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I tried the save/load function. On loading my model is showing very high variance in the DGP predictions. I used aep.SDGPR function to train my model and saved it in pickle format.
On loading the model in a different file, I am using the following commands
model = aep.SDGPR(X, y_train_normalized, M,hidden_size, lik='Gaussian')
model.load_model(fname = 'GeePeeModel/GBE-DGPModelHS1.pickle')
The above command results in a model with high GP variance.
Is saving/loading just the hyperparameters/parameters by calling get_hypers()/update_hypers() enough?
also create an example for doing so
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