ebf2c8bb8e5f14d371d5052fafd3a0361fffae0b,doc/source/notebooks/advanced/varying_noise.pct.py,,,#,117

Before Change

    return - model.log_marginal_likelihood()

opt = gpflow.optimizers.Scipy()
opt.minimize(objective_closure,
             model.trainable_variables,
             options=dict(maxiter=ci_niter(1000)))

// %%
// let"s do some plotting!
xx = np.linspace(-5, 5, 200)[:, None]

After Change

set_trainable(model.q_mu, False)
set_trainable(model.q_sqrt, False)

for _ in range(ci_niter(1000)):
    natgrad.minimize(objective_closure, [(model.q_mu, model.q_sqrt)])
    adam.minimize(objective_closure, model.trainable_variables)

// %%
// let"s do some plotting!
xx = np.linspace(-5, 5, 200)[:, None]

mu, var = model.predict_f(xx)

Instances