def trace():
kernel = lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=0.1,
num_integrator_steps=3,
target_log_prob_fn=target_log_prob_fn,
seed=tfp_test_util.test_seed())
fun_mcmc.trace(state=fun_mcmc.HamiltonianMonteCarloState(
tf.zeros([1])),
fn=kernel,
num_steps=4,
trace_fn=lambda *args: ())
def testBasicHMC(self):
if not tf.executing_eagerly():
return
step_size = 0.2
num_steps = 2000
num_leapfrog_steps = 10
state = tf.ones([16, 2])
base_mean = [1., 0]
base_cov = [[1, 0.5], [0.5, 1]]
bijector = tfb.Softplus()
base_dist = tfd.MultivariateNormalFullCovariance(
loc=base_mean, covariance_matrix=base_cov)
target_dist = bijector(base_dist)
def orig_target_log_prob_fn(x):
return target_dist.log_prob(x), ()
target_log_prob_fn, state = fun_mcmc.transform_log_prob_fn(
orig_target_log_prob_fn, bijector, state)
# pylint: disable=g-long-lambda
kernel = tf.function(lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=step_size,
num_leapfrog_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=tfp_test_util.test_seed()))
_, chain = fun_mcmc.trace(
state=fun_mcmc.HamiltonianMonteCarloState(state=state,
state_grads=None,
target_log_prob=None,
state_extra=None),
fn=kernel,
num_steps=num_steps,
trace_fn=lambda state, extra: state.state_extra[0])
sample_mean = tf.reduce_mean(input_tensor=chain, axis=[0, 1])
sample_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
true_samples = target_dist.sample(4096, seed=tfp_test_util.test_seed())
true_mean = tf.reduce_mean(input_tensor=true_samples, axis=0)
true_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
self.assertAllClose(true_mean, sample_mean, rtol=0.1, atol=0.1)
self.assertAllClose(true_cov, sample_cov, rtol=0.1, atol=0.1)
def computation(state):
bijector = tfb.Softplus()
base_dist = tfd.MultivariateNormalFullCovariance(
loc=base_mean, covariance_matrix=base_cov)
target_dist = bijector(base_dist)
def orig_target_log_prob_fn(x):
return target_dist.log_prob(x), ()
target_log_prob_fn, state = fun_mcmc.transform_log_prob_fn(
orig_target_log_prob_fn, bijector, state)
def kernel(hmc_state, step_size, step):
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn)
rate = tf.compat.v1.train.polynomial_decay(
0.01,
global_step=step,
power=0.5,
decay_steps=num_adapt_steps,
end_learning_rate=0.)
mean_p_accept = tf.reduce_mean(input_tensor=tf.exp(
tf.minimum(0., hmc_extra.log_accept_ratio)))
step_size = fun_mcmc.sign_adaptation(step_size,
output=mean_p_accept,
set_point=0.9,
adaptation_rate=rate)
return (hmc_state, step_size, step + 1), hmc_extra
_, (chain, log_accept_ratio_trace) = fun_mcmc.trace(
(fun_mcmc.HamiltonianMonteCarloState(state), step_size, 0),
kernel,
num_adapt_steps + num_steps,
trace_fn=lambda state, extra:
(state[0].state_extra[0], extra.log_accept_ratio))
true_samples = target_dist.sample(4096,
seed=tfp_test_util.test_seed())
return chain, log_accept_ratio_trace, true_samples
def testAdaptiveStepSize(self):
if not tf.executing_eagerly():
return
step_size = 0.2
num_steps = 2000
num_adapt_steps = 1000
num_leapfrog_steps = 10
state = tf.ones([16, 2])
base_mean = [1., 0]
base_cov = [[1, 0.5], [0.5, 1]]
bijector = tfb.Softplus()
base_dist = tfd.MultivariateNormalFullCovariance(
loc=base_mean, covariance_matrix=base_cov)
target_dist = bijector(base_dist)
def orig_target_log_prob_fn(x):
return target_dist.log_prob(x), ()
target_log_prob_fn, state = fun_mcmc.transform_log_prob_fn(
orig_target_log_prob_fn, bijector, state)
@tf.function
def kernel(hmc_state, step_size, step):
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_leapfrog_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn)
rate = tf.compat.v1.train.polynomial_decay(
0.01,
global_step=step,
power=0.5,
decay_steps=num_adapt_steps,
end_learning_rate=0.)
mean_p_accept = tf.reduce_mean(input_tensor=tf.exp(
tf.minimum(0., hmc_extra.log_accept_ratio)))
step_size = fun_mcmc.sign_adaptation(step_size,
output=mean_p_accept,
set_point=0.9,
adaptation_rate=rate)
return (hmc_state, step_size, step + 1), hmc_extra
_, (chain, log_accept_ratio_trace) = fun_mcmc.trace(
(fun_mcmc.HamiltonianMonteCarloState(
state=state,
state_grads=None,
target_log_prob=None,
state_extra=None), step_size, 0),
kernel,
num_adapt_steps + num_steps,
trace_fn=lambda state, extra:
(state[0].state_extra[0], extra.log_accept_ratio))
log_accept_ratio_trace = log_accept_ratio_trace[num_adapt_steps:]
chain = chain[num_adapt_steps:]
sample_mean = tf.reduce_mean(input_tensor=chain, axis=[0, 1])
sample_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
true_samples = target_dist.sample(4096, seed=tfp_test_util.test_seed())
true_mean = tf.reduce_mean(input_tensor=true_samples, axis=0)
true_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
self.assertAllClose(true_mean, sample_mean, rtol=0.05, atol=0.05)
self.assertAllClose(true_cov, sample_cov, rtol=0.05, atol=0.05)
self.assertAllClose(tf.reduce_mean(
input_tensor=tf.exp(tf.minimum(0., log_accept_ratio_trace))),
0.9,
rtol=0.1)