layer_variational_gaussian_process: A Variational Gaussian Process Layer.
In tfprobability: Interface to 'TensorFlow Probability'

layer_variational_gaussian_process

R Documentation

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.