def create_model():
kernel = create_kernel()
model = gpflow.models.SVGP(
kernel=kernel,
likelihood=gpflow.likelihoods.Gaussian(variance_lower_bound=None),
inducing_variable=Data.Z,
q_diag=True,
)
set_trainable(model.q_mu, False)
return model
def create_kernel():
kern = gpflow.kernels.SquaredExponential(lengthscales=Data.ls,
variance=Data.var)
set_trainable(kern.lengthscales, False)
return kern