trieste.models.keras.interface#

Module Contents#

class KerasPredictor(optimizer: trieste.models.optimizer.KerasOptimizer | None = None)[source]#

Bases: trieste.models.interfaces.ProbabilisticModel, abc.ABC

This is an interface for trainable wrappers of TensorFlow and Keras neural network models.

Parameters:

optimizer – The optimizer wrapper containing the optimizer with which to train the model and arguments for the wrapper and the optimizer. The optimizer must be an instance of a Optimizer. Defaults to Adam optimizer with default parameters.

Raises:

ValueError – If the optimizer is not an instance of Optimizer.

abstract property model: tensorflow.keras.Model[source]#

The compiled Keras model.

property optimizer: trieste.models.optimizer.KerasOptimizer[source]#

The optimizer wrapper for training the model.

predict(query_points: trieste.types.TensorType) → tuple[trieste.types.TensorType, trieste.types.TensorType][source]#

Return the mean and variance of the independent marginal distributions at each point in query_points.

This is essentially a convenience method for predict_joint(), where non-event dimensions of query_points are all interpreted as broadcasting dimensions instead of batch dimensions, and the covariance is squeezed to remove redundant nesting.

Parameters:

query_points – The points at which to make predictions, of shape […, D].

Returns:

The mean and variance of the independent marginal distributions at each point in query_points. For a predictive distribution with event shape E, the mean and variance will both have shape […] + E.

abstract sample(query_points: trieste.types.TensorType, num_samples: int) → trieste.types.TensorType[source]#

Return num_samples samples from the independent marginal distributions at query_points.

Parameters:

  • query_points – The points at which to sample, with shape […, N, D].

  • num_samples – The number of samples at each point.

Returns:

The samples. For a predictive distribution with event shape E, this has shape […, S, N] + E, where S is the number of samples.

class DeepEnsembleModel[source]#

Bases: trieste.models.interfaces.ProbabilisticModel, typing_extensions.Protocol

This is an interface for deep ensemble type of model, primarily for usage by trajectory samplers, to avoid circular imports. These models can act as probabilistic models by deriving estimates of epistemic uncertainty from the diversity of predictions made by individual models in the ensemble.

abstract property ensemble_size: int[source]#

Returns the size of the ensemble, that is, the number of base learners or individual models in the ensemble.

abstract property num_outputs: int[source]#

Returns the number of outputs trained on by each member network.

abstract ensemble_distributions(query_points: trieste.types.TensorType) → tuple[tensorflow_probability.distributions.Distribution, Ellipsis][source]#

Return distributions for each member of the ensemble. Type of the output will depend on the subclass, it might be a predicted value or a distribution.

Parameters:

query_points – The points at which to return outputs.

Returns:

The outputs for the observations at the specified query_points for each member of the ensemble.