tfp.experimental.mcmc.GradientBasedTrajectoryLengthAdaptationResults

Internal state of GradientBasedTrajectoryLengthAdaptation.

tfp.experimental.mcmc.GradientBasedTrajectoryLengthAdaptationResults(
    inner_results,
    max_trajectory_length,
    step,
    adaptation_rate,
    jitter_amount,
    averaged_sq_grad,
    averaged_sq_grad_adaptation_rate,
    averaged_max_trajectory_length,
    criterion,
    seed
)

Attributes
`inner_results`	Results of the inner kernel.
`max_trajectory_length`	Floating point scalar `Tensor`. Maximum HMC trajectory length.
`step`	Int32 scalar `Tensor`. The number of steps this kernel has taken. Increases by 1 for every call to `one_step`.
`adaptation_rate`	Floating point scalar `Tensor`. How rapidly to adapt the trajectory length.
`jitter_amount`	Floating point scalar `Tensor`. How much to jitter the trajectory on the next step. The trajectory length is sampled from `[(1 - jitter_amount) * max_trajectory_length, max_trajectory_length]`.
`averaged_sq_grad`	Floating point scalar `Tensor`. Moving average of squared criterion gradients.
`averaged_sq_grad_adaptation_rate`	Floating point scalar `Tensor`. How rapidly to adapt the running average squared gradient. This is `1 - beta_2` from Adam.
`averaged_max_trajectory_length`	Floating point scalar `Tensor`. Moving average of the maximum of trajectory length. This is used after the burnin period.
`criterion`	Floating point `Tensor` with shape `[C0, ..., Cb]` with `b > 0`. The value of the criterion returned by the `criterion_fn` corresponding to each Markov chain.
`seed`	PRNG seed; see `tfp.random.sanitize_seed` for details. The random seed used by the kernel in the previous step.

tfp.experimental.mcmc.GradientBasedTrajectoryLengthAdaptationResults

Attributes