def test_monte_carlo_2_din(white, mean):
kernel = gpflow.kernels.SquaredExponential(variance=rng.rand())
mean_function = mean_function_factory(mean, DataMC2.D_in, DataMC2.D_out)
model = MomentMatchingSVGP(kernel,
gpflow.likelihoods.Gaussian(),
num_latent=DataMC2.D_out,
mean_function=mean_function,
inducing_variable=DataMC2.X.copy(),
whiten=white)
model.full_output_cov = True
@tf.function
def closure():
return -model.log_marginal_likelihood(DataMC2.data)
training_loop(closure,
optimizer=tf.optimizers.Adam(),
var_list=model.trainable_variables,
maxiter=100)
mean1, var1 = model.uncertain_predict_f_moment_matching(
*map(tf.convert_to_tensor, [DataMC2.Xnew_mu, DataMC2.Xnew_covar]))
for n in range(DataMC2.N_new):
mean2, var2 = model.uncertain_predict_f_monte_carlo(
DataMC2.Xnew_mu[n, ...], DataMC2.L[n, ...])
assert_allclose(mean1[n, ...], mean2, atol=1e-2)
assert_allclose(var1[n, ...], var2, atol=1e-2)
def test_monte_carlo_1_din(white, mean):
kernel = gpflow.kernels.SquaredExponential(variance=rng.rand())
mean_function = mean_function_factory(mean, DataMC1.D_in, DataMC1.D_out)
model = MomentMatchingSVGP(
kernel,
gpflow.likelihoods.Gaussian(),
num_latent_gps=DataMC1.D_out,
mean_function=mean_function,
inducing_variable=DataMC1.X.copy(),
whiten=white,
)
model.full_output_cov = True
training_loop(
model.training_loss_closure(DataMC1.data),
optimizer=tf.optimizers.Adam(),
var_list=model.trainable_variables,
maxiter=200,
compile=True,
)
mean1, var1 = model.uncertain_predict_f_moment_matching(
*map(tf.convert_to_tensor, [DataMC1.Xnew_mu, DataMC1.Xnew_covar])
)
for n in range(DataMC1.N_new):
mean2, var2 = model.uncertain_predict_f_monte_carlo(
DataMC1.Xnew_mu[n, ...], DataMC1.Xnew_covar[n, ...] ** 0.5
)
assert_allclose(mean1[n, ...], mean2, atol=1e-3, rtol=1e-1)
assert_allclose(var1[n, ...], var2, atol=1e-2, rtol=1e-1)
def test_no_uncertainty(white, mean):
mean_function = mean_function_factory(mean, Data.D_in, Data.D_out)
kernel = gpflow.kernels.SquaredExponential(variance=rng.rand())
model = MomentMatchingSVGP(
kernel,
gpflow.likelihoods.Gaussian(),
num_latent_gps=Data.D_out,
mean_function=mean_function,
inducing_variable=Data.X.copy(),
whiten=white,
)
model.full_output_cov = False
training_loop(
model.training_loss_closure(Data.data),
optimizer=tf.optimizers.Adam(),
var_list=model.trainable_variables,
maxiter=100,
compile=True,
)
mean1, var1 = model.predict_f(Data.Xnew_mu)
mean2, var2 = model.uncertain_predict_f_moment_matching(
*map(tf.convert_to_tensor, [Data.Xnew_mu, Data.Xnew_covar])
)
assert_allclose(mean1, mean2)
for n in range(Data.N_new):
assert_allclose(var1[n, :], var2[n, ...])
def test_training_loop_converges():
m = create_model()
mref = create_model()
gpflow.optimizers.Scipy().minimize(mref.training_loss,
mref.trainable_variables)
training_loop(
m.training_loss,
tf.optimizers.Adam(learning_rate=0.01),
m.trainable_variables,
maxiter=5000,
compile=True,
)
assert_models_close(m, mref, rtol=1e-5)
def test_training_loop_compiles():
m1 = create_model()
m2 = create_model()
training_loop(m1.training_loss,
tf.optimizers.Adam(),
m1.trainable_variables,
maxiter=50,
compile=True)
training_loop(m2.training_loss,
tf.optimizers.Adam(),
m2.trainable_variables,
maxiter=50,
compile=False)
assert_models_close(m1, m2)
