Method assign

gpflow/base.py:250–280

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)

(
        self,
        value: "TensorData",
        use_locking: bool = False,
        name: Optional[str] = None,
        read_value: bool = True,
    )

        return self.unconstrained_variable.trainable  # type: ignore[no-any-return]

    def assign(
        self,
        value: "TensorData",
        use_locking: bool = False,
        name: Optional[str] = None,
        read_value: bool = True,
    ) -> tf.Tensor:
        """
        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`.


        :param value: Constrained tensor-like value.
        :param use_locking: If `True`, use locking during the assignment.
        :param name: The name of the operation to be created.
        :param read_value: if True, will return something which evaluates to the new
            value of the variable; if False will return the assign op.
        """
        unconstrained_value = _validate_unconstrained_value(value, self.transform, self.dtype)
        return self.unconstrained_variable.assign(
            unconstrained_value, use_locking=use_locking, name=name, read_value=read_value
        )


# These types are defined after "Parameter" to avoid forward references that breaks our