layer_variational_gaussian_process {tfprobability}

A Variational Gaussian Process Layer.

Description

Create a Variational Gaussian Process distribution whose index_points are the inputs to the layer. Parameterized by number of inducing points and a kernel_provider, which should be a tf.keras.Layer with an @property that late-binds variable parameters to a tfp.positive_semidefinite_kernel.PositiveSemidefiniteKernel instance (this requirement has to do with the way that variables must be created in a keras model). The mean_fn is an optional argument which, if omitted, will be automatically configured to be a constant function with trainable variable output.

Usage

layer_variational_gaussian_process(
  object,
  num_inducing_points,
  kernel_provider,
  event_shape = 1,
  inducing_index_points_initializer = NULL,
  unconstrained_observation_noise_variance_initializer = NULL,
  mean_fn = NULL,
  jitter = 1e-06,
  name = NULL
)

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

num_inducing_points

number of inducing points in the Variational Gaussian Process distribution.

kernel_provider

a Layer instance equipped with an ⁠@property⁠, which yields a PositiveSemidefiniteKernel instance. The latter is used to parametrize the constructed Variational Gaussian Process distribution returned by calling the layer.

event_shape

the shape of the output of the layer. This translates to a batch of underlying Variational Gaussian Process distributions. For example, event_shape = 3 means we are modelling a batch of 3 distributions over functions. We can think oof this as a distribution over 3-dimensional veector-valued functions.

inducing_index_points_initializer

a tf.keras.initializer.Initializer used to initialize the trainable ⁠inducing_index_points variables⁠. Training VGP's is pretty sensitive to choice of initial inducing index point locations. A reasonable heuristic is to scatter them near the data, not too close to each other.

unconstrained_observation_noise_variance_initializer

a tf.keras.initializer.Initializer used to initialize the unconstrained observation noise variable. The observation noise variance is computed from this variable via the tf.nn.softplus function.

mean_fn

a callable that maps layer inputs to mean function values. Passed to the mean_fn parameter of Variational Gaussian Process distribution. If omitted, defaults to a constant function with trainable variable value.

jitter

a small term added to the diagonal of various kernel matrices for numerical stability.

name

name to give to this layer and the scope of ops and variables it contains.

Value

a Keras layer