gpflow#

Modules#

Classes#

gpflow.Module#

class gpflow.Module(name=None)[source]#

Bases: tensorflow.python.module.module.Module

gpflow.Parameter#

class gpflow.Parameter(value, *, transform=None, prior=None, prior_on=None, trainable=None, dtype=None, name=None, unconstrained_shape=None, constrained_shape=None, shape=None)[source]#

Bases: tensorflow_probability.python.util.deferred_tensor.TransformedVariable

Parameters

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

  • transform (Optional[Bijector]) –

  • prior (Optional[Distribution]) –

  • prior_on (Union[str, PriorOn, None]) –

  • trainable (Optional[bool]) –

  • dtype (Union[dtype, DType, None]) –

  • name (Optional[str]) –

  • unconstrained_shape (Optional[Sequence[Optional[int]]]) –

  • constrained_shape (Optional[Sequence[Optional[int]]]) –

  • shape (Optional[Sequence[Optional[int]]]) –

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], ndarray[Any, 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: bool#

True if this instance is trainable, else False.

This attribute cannot be set directly. Use gpflow.set_trainable().

Return type

bool

Functions#

gpflow.default_float#

gpflow.default_float()[source]#

Returns default float type

Return type

type

gpflow.default_int#

gpflow.default_int()[source]#

Returns default integer type

Return type

type

gpflow.default_jitter#

gpflow.default_jitter()[source]#

The jitter is a constant that GPflow adds to the diagonal of matrices to achieve numerical stability of the system when the condition number of the associated matrices is large, and therefore the matrices nearly singular.

Return type

float

gpflow.set_trainable#

gpflow.set_trainable(model, flag)[source]#

Set trainable flag for all tf.Variables and gpflow.Parameters in a tf.Module or collection of tf.Modules.

Parameters

  • model (Union[Module, Iterable[Module]]) –

  • flag (bool) –

Return type

None