05699e6/ctgan/ctgan_model.py#L458-L465Would it make sense to do this once when we instantiate
CTGANSynthesizer
so we can e.g. look at the behavior of generated data as we train for more epochs?The text was updated successfully, but these errors were encountered: