def testInvalidP(self):
temperature = 1.0
invalid_ps = [1.01, 2.]
for p in invalid_ps:
with self.cached_session():
with self.assertRaisesOpError("probs has components greater than 1"):
dist = relaxed_bernoulli.RelaxedBernoulli(temperature,
probs=p,
validate_args=True)
dist.probs.eval()
invalid_ps = [-0.01, -3.]
for p in invalid_ps:
with self.cached_session():
with self.assertRaisesOpError("Condition x >= 0"):
dist = relaxed_bernoulli.RelaxedBernoulli(temperature,
probs=p,
validate_args=True)
dist.probs.eval()
valid_ps = [0.0, 0.5, 1.0]
for p in valid_ps:
with self.cached_session():
dist = relaxed_bernoulli.RelaxedBernoulli(temperature,
probs=p)
self.assertEqual(p, dist.probs.eval())
def testLogits(self):
temperature = 2.0
logits = [-42., 42.]
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, logits=logits)
with self.cached_session():
self.assertAllClose(logits, dist.logits.eval())
with self.cached_session():
self.assertAllClose(scipy.special.expit(logits), dist.probs.eval())
p = [0.01, 0.99, 0.42]
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
with self.cached_session():
self.assertAllClose(scipy.special.logit(p), dist.logits.eval())
def testDtype(self):
temperature = constant_op.constant(1.0, dtype=dtypes.float32)
p = constant_op.constant([0.1, 0.4], dtype=dtypes.float32)
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
self.assertEqual(dist.dtype, dtypes.float32)
self.assertEqual(dist.dtype, dist.sample(5).dtype)
self.assertEqual(dist.probs.dtype, dist.prob([0.0]).dtype)
self.assertEqual(dist.probs.dtype, dist.log_prob([0.0]).dtype)
temperature = constant_op.constant(1.0, dtype=dtypes.float64)
p = constant_op.constant([0.1, 0.4], dtype=dtypes.float64)
dist64 = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
self.assertEqual(dist64.dtype, dtypes.float64)
self.assertEqual(dist64.dtype, dist64.sample(5).dtype)
def testP(self):
"""Tests that parameter P is set correctly. Note that dist.p != dist.pdf."""
temperature = 1.0
p = [0.1, 0.4]
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
with self.cached_session():
self.assertAllClose(p, dist.probs.eval())
def testZeroTemperature(self):
"""If validate_args, raises InvalidArgumentError when temperature is 0."""
temperature = constant_op.constant(0.0)
p = constant_op.constant([0.1, 0.4])
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError, "x > 0 did not hold"):
_ = relaxed_bernoulli.RelaxedBernoulli(temperature,
probs=p,
validate_args=True)
def testZeroTemperature(self):
"""If validate_args, raises InvalidArgumentError when temperature is 0."""
temperature = constant_op.constant(0.0)
p = constant_op.constant([0.1, 0.4])
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p,
validate_args=True)
with self.cached_session():
sample = dist.sample()
with self.assertRaises(errors_impl.InvalidArgumentError):
sample.eval()
def testShapes(self):
with self.cached_session():
for batch_shape in ([], [1], [2, 3, 4]):
temperature = 1.0
p = np.random.random(batch_shape).astype(np.float32)
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
self.assertAllEqual(batch_shape, dist.batch_shape.as_list())
self.assertAllEqual(batch_shape, dist.batch_shape_tensor().eval())
self.assertAllEqual([], dist.event_shape.as_list())
self.assertAllEqual([], dist.event_shape_tensor().eval())
def testLogProb(self):
with self.cached_session():
t = np.array(1.0, dtype=np.float64)
p = np.array(0.1, dtype=np.float64) # P(x=1)
dist = relaxed_bernoulli.RelaxedBernoulli(t, probs=p)
xs = np.array([0.1, 0.3, 0.5, 0.9], dtype=np.float64)
# analytical density from Maddison et al. 2016
alpha = np.array(p/(1-p), dtype=np.float64)
expected_log_pdf = (np.log(t) + np.log(alpha) +
(-t-1)*(np.log(xs)+np.log(1-xs)) -
2*np.log(alpha*np.power(xs, -t) + np.power(1-xs, -t)))
log_pdf = dist.log_prob(xs).eval()
self.assertAllClose(expected_log_pdf, log_pdf)
def testSampleN(self):
"""mean of quantized samples still approximates the Bernoulli mean."""
with self.cached_session():
temperature = 1e-2
p = [0.2, 0.6, 0.5]
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
n = 10000
samples = dist.sample(n)
self.assertEqual(samples.dtype, dtypes.float32)
sample_values = samples.eval()
self.assertTrue(np.all(sample_values >= 0))
self.assertTrue(np.all(sample_values <= 1))
frac_ones_like = np.sum(sample_values >= 0.5, axis=0)/n
self.assertAllClose(p, frac_ones_like, atol=1e-2)
def testBoundaryConditions(self):
with self.cached_session():
temperature = 1e-2
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=1.0)
self.assertAllClose(np.nan, dist.log_prob(0.0).eval())
self.assertAllClose([np.nan], [dist.log_prob(1.0).eval()])
def testContinuous(self):
temperature = 1.0
p = [0.1, 0.4]
dist = relaxed_bernoulli.RelaxedBernoulli(temperature, probs=p)
self.assertTrue(dist.is_continuous)
