Source code for gpflow.models.training_mixins

# Copyright 2020 The GPflow Contributors. All Rights Reserved.
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"""
This module provides mixin classes to be used in conjunction with inheriting
from gpflow.models.BayesianModel (or its subclass gpflow.models.GPModel).

They provide a unified interface to obtain closures that return the training
loss, to be passed as the first argument to the minimize() method of the
optimizers defined in TensorFlow and GPflow.

All TrainingLossMixin classes assume that self._training_loss()
(which is provided by the BayesianModel base class), will be available. Note
that new models only need to implement the maximum_log_likelihood_objective
method that is defined as abstract in BayesianModel.

There are different mixins depending on whether the model already contains the
training data (InternalDataTrainingLossMixin), or requires it to be passed in
to the objective function (ExternalDataTrainingLossMixin).
"""
from typing import Callable, TypeVar, Union

import tensorflow as tf
from tensorflow.python.data.ops.iterator_ops import OwnedIterator as DatasetOwnedIterator

from ..base import InputData, OutputData, RegressionData
from ..experimental.check_shapes import check_shapes

Data = TypeVar("Data", RegressionData, InputData, OutputData)


[docs]class InternalDataTrainingLossMixin: """ Mixin utility for training loss methods for models that own their own data. It provides - a uniform API for the training loss :meth:`training_loss` - a convenience method :meth:`training_loss_closure` for constructing the closure expected by various optimizers, namely :class:`gpflow.optimizers.Scipy` and subclasses of `tf.optimizers.Optimizer`. See :class:`ExternalDataTrainingLossMixin` for an equivalent mixin for models that do **not** own their own data. """
[docs] @check_shapes( "return: []", ) def training_loss(self) -> tf.Tensor: """ Returns the training loss for this model. """ # Type-ignore is because _training_loss should be added by implementing class. return self._training_loss() # type: ignore[attr-defined]
[docs] def training_loss_closure(self, *, compile: bool = True) -> Callable[[], tf.Tensor]: """ Convenience method. Returns a closure which itself returns the training loss. This closure can be passed to the minimize methods on :class:`gpflow.optimizers.Scipy` and subclasses of `tf.optimizers.Optimizer`. :param compile: If `True` (default), compile the training loss function in a TensorFlow graph by wrapping it in tf.function() """ closure = self.training_loss if compile: closure = tf.function(closure) return closure
[docs]class ExternalDataTrainingLossMixin: """ Mixin utility for training loss methods for models that do **not** own their own data. It provides - a uniform API for the training loss :meth:`training_loss` - a convenience method :meth:`training_loss_closure` for constructing the closure expected by various optimizers, namely :class:`gpflow.optimizers.Scipy` and subclasses of `tf.optimizers.Optimizer`. See :class:`InternalDataTrainingLossMixin` for an equivalent mixin for models that **do** own their own data. """
[docs] @check_shapes( "data[0]: [N, D]", "data[1]: [N, P]", "return: []", ) def training_loss(self, data: Data) -> tf.Tensor: """ Returns the training loss for this model. :param data: the data to be used for computing the model objective. """ # Type-ignore is because _training_loss should be added by implementing class. return self._training_loss(data) # type: ignore[attr-defined]
[docs] def training_loss_closure( self, data: Union[Data, DatasetOwnedIterator], *, compile: bool = True, ) -> Callable[[], tf.Tensor]: """ Returns a closure that computes the training loss, which by default is wrapped in tf.function(). This can be disabled by passing `compile=False`. :param data: the data to be used by the closure for computing the model objective. Can be the full dataset or an iterator, e.g. `iter(dataset.batch(batch_size))`, where dataset is an instance of tf.data.Dataset. :param compile: if True, wrap training loss in tf.function() """ training_loss = self.training_loss if isinstance(data, DatasetOwnedIterator): if compile: # lambda because: https://github.com/GPflow/GPflow/issues/1929 training_loss_lambda = lambda d: self.training_loss(d) input_signature = [data.element_spec] training_loss = tf.function(training_loss_lambda, input_signature=input_signature) def closure() -> tf.Tensor: assert isinstance(data, DatasetOwnedIterator) # Hint for mypy. batch = next(data) return training_loss(batch) else: def closure() -> tf.Tensor: return training_loss(data) if compile: closure = tf.function(closure) return closure