gpflow.utilities#

Modules#

Classes#

gpflow.utilities.Dispatcher#

class gpflow.utilities.Dispatcher(name, doc=None)[source]#

Bases: multipledispatch.dispatcher.Dispatcher

multipledispatch.Dispatcher uses a generator to yield the desired function implementation, which is problematic as TensorFlow’s autograph is not able to compile code that passes through generators.

This class overwrites the problematic method in the original Dispatcher and solely makes use of simple for-loops, which are compilable by AutoGraph.

dispatch(*types)[source]#

Returns matching function for types; if not existing returns None.

Parameters

types (Union[Type[Any], Tuple[Type[Any], ...]]) –

Return type

Optional[Callable[..., Any]]

dispatch_or_raise(*types)[source]#

Returns matching function for types; if not existing raises an error.

Parameters

types (Union[Type[Any], Tuple[Type[Any], ...]]) –

Return type

Callable[..., Any]

get_first_occurrence(*types)[source]#

Returns the first occurrence of a matching function

Based on multipledispatch.Dispatcher.dispatch_iter, which returns an iterator of matching functions. This method uses the same logic to select functions, but simply returns the first element of the iterator. If no matching functions are found, None is returned.

Parameters

types (Union[Type[Any], Tuple[Type[Any], ...]]) –

Return type

Optional[Callable[..., Any]]

register(*types, **kwargs)[source]#

register dispatcher with new implementation

>>> f = Dispatcher('f')
>>> @f.register(int)
... def inc(x):
...     return x + 1

>>> @f.register(float)
... def dec(x):
...     return x - 1

>>> @f.register(list)
... @f.register(tuple)
... def reverse(x):
...     return x[::-1]

>>> f(1)
2

>>> f(1.0)
0.0

>>> f([1, 2, 3])
[3, 2, 1]
Parameters

  • types (Union[Type[Any], Tuple[Type[Any], ...]]) –

  • kwargs (Any) –

Return type

Callable[[TypeVar(_C, bound= Callable[..., Any])], TypeVar(_C, bound= Callable[..., Any])]

Functions#

gpflow.utilities.add_noise_cov#

gpflow.utilities.add_noise_cov(K, likelihood_variance)[source]#

Returns K + σ² I, where σ² is the likelihood noise variance (scalar), and I is the corresponding identity matrix.

Parameters

  • K (Tensor) –

  • likelihood_variance (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

Return type

Tensor

gpflow.utilities.deepcopy#

gpflow.utilities.deepcopy(input_module, memo=None)[source]#

Returns a deepcopy of the input tf.Module. To do that first resets the caches stored inside each tfp.bijectors.Bijector to allow the deepcopy of the tf.Module.

Parameters

  • input_module (TypeVar(M, bound= Module)) – tf.Module including keras.Model, keras.layers.Layer and gpflow.Module.

  • memo (Optional[Dict[int, Any]]) – passed through to func:copy.deepcopy (see https://docs.python.org/3/library/copy.html).

Return type

TypeVar(M, bound= Module)

Returns

Returns a deepcopy of an input object.

gpflow.utilities.freeze#

gpflow.utilities.freeze(input_module)[source]#

Returns a deepcopy of the input tf.Module with constants instead of variables and parameters.

Parameters

input_module (TypeVar(M, bound= Module)) – tf.Module or gpflow.Module.

Return type

TypeVar(M, bound= Module)

Returns

Returns a frozen deepcopy of an input object.

gpflow.utilities.is_variable#

gpflow.utilities.is_variable(t)[source]#

Returns whether the t is a TensorFlow variable.

Parameters

t (Union[int, float, Sequence[Any], ndarray[Any, Any], Tensor, Variable, Parameter]) –

Return type

bool

gpflow.utilities.leaf_components#

gpflow.utilities.leaf_components(input)[source]#
Parameters

input (Module) –

Return type

Mapping[str, Union[Variable, Parameter]]

gpflow.utilities.multiple_assign#

gpflow.utilities.multiple_assign(module, parameters)[source]#

Multiple assign takes a dictionary with new values. Dictionary keys are paths to the tf.Variable`s or `gpflow.Parameter of the input module.

Parameters

  • module (Module) – tf.Module.

  • parameters (Mapping[str, Tensor]) – a dictionary with keys of the form “.module.path.to.variable” and new value tensors.

Return type

None

gpflow.utilities.parameter_dict#

gpflow.utilities.parameter_dict(module)[source]#

Returns a dictionary of parameters (variables) for the tf.Module component. Dictionary keys are relative paths to the attributes to which parameters (variables) assigned to.

class SubModule(tf.Module):
def __init__(self):

self.parameter = gpflow.Parameter(1.0) self.variable = tf.Variable(1.0)

class Module(tf.Module):
def __init__(self):

self.submodule = SubModule()

m = Module() params = parameter_dict(m) # { # “.submodule.parameter”: <parameter object>, # “.submodule.variable”: <variable object> # }

Parameters

module (Module) –

Return type

Dict[str, Union[Variable, Parameter]]

gpflow.utilities.positive#

gpflow.utilities.positive(lower=None, base=None)[source]#

Returns a positive bijector (a reversible transformation from real to positive numbers).

Parameters

  • lower (Optional[float]) – overrides default lower bound (if None, defaults to gpflow.config.default_positive_minimum())

  • base (Optional[str]) – overrides base positive bijector (if None, defaults to gpflow.config.default_positive_bijector())

Return type

Bijector

Returns

a bijector instance

gpflow.utilities.print_summary#

gpflow.utilities.print_summary(module, fmt=None)[source]#

Prints a summary of the parameters and variables contained in a tf.Module.

Parameters

  • module (Module) –

  • fmt (Optional[str]) –

Return type

None

gpflow.utilities.read_values#

gpflow.utilities.read_values(module)[source]#

Returns a dictionary of numpy values of the module parameters (variables).

Parameters

module (Module) –

Return type

Dict[str, ndarray[Any, Any]]

gpflow.utilities.reset_cache_bijectors#

gpflow.utilities.reset_cache_bijectors(input_module)[source]#

Recursively finds tfp.bijectors.Bijector-s inside the components of the tf.Module using traverse_component. Resets the caches stored inside each tfp.bijectors.Bijector.

Parameters

input_module (Module) – tf.Module including keras.Model, keras.layers.Layer and gpflow.Module.

Return type

Module

Returns

same object but with all bijector caches reset

gpflow.utilities.select_dict_parameters_with_prior#

gpflow.utilities.select_dict_parameters_with_prior(model)[source]#

Collects parameters with prior into a dictionary.

Parameters

model (Module) –

Return type

Dict[str, Parameter]

gpflow.utilities.tabulate_module_summary#

gpflow.utilities.tabulate_module_summary(module, tablefmt=None)[source]#
Parameters

  • module (Module) –

  • tablefmt (Optional[str]) –

Return type

str

gpflow.utilities.to_default_float#

gpflow.utilities.to_default_float(x)[source]#
Parameters

x (Union[int, float, Sequence[Any], ndarray[Any, Any], Tensor, Variable, Parameter]) –

Return type

Tensor

gpflow.utilities.to_default_int#

gpflow.utilities.to_default_int(x)[source]#
Parameters

x (Union[int, float, Sequence[Any], ndarray[Any, Any], Tensor, Variable, Parameter]) –

Return type

Tensor

gpflow.utilities.training_loop#

gpflow.utilities.training_loop(closure, optimizer=None, var_list=None, maxiter=1000, compile=False)[source]#

Simple generic training loop. At each iteration uses a GradientTape to compute the gradients of a loss function with respect to a set of variables.

Parameters

  • closure (Callable[[], Tensor]) – Callable that constructs a loss function based on data and model being trained

  • optimizer (Optional[OptimizerV2]) – tf.optimizers or tf.keras.optimizers that updates variables by applying the corresponding loss gradients. Adam is a default optimizer with default settings.

  • var_list (Optional[List[Variable]]) – List of model variables to be learnt during training

  • maxiter (int) – Maximum number of

  • compile (bool) –

Return type

None

Returns

gpflow.utilities.triangular#

gpflow.utilities.triangular()[source]#

Returns instance of a triangular bijector.

Return type

Bijector

gpflow.utilities.triangular_size#

gpflow.utilities.triangular_size(n)[source]#

Returns the number of non-zero elements in an n by n triangular matrix.

Parameters

n (Tensor) –

Return type

Tensor