def test_log_prob_extreme(self):
assert_allclose = functools.partial(
np.testing.assert_allclose, rtol=1e-5, atol=1e-6)
x = np.asarray(0., dtype=np.float64)
mean = np.asarray(0., dtype=np.float64)
bin_size = 1 / 256.
# to ensure bin_size / (2*scale) < cdf_delta
cdf_delta = 1e-8
s = np.linspace(0, 20, 101)
t = bin_size / (2 * np.exp(s))
idx = np.where(safe_sigmoid(t) - safe_sigmoid(-t) < cdf_delta)[0]
self.assertGreater(np.size(idx), 2)
log_scale = s[idx]
with self.test_session() as sess:
# now compute the log-probability of this extreme case
d = DiscretizedLogistic(
mean=mean, log_scale=log_scale, bin_size=bin_size,
epsilon=1e-7
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=0)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, None, None,
biased_edges=True, group_ndims=0)
)
def test_log_prob(self):
assert_allclose = functools.partial(
np.testing.assert_allclose, rtol=1e-5, atol=1e-6)
np.random.seed(1234)
x = np.random.normal(size=[7, 3, 2, 5, 4]).astype(np.float64)
self.assertLess(np.min(x), -1.1)
self.assertGreater(np.max(x), 2.1)
self.assertGreater(np.sum(np.logical_and(x > -0.5, x < 1.5)), 0)
mean = 3 * np.random.uniform(size=[2, 1, 4]).astype(np.float64) - 1
log_scale = np.random.normal(size=[3, 1, 5, 1]).astype(np.float64)
bin_size = 1 / 256.
min_val = -1.
max_val = 2.
with self.test_session() as sess:
# biased_edges = False, discretize_given = True
d = DiscretizedLogistic(
mean=mean, log_scale=log_scale, bin_size=bin_size,
min_val=None, max_val=None, biased_edges=False,
dtype=tf.float64
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=0)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, None, None,
biased_edges=False, group_ndims=0)
)
# biased_edges = False, discretize_given = True
d = DiscretizedLogistic(
mean=mean, log_scale=log_scale, bin_size=bin_size,
min_val=min_val, max_val=max_val, biased_edges=False,
dtype=tf.float64
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=0)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, min_val, max_val,
biased_edges=False, group_ndims=0)
)
# biased_edges = False, discretize_given = False
d = DiscretizedLogistic(
mean=mean, log_scale=log_scale, bin_size=bin_size,
min_val=min_val, max_val=max_val, biased_edges=False,
dtype=tf.float64, discretize_given=False
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=0)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, min_val, max_val,
biased_edges=False, discretize_given=False, group_ndims=0)
)
# biased_edges = True, discretize_given = True
d = DiscretizedLogistic(
mean=mean, log_scale=log_scale, bin_size=bin_size,
min_val=min_val, max_val=max_val, biased_edges=True,
dtype=tf.float64
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=0)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, min_val, max_val,
biased_edges=True, group_ndims=0)
)
assert_allclose(
sess.run(d.log_prob(x, group_ndims=2)),
naive_discretized_logistic_pdf(
x, mean, log_scale, bin_size, min_val, max_val,
biased_edges=True, group_ndims=2)
)