trieste.models.keras.architectures#

This file contains implementations of neural network architectures with Keras.

Module Contents#

class KerasEnsemble(networks: Sequence[KerasEnsembleNetwork])[source]#

This class builds an ensemble of neural networks, using Keras. Individual networks must be instance of KerasEnsembleNetwork. This class is meant to be used with DeepEnsemble model wrapper, which compiles the model.

Parameters

networks – A list of neural network specifications, one for each member of the ensemble. The ensemble will be built using these specifications.

Raises

ValueError – If there are no objects in networks or we try to create a model with networks whose input or output shapes are not the same.

property model → tensorflow.keras.Model[source]#

Returns built but uncompiled Keras ensemble model.

property ensemble_size → int[source]#

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

_build_ensemble() → tensorflow.keras.Model[source]#

Builds the ensemble model by combining all the individual networks in a single Keras model. This method relies on connect_layers method of KerasEnsembleNetwork objects to construct individual networks.

Returns

The Keras model.

class KerasEnsembleNetwork(input_tensor_spec: tensorflow.TensorSpec, output_tensor_spec: tensorflow.TensorSpec, network_name: str = '')[source]#

This class is an interface that defines necessary attributes and methods for neural networks that are meant to be used for building ensembles by KerasEnsemble. Subclasses are not meant to build and compile Keras models, instead they are providing specification that KerasEnsemble will use to build the Keras model.

Parameters

  • input_tensor_spec – Tensor specification for the input to the network.

  • output_tensor_spec – Tensor specification for the output of the network.

  • network_name – The name to be used when building the network.

abstract connect_layers() → tuple[tensorflow.Tensor, tensorflow.Tensor][source]#

Connects the layers of the neural network. Architecture, layers and layer specifications need to be defined by the subclasses.

Returns

Input and output tensor of the network, required by tf.keras.Model to build a model.

class MultivariateNormalTriL(event_size: int, convert_to_tensor_fn: Callable[[tensorflow_probability.python.distributions.Distribution], trieste.types.TensorType] = tfp.python.distributions.Distribution.sample, validate_args: bool = False, **kwargs: Any)[source]#

Bases: tensorflow_probability.layers.MultivariateNormalTriL

Fixed version of tfp.layers.MultivariateNormalTriL that handles saving.

class GaussianNetwork(input_tensor_spec: tensorflow.TensorSpec, output_tensor_spec: tensorflow.TensorSpec, hidden_layer_args: Sequence[dict[str, Any]] = ({'units': 50, 'activation': 'relu'}, {'units': 50, 'activation': 'relu'}), independent: bool = False)[source]#

Bases: KerasEnsembleNetwork

This class defines layers of a probabilistic neural network using Keras. The network architecture is a multilayer fully-connected feed-forward network, with Gaussian distribution as an output. The layers are meant to be built as an ensemble model by KerasEnsemble. Note that this is not a Bayesian neural network.

Parameters

  • input_tensor_spec – Tensor specification for the input to the network.

  • output_tensor_spec – Tensor specification for the output of the network.

  • hidden_layer_args – Specification for building dense hidden layers. Each element in the sequence should be a dictionary containing arguments (keys) and their values for a Dense hidden layer. Please check Keras Dense layer API for available arguments. Objects in the sequence will sequentially be used to add Dense layers. Length of this sequence determines the number of hidden layers in the network. Default value is two hidden layers, 50 nodes each, with ReLu activation functions. Empty sequence needs to be passed to have no hidden layers.

  • independent – In case multiple outputs are modeled, if set to True then IndependentNormal layer is used as the output layer. This models outputs as independent, only the diagonal elements of the covariance matrix are parametrized. If left as the default False, then MultivariateNormalTriL layer is used where correlations between outputs are learned as well.

Raises

ValueError – If objects in hidden_layer_args are not dictionaries.

connect_layers() → tuple[tensorflow.Tensor, tensorflow.Tensor][source]#

Connect all layers in the network. We start by generating an input tensor based on input tensor specification. Next we generate a sequence of hidden dense layers based on hidden layer arguments. Finally, we generate a dense layer whose nodes act as parameters of a Gaussian distribution in the final probabilistic layer.

Returns

Input and output tensor of the sequence of layers.