gpflux.layers.latent_variable_layer

This module implements a latent variable layer for deep GPs.

Module Contents

class TrackableLayer

Bases: gpflow.keras.tf_keras.layers.Layer

With the release of TensorFlow 2.5, our TrackableLayer workaround is no longer needed. See Prowler-io/gpflux#189. Will be removed in GPflux version 1.0.0

ObservationType

Type for the [inputs, targets] list used by LayerWithObservations

class LayerWithObservations

Bases: gpflux.layers.trackable_layer.TrackableLayer

By inheriting from this class, Layers indicate that their call() method takes a second observations argument after the customary layer_inputs argument.

This is used to distinguish which layers (unlike most standard Keras layers) require the original inputs and/or targets during training. For example, it is used by the amortized variational inference in the LatentVariableLayer.

abstract call(layer_inputs: gpflow.base.TensorType, observations: gpflux.types.ObservationType | None = None, training: bool | None = None) → tf.Tensor

The call() method of LayerWithObservations subclasses should accept a second argument, observations. In training mode, this will be the [inputs, targets] of the training points; otherwise, it is None.

class LatentVariableLayer(prior: tfp.distributions.Distribution, encoder: gpflow.keras.tf_keras.layers.Layer, compositor: gpflow.keras.tf_keras.layers.Layer | None = None, name: str | None = None)

Bases: LayerWithObservations

A latent variable layer, with amortized mean-field variational inference.

The latent variable is distribution-agnostic, but assumes a variational posterior that is fully factorised and is of the same distribution family as the prior.

This class is used by models as described in [DSHD18, SDHD19].

Parameters:

• prior – A distribution that represents the prior over the latent variable.

• encoder – A layer which is passed the concatenated observation inputs and targets, and returns the appropriate parameters for the approximate posterior distribution; see encoder.

• compositor – A layer that combines layer inputs and latent variable samples into a single tensor; see compositor. If you do not specify a value for this parameter, the default is tf.keras.layers.Concatenate(axis=-1, dtype=default_float()). Note that you should set dtype of the layer to GPflow’s default dtype as in default_float().

• name – The name of this layer (passed through to tf.keras.layers.Layer).

prior: tfp.distributions.Distribution

The prior distribution for the latent variables.

encoder: gpflow.keras.tf_keras.layers.Layer

An encoder that maps from a concatenation of inputs and targets to the parameters of the approximate posterior distribution of the corresponding latent variables.

compositor: gpflow.keras.tf_keras.layers.Layer

A layer that takes as input the two-element [layer_inputs, latent_variable_samples] list and combines the elements into a single output tensor.

call(layer_inputs: gpflow.base.TensorType, observations: gpflux.types.ObservationType | None = None, training: bool | None = None, seed: int | None = None) → tf.Tensor

Sample the latent variables and compose them with the layer input.

When training, draw a sample of the latent variable from the posterior, whose distribution is parameterised by the encoder mapping from the data. Also add a KL divergence [posterior∥prior] to the losses.

When not training, draw a sample of the latent variable from the prior.

Parameters:

• layer_inputs – The output of the previous layer.

• observations – The [inputs, targets], with the shapes [batch size, Din] and [batch size, Dout] respectively. This parameter should be passed only when in training mode.

• training – The training mode indicator.

• seed – A random seed for the sampling operation.

Returns:

Samples of the latent variable composed with the layer inputs through the compositor

_inference_posteriors(observations: gpflux.types.ObservationType, training: bool | None = None) → tfp.distributions.Distribution

Return the posterior distributions parametrised by the encoder, which gets called with the concatenation of the inputs and targets in the observations argument.

Todo

We might want to change encoders to have a tfp.layers.DistributionLambda final layer that directly returns the appropriately parameterised distributions object.

Parameters:

• observations – The [inputs, targets], with the shapes [batch size, Din] and [batch size, Dout] respectively.

• training – The training mode indicator (passed through to the encoder’s call).

Returns:

The posterior distributions object.

_inference_latent_samples_and_loss(layer_inputs: gpflow.base.TensorType, observations: gpflux.types.ObservationType, seed: int | None = None) → Tuple[tf.Tensor, tf.Tensor]

Sample latent variables during the training forward pass, hence requiring the observations. Also return the KL loss per datapoint.

Parameters:

• layer_inputs – The output of the previous layer _(unused)_.

• observations – The [inputs, targets], with the shapes [batch size, Din] and [batch size, Dout] respectively.

• seed – A random seed for the sampling operation.

Returns:

The samples and the loss-per-datapoint.

_prediction_latent_samples(layer_inputs: gpflow.base.TensorType, seed: int | None = None) → tf.Tensor

Sample latent variables during the prediction forward pass, only depending on the shape of this layer’s inputs.

Parameters:

• layer_inputs – The output of the previous layer (for determining batch shape).

• seed – A random seed for the sampling operation.

Returns:

The samples.

_local_kls(posteriors: tfp.distributions.Distribution) → tf.Tensor

Compute the KL divergences [posteriors∥prior].

Parameters:

posteriors – A distribution that represents the approximate posteriors.

Returns:

The KL divergences from the prior for each of the posteriors.