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Runs one step of Uncalibrated Hamiltonian Monte Carlo.
Inherits From: TransitionKernel
tfp.mcmc.UncalibratedHamiltonianMonteCarlo(
target_log_prob_fn,
step_size,
num_leapfrog_steps,
state_gradients_are_stopped=False,
store_parameters_in_results=False,
experimental_shard_axis_names=None,
name=None
)
For more details on UncalibratedHamiltonianMonteCarlo
, see
HamiltonianMonteCarlo
.
Args | |
---|---|
target_log_prob_fn
|
Python callable which takes an argument like
current_state (or *current_state if it's a list) and returns its
(possibly unnormalized) log-density under the target distribution.
|
step_size
|
Tensor or Python list of Tensor s representing the step
size for the leapfrog integrator. Must broadcast with the shape of
current_state . Larger step sizes lead to faster progress, but
too-large step sizes make rejection exponentially more likely. When
possible, it's often helpful to match per-variable step sizes to the
standard deviations of the target distribution in each variable.
|
num_leapfrog_steps
|
Integer number of steps to run the leapfrog integrator
for. Total progress per HMC step is roughly proportional to
step_size * num_leapfrog_steps .
|
state_gradients_are_stopped
|
Python bool indicating that the proposed
new state be run through tf.stop_gradient . This is particularly useful
when combining optimization over samples from the HMC chain.
Default value: False (i.e., do not apply stop_gradient ).
|
store_parameters_in_results
|
If True , then step_size and
num_leapfrog_steps are written to and read from eponymous fields in
the kernel results objects returned from one_step and
bootstrap_results . This allows wrapper kernels to adjust those
parameters on the fly.
|
experimental_shard_axis_names
|
A structure of string names indicating how members of the state are sharded. |
name
|
Python str name prefixed to Ops created by this function.
Default value: None (i.e., 'hmc_kernel').
|
Attributes | |
---|---|
experimental_shard_axis_names
|
The shard axis names for members of the state. |
is_calibrated
|
Returns True if Markov chain converges to specified distribution.
|
name
|
|
num_leapfrog_steps
|
Returns the num_leapfrog_steps parameter.
If |
parameters
|
Return dict of __init__ arguments and their values.
|
state_gradients_are_stopped
|
|
step_size
|
Returns the step_size parameter.
If |
target_log_prob_fn
|
Methods
bootstrap_results
bootstrap_results(
init_state
)
Creates initial previous_kernel_results
using a supplied state
.
copy
copy(
**override_parameter_kwargs
)
Non-destructively creates a deep copy of the kernel.
Args | |
---|---|
**override_parameter_kwargs
|
Python String/value dictionary of
initialization arguments to override with new values.
|
Returns | |
---|---|
new_kernel
|
TransitionKernel object of same type as self ,
initialized with the union of self.parameters and
override_parameter_kwargs, with any shared keys overridden by the
value of override_parameter_kwargs, i.e.,
dict(self.parameters, **override_parameters_kwargs) .
|
experimental_with_shard_axes
experimental_with_shard_axes(
shard_axis_names
)
Returns a copy of the kernel with the provided shard axis names.
Args | |
---|---|
shard_axis_names
|
a structure of strings indicating the shard axis names for each component of this kernel's state. |
Returns | |
---|---|
A copy of the current kernel with the shard axis information. |
one_step
one_step(
current_state, previous_kernel_results, seed=None
)
Runs one iteration of Hamiltonian Monte Carlo.
Args | |
---|---|
current_state
|
Tensor or Python list of Tensor s representing the
current state(s) of the Markov chain(s). The first r dimensions index
independent chains, r = tf.rank(target_log_prob_fn(*current_state)) .
|
previous_kernel_results
|
collections.namedtuple containing Tensor s
representing values from previous calls to this function (or from the
bootstrap_results function.)
|
seed
|
PRNG seed; see tfp.random.sanitize_seed for details.
|
Returns | |
---|---|
next_state
|
Tensor or Python list of Tensor s representing the state(s)
of the Markov chain(s) after taking exactly one step. Has same type and
shape as current_state .
|
kernel_results
|
collections.namedtuple of internal calculations used to
advance the chain.
|
Raises | |
---|---|
ValueError
|
if there isn't one step_size or a list with same length as
current_state .
|