Source code for gpflow.models.util

# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from typing import Any, Callable, Sequence, Union

import numpy as np
import tensorflow as tf

from ..base import AnyNDArray
from ..config import default_float
from ..experimental.check_shapes import check_shapes
from ..inducing_variables import InducingPoints, InducingVariables
from .model import BayesianModel
from .training_mixins import Data, ExternalDataTrainingLossMixin

InducingVariablesLike = Union[InducingVariables, tf.Tensor, AnyNDArray]
InducingPointsLike = Union[InducingPoints, tf.Tensor, AnyNDArray]


[docs]def inducingpoint_wrapper(inducing_variable: InducingVariablesLike) -> InducingVariables: """ This wrapper allows transparently passing either an InducingVariables object or an array specifying InducingPoints positions. """ if not isinstance(inducing_variable, InducingVariables): inducing_variable = InducingPoints(inducing_variable) return inducing_variable
def _assert_equal_data( data1: Union[tf.Tensor, Sequence[tf.Tensor]], data2: Union[tf.Tensor, Sequence[tf.Tensor]] ) -> None: if isinstance(data1, tf.Tensor) and isinstance(data2, tf.Tensor): tf.debugging.assert_equal(data1, data2) else: for v1, v2 in zip(data1, data2): tf.debugging.assert_equal(v1, v2)
[docs]@check_shapes( "data[0]: [N, D]", "data[1]: [N, P]", ) def training_loss_closure( model: BayesianModel, data: Data, **closure_kwargs: Any ) -> Callable[[], tf.Tensor]: if isinstance(model, ExternalDataTrainingLossMixin): return model.training_loss_closure(data, **closure_kwargs) # type: ignore[no-any-return] else: _assert_equal_data(model.data, data) return model.training_loss_closure(**closure_kwargs) # type: ignore[no-any-return]
[docs]@check_shapes( "data[0]: [N, D]", "data[1]: [N, P]", "return: []", ) def training_loss(model: BayesianModel, data: Data) -> tf.Tensor: if isinstance(model, ExternalDataTrainingLossMixin): return model.training_loss(data) else: _assert_equal_data(model.data, data) return model.training_loss()
[docs]@check_shapes( "data[0]: [N, D]", "data[1]: [N, P]", "return: []", ) def maximum_log_likelihood_objective(model: BayesianModel, data: Data) -> tf.Tensor: if isinstance(model, ExternalDataTrainingLossMixin): return model.maximum_log_likelihood_objective(data) else: _assert_equal_data(model.data, data) return model.maximum_log_likelihood_objective()
[docs]def data_input_to_tensor(structure: Any) -> Any: """ Converts non-tensor elements of a structure to TensorFlow tensors retaining the structure itself. The function doesn't keep original element's dtype and forcefully converts them to GPflow's default float type. """ def convert_to_tensor(elem: Any) -> tf.Tensor: if tf.is_tensor(elem): return elem elif isinstance(elem, np.ndarray): return tf.convert_to_tensor(elem) return tf.convert_to_tensor(elem, dtype=default_float()) return tf.nest.map_structure(convert_to_tensor, structure)