gpflow¶
gpflow.Module¶
-
class
gpflow.Module(*args, **kwargs)[source]¶ Bases:
tensorflow.Module- Attributes
- parameters
- trainable_parameters
Methods
__call__(*args, **kwargs)Call self as a function.
- Parameters
args (
Any) –kwargs (
Any) –
gpflow.Parameter¶
-
class
gpflow.Parameter(value, *, transform=None, prior=None, prior_on=<PriorOn.CONSTRAINED: 'constrained'>, trainable=True, dtype=None, name=None)[source]¶ Bases:
tensorflow_probability.util.TransformedVariable- Attributes
- prior_on
trainableTrue if this instance is trainable, else False.
- transform
- unconstrained_variable
Methods
__call__(*args, **kwargs)Call self as a function.
assign(value[, use_locking, name, read_value])Assigns constrained value to the unconstrained parameter’s variable.
Log of the prior probability density of the constrained variable.
- Parameters
value (
Union[int,float,Sequence[Any],Tensor,Variable,Parameter]) –transform (
Optional[Bijector]) –prior (
Optional[Distribution]) –prior_on (
Union[str,PriorOn]) –trainable (
bool) –dtype (
Union[dtype,DType,None]) –name (
Optional[str]) –
-
__init__(value, *, transform=None, prior=None, prior_on=<PriorOn.CONSTRAINED: 'constrained'>, trainable=True, dtype=None, name=None)[source]¶ A parameter retains both constrained and unconstrained representations. If no transform is provided, these two values will be the same. It is often challenging to operate with unconstrained parameters. For example, a variance cannot be negative, therefore we need a positive constraint and it is natural to use constrained values. A prior can be imposed either on the constrained version (default) or on the unconstrained version of the parameter.
- Parameters
value (
Union[int,float,Sequence[Any],Tensor,Variable,Parameter]) –transform (
Optional[Bijector]) –prior (
Optional[Distribution]) –prior_on (
Union[str,PriorOn]) –trainable (
bool) –dtype (
Union[dtype,DType,None]) –name (
Optional[str]) –
-
assign(value, use_locking=False, name=None, read_value=True)[source]¶ Assigns constrained value to the unconstrained parameter’s variable. It passes constrained value through parameter’s transform first.
- Example:
``` a = Parameter(2.0, transform=tfp.bijectors.Softplus()) b = Parameter(3.0)
a.assign(4.0) # a parameter to 2.0 value. a.assign(tf.constant(5.0)) # a parameter to 5.0 value. a.assign(b) # a parameter to constrained value of b. ```
- Parameters
value (
Union[int,float,Sequence[Any],Tensor,Variable,Parameter]) – Constrained tensor-like value.use_locking (
bool) – If True, use locking during the assignment.name (
Optional[str]) – The name of the operation to be created.read_value (
bool) – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
- Return type
Tensor
-
log_prior_density()[source]¶ Log of the prior probability density of the constrained variable.
- Return type
Tensor
-
property
trainable¶ True if this instance is trainable, else False.
This attribute cannot be set directly. Use
gpflow.set_trainable().- Return type
bool
gpflow.default_jitter¶
gpflow.set_trainable¶
gpflow.base¶
gpflow.conditionals¶
gpflow.config¶
gpflow.covariances¶
gpflow.expectations¶
gpflow.inducing_variables¶
gpflow.kernels¶
gpflow.kullback_leiblers¶
gpflow.likelihoods¶
gpflow.logdensities¶
gpflow.mean_functions¶
Throughout GPflow, by default, latent functions being modelled with Gaussian processes are assumed to have zero mean, f ~ GP(0, k(x,x’)).
In some cases we may wish to model only the deviation from a fixed function with a Gaussian process. For flexibility this fixed function could be both input dependent and parameterised function, μ(x; θ), with some unknown parameters θ, resulting in f ~ GP(μ(x;θ), k(x,x’)).
The GPflow MeanFunction class
allows this to be done whilst additionally learning parameters of the
parametric function.