def testLogisticVariance(self):
with self.test_session():
loc = [2.0, 1.5, 1.0]
scale = 1.5
expected_variance = stats.logistic.var(loc, scale)
dist = logistic.Logistic(loc, scale)
self.assertAllClose(dist.variance().eval(), expected_variance)
def testLogisticMean(self):
with self.test_session():
loc = [2.0, 1.5, 1.0]
scale = 1.5
expected_mean = stats.logistic.mean(loc, scale)
dist = logistic.Logistic(loc, scale)
self.assertAllClose(dist.mean().eval(), expected_mean)
def testReparameterizable(self):
batch_size = 6
np_loc = np.array([2.0] * batch_size, dtype=np.float32)
loc = constant_op.constant(np_loc)
scale = 1.5
dist = logistic.Logistic(loc, scale)
self.assertTrue(
dist.reparameterization_type == distribution.FULLY_REPARAMETERIZED)
def __init__(self,
temperature,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="RelaxedBernoulli"):
"""Construct RelaxedBernoulli distributions.
Args:
temperature: An 0-D `Tensor`, representing the temperature
of a set of RelaxedBernoulli distributions. The temperature should be
positive.
logits: An N-D `Tensor` representing the log-odds
of a positive event. Each entry in the `Tensor` parametrizes
an independent RelaxedBernoulli distribution where the probability of an
event is sigmoid(logits). Only one of `logits` or `probs` should be
passed in.
probs: An N-D `Tensor` representing the probability of a positive event.
Each entry in the `Tensor` parameterizes an independent Bernoulli
distribution. Only one of `logits` or `probs` should be passed in.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: If both `probs` and `logits` are passed, or if neither.
"""
parameters = locals()
with ops.name_scope(name, values=[logits, probs, temperature]) as ns:
with ops.control_dependencies([check_ops.assert_positive(temperature)]
if validate_args else []):
self._temperature = array_ops.identity(temperature, name="temperature")
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits, probs=probs, validate_args=validate_args)
dist = logistic.Logistic(self._logits / self._temperature,
1. / self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
self._parameters = parameters
def inverse_log_det_jacobian_fn(y):
return -math_ops.log(y) - math_ops.log1p(-y)
sigmoid_bijector = bijectors.Inline(
forward_fn=math_ops.sigmoid,
inverse_fn=(lambda y: math_ops.log(y) - math_ops.log1p(-y)),
inverse_log_det_jacobian_fn=inverse_log_det_jacobian_fn,
name="sigmoid")
super(RelaxedBernoulli, self).__init__(dist, sigmoid_bijector, name=name)
def testLogisticSample(self):
with self.test_session():
loc = [3.0, 4.0, 2.0]
scale = 1.0
dist = logistic.Logistic(loc, scale)
sample = dist.sample(seed=100)
self.assertEqual(sample.get_shape(), (3, ))
self.assertAllClose(sample.eval(),
[6.22460556, 3.79602098, 2.05084133])
def testLogisticEntropy(self):
with self.test_session():
batch_size = 3
np_loc = np.array([2.0] * batch_size, dtype=np.float32)
loc = constant_op.constant(np_loc)
scale = 1.5
expected_entropy = stats.logistic.entropy(np_loc, scale)
dist = logistic.Logistic(loc, scale)
self.assertAllClose(dist.entropy().eval(), expected_entropy)
def testDtype(self):
loc = constant_op.constant([0.1, 0.4], dtype=dtypes.float32)
scale = constant_op.constant(1.0, dtype=dtypes.float32)
dist = logistic.Logistic(loc, scale)
self.assertEqual(dist.dtype, dtypes.float32)
self.assertEqual(dist.loc.dtype, dist.scale.dtype)
self.assertEqual(dist.dtype, dist.sample(5).dtype)
self.assertEqual(dist.dtype, dist.mode().dtype)
self.assertEqual(dist.loc.dtype, dist.mean().dtype)
self.assertEqual(dist.loc.dtype, dist.variance().dtype)
self.assertEqual(dist.loc.dtype, dist.stddev().dtype)
self.assertEqual(dist.loc.dtype, dist.entropy().dtype)
self.assertEqual(dist.loc.dtype, dist.prob(0.2).dtype)
self.assertEqual(dist.loc.dtype, dist.log_prob(0.2).dtype)
loc = constant_op.constant([0.1, 0.4], dtype=dtypes.float64)
scale = constant_op.constant(1.0, dtype=dtypes.float64)
dist64 = logistic.Logistic(loc, scale)
self.assertEqual(dist64.dtype, dtypes.float64)
self.assertEqual(dist64.dtype, dist64.sample(5).dtype)
def testLogisticLogCDF(self):
with self.test_session():
batch_size = 6
np_loc = np.array([2.0] * batch_size, dtype=np.float32)
loc = constant_op.constant(np_loc)
scale = 1.5
dist = logistic.Logistic(loc, scale)
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
logcdf = dist.log_cdf(x)
expected_logcdf = stats.logistic.logcdf(x, np_loc, scale)
self.assertEqual(logcdf.get_shape(), (6, ))
self.assertAllClose(logcdf.eval(), expected_logcdf)
def testLogisticSurvivalFunction(self):
with self.test_session():
batch_size = 6
np_loc = np.array([2.0] * batch_size, dtype=np.float32)
loc = constant_op.constant(np_loc)
scale = 1.5
dist = logistic.Logistic(loc, scale)
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
survival_function = dist.survival_function(x)
expected_survival_function = stats.logistic.sf(x, np_loc, scale)
self.assertEqual(survival_function.get_shape(), (6, ))
self.assertAllClose(survival_function.eval(),
expected_survival_function)
def testLogisticLogProb(self):
with self.test_session():
batch_size = 6
np_loc = np.array([2.0] * batch_size, dtype=np.float32)
loc = constant_op.constant(np_loc)
scale = 1.5
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
dist = logistic.Logistic(loc, scale)
expected_log_prob = stats.logistic.logpdf(x, np_loc, scale)
log_prob = dist.log_prob(x)
self.assertEqual(log_prob.get_shape(), (6, ))
self.assertAllClose(log_prob.eval(), expected_log_prob)
prob = dist.prob(x)
self.assertEqual(prob.get_shape(), (6, ))
self.assertAllClose(prob.eval(), np.exp(expected_log_prob))
