|---|
| [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/mcmc/sample_fold.py#L36-L173) |
Computes the requested reductions over the `kernel`'s samples.
tfp.experimental.mcmc.sample_fold(
num_steps,
current_state,
previous_kernel_results=None,
kernel=None,
reducer=None,
previous_reducer_state=None,
return_final_reducer_states=False,
num_burnin_steps=0,
num_steps_between_results=0,
parallel_iterations=10,
seed=None,
name=None
)
To wit, runs the given `kernel` for `num_steps` steps, and consumes
the stream of samples with the given `Reducer`s' `one_step` method(s).
This runs in constant memory (unless a given `Reducer` builds a
large structure).
The driver internally composes the correct onion of `WithReductions`
and `SampleDiscardingKernel` to implement the requested optionally
thinned reduction; however, the kernel results of those applied
Transition Kernels will not be returned. Hence, if warm-restarting
reductions is desired, one should manually build the Transition Kernel
onion and use [`tfp.experimental.mcmc.step_kernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/mcmc/step_kernel).
An arbitrary collection of `reducer` can be provided, and the resulting
finalized statistic(s) will be returned in an identical structure.
This function can sample from and reduce over multiple chains, in parallel.
Whether or not there are multiple chains is dictated by how the `kernel`
treats its inputs. Typically, the shape of the independent chains is shape of
the result of the `target_log_prob_fn` used by the `kernel` when applied to
the given `current_state`.
| ## Args ||
|---|---|
| `num_steps` | Integer or scalar `Tensor` representing the number of `Reducer` steps. |
| `current_state` | `Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s). |
| `previous_kernel_results` | A `Tensor` or a nested collection of `Tensor`s. Warm-start for the auxiliary state needed by the given `kernel`. If not supplied, `sample_fold` will cold-start with `kernel.bootstrap_results`. |
| `kernel` | An instance of [`tfp.mcmc.TransitionKernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/TransitionKernel) which implements one step of the Markov chain. |
| `reducer` | A (possibly nested) structure of `Reducer`s to be evaluated on the `kernel`'s samples. If no reducers are given (`reducer=None`), then `None` will be returned in place of streaming calculations. |
| `previous_reducer_state` | A (possibly nested) structure of running states corresponding to the structure in `reducer`. For resuming streaming reduction computations begun in a previous run. |
| `return_final_reducer_states` | A Python `bool` giving whether to return resumable final reducer states. |
| `num_burnin_steps` | Integer or scalar `Tensor` representing the number of chain steps to take before starting to collect results. Defaults to 0 (i.e., no burn-in). |
| `num_steps_between_results` | Integer or scalar `Tensor` representing the number of chain steps between collecting a result. Only one out of every `num_steps_between_samples + 1` steps is included in the returned results. Defaults to 0 (i.e., no thinning). |
| `parallel_iterations` | The number of iterations allowed to run in parallel. It must be a positive integer. See [`tf.while_loop`](https://www.tensorflow.org/api_docs/python/tf/while_loop) for more details. |
| `seed` | PRNG seed; see [`tfp.random.sanitize_seed`](https://www.tensorflow.org/probability/api_docs/python/tfp/random/sanitize_seed) for details. |
| `name` | Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., 'mcmc_sample_fold'). |
| ## Returns ||
|---|---|
| `reduction_results` | A (possibly nested) structure of finalized reducer statistics. The structure identically mimics that of `reducer`. |
| `end_state` | The final state of the Markov chain(s). |
| `final_kernel_results` | `collections.namedtuple` of internal calculations used to advance the supplied `kernel`. These results do not include the kernel results of `WithReductions` or `SampleDiscardingKernel`. |
| `final_reducer_states` | A (possibly nested) structure of final running reducer states, if `return_final_reducer_states` was `True`. Can be used to resume streaming reductions when continuing sampling. |