Function test_vgp_multiclass

tests/integration/test_dynamic_shapes.py:120–151

()

def test_vgp_multiclass() -> None:
    X = tf.Variable(
        tf.zeros((1, Datum.n_inputs), dtype=default_float()), shape=(None, None), trainable=False
    )
    Yc = tf.Variable(
        tf.zeros((1, Datum.n_outputs_c), dtype=default_float()), shape=(None, None), trainable=False
    )

    num_classes = 3
    model = gpflow.models.VGP(
        (X, Yc),
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.MultiClass(num_classes=num_classes),
        num_latent_gps=num_classes,
    )

    @tf.function
    def model_closure() -> tf.Tensor:
        return -model.elbo()

    model_closure()  # Trigger compilation.

    gpflow.models.vgp.update_vgp_data(model, (Datum.X, Datum.Yc))
    opt = gpflow.optimizers.Scipy()

    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )


def test_svgp_multiclass() -> None: