def testGetLogitsAndProbsImproperArguments(self):
with self.test_session():
with self.assertRaises(ValueError):
distribution_util.get_logits_and_probs(logits=None, probs=None)
with self.assertRaises(ValueError):
distribution_util.get_logits_and_probs(logits=[0.1], probs=[0.1])
def testGetLogitsAndProbsProbabilityValidateArgs(self):
p = [0.01, 0.2, 0.5, 0.7, .99]
# Component less than 0.
p2 = [-1, 0.2, 0.5, 0.3, .2]
# Component greater than 1.
p3 = [2, 0.2, 0.5, 0.3, .2]
with self.test_session():
_, prob = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=False)
prob.eval()
with self.assertRaisesOpError("probs has components greater than 1"):
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=False)
prob.eval()
def testGetLogitsAndProbsProbabilityValidateArgs(self):
p = [0.01, 0.2, 0.5, 0.7, .99]
# Component less than 0.
p2 = [-1, 0.2, 0.5, 0.3, .2]
# Component greater than 1.
p3 = [2, 0.2, 0.5, 0.3, .2]
with self.test_session():
_, prob = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=False)
prob.eval()
with self.assertRaisesOpError(
"probs has components greater than 1"):
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=False)
prob.eval()
def testGetLogitsAndProbsImproperArguments(self):
with self.test_session():
with self.assertRaises(ValueError):
distribution_util.get_logits_and_probs(logits=None, probs=None)
with self.assertRaises(ValueError):
distribution_util.get_logits_and_probs(logits=[0.1],
probs=[0.1])
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 `Boolean`, 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 `Boolean`, 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: `String` 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.reduce_sum(math_ops.log(y) + math_ops.log1p(-y), -1)
sigmoid_bijector = bijector.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 __init__(
self,
logits=None,
probs=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="OneHotCategorical"):
"""Initialize OneHotCategorical distributions using class log-probabilities.
Args:
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
set of Categorical distributions. The first `N - 1` dimensions index
into a batch of independent distributions and the last dimension
represents a vector of logits for each class. Only one of `logits` or
`probs` should be passed in.
probs: An N-D `Tensor`, `N >= 1`, representing the probabilities of a set
of Categorical distributions. The first `N - 1` dimensions index into a
batch of independent distributions and the last dimension represents a
vector of probabilities for each class. Only one of `logits` or `probs`
should be passed in.
dtype: The type of the event samples (default: int32).
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.
"""
parameters = locals()
with ops.name_scope(name, values=[logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
name=name, logits=logits, probs=probs, validate_args=validate_args,
multidimensional=True)
logits_shape_static = self._logits.get_shape().with_rank_at_least(1)
if logits_shape_static.ndims is not None:
self._batch_rank = ops.convert_to_tensor(
logits_shape_static.ndims - 1,
dtype=dtypes.int32,
name="batch_rank")
else:
with ops.name_scope(name="batch_rank"):
self._batch_rank = array_ops.rank(self._logits) - 1
with ops.name_scope(name="event_size"):
self._event_size = array_ops.shape(self._logits)[-1]
super(OneHotCategorical, self).__init__(
dtype=dtype,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits,
self._probs],
name=ns)
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 `Boolean`, 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 `Boolean`, 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: `String` 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 = bijector.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 testGetLogitsAndProbsProbability(self):
p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
self.assertAllClose(special.logit(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def __init__(self,
total_count,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="Multinomial"):
"""Initialize a batch of Multinomial distributions.
Args:
total_count: Non-negative floating point tensor with shape broadcastable
to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
`N1 x ... x Nm` different Multinomial distributions. Its components
should be equal to integer values.
logits: Floating point tensor representing the log-odds of a
positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
and the same dtype as `total_count`. Defines this as a batch of
`N1 x ... x Nm` different `k` class Multinomial distributions. Only one
of `logits` or `probs` should be passed in.
probs: Positive floating point tensor with shape broadcastable to
`[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
this as a batch of `N1 x ... x Nm` different `k` class Multinomial
distributions. `probs`'s components in the last portion of its shape
should sum to `1`. 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.
"""
parameters = locals()
with ops.name_scope(name, values=[total_count, logits, probs]) as ns:
self._total_count = self._maybe_assert_valid_total_count(
ops.convert_to_tensor(total_count, name="total_count"),
validate_args)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
multidimensional=True,
validate_args=validate_args,
name=name)
self._mean_val = self._total_count[..., array_ops.newaxis] * self._probs
super(Multinomial, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count,
self._logits,
self._probs],
name=ns)
def __init__(self,
total_count,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="Multinomial"):
"""Initialize a batch of Multinomial distributions.
Args:
total_count: Non-negative floating point tensor with shape broadcastable
to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
`N1 x ... x Nm` different Multinomial distributions. Its components
should be equal to integer values.
logits: Floating point tensor representing the log-odds of a
positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
and the same dtype as `total_count`. Defines this as a batch of
`N1 x ... x Nm` different `k` class Multinomial distributions. Only one
of `logits` or `probs` should be passed in.
probs: Positive floating point tensor with shape broadcastable to
`[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
this as a batch of `N1 x ... x Nm` different `k` class Multinomial
distributions. `probs`'s components in the last portion of its shape
should sum to `1`. Only one of `logits` or `probs` should be passed in.
validate_args: Python `Boolean`, 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 `Boolean`, 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: `String` name prefixed to Ops created by this class.
"""
parameters = locals()
with ops.name_scope(name, values=[total_count, logits, probs]) as ns:
self._total_count = self._maybe_assert_valid_total_count(
ops.convert_to_tensor(total_count, name="total_count"),
validate_args)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
multidimensional=True,
validate_args=validate_args,
name=name)
self._mean_val = self._total_count[..., None] * self._probs
super(Multinomial, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count,
self._logits,
self._probs],
name=ns)
def __init__(self,
total_count,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="NegativeBinomial"):
"""Construct NegativeBinomial distributions.
Args:
total_count: Non-negative floating-point `Tensor` with shape
broadcastable to `[B1,..., Bb]` with `b >= 0` and the same dtype as
`probs` or `logits`. Defines this as a batch of `N1 x ... x Nm`
different Negative Binomial distributions. In practice, this represents
the number of negative Bernoulli trials to stop at (the `total_count`
of failures), but this is still a valid distribution when
`total_count` is a non-integer.
logits: Floating-point `Tensor` with shape broadcastable to
`[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
Each entry represents logits for the probability of success for
independent Negative Binomial distributions and must be in the open
interval `(-inf, inf)`. Only one of `logits` or `probs` should be
specified.
probs: Positive floating-point `Tensor` with shape broadcastable to
`[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
Each entry represents the probability of success for independent
Negative Binomial distributions and must be in the open interval
`(0, 1)`. Only one of `logits` or `probs` should be specified.
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.
"""
parameters = locals()
with ops.name_scope(name, values=[total_count, logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with ops.control_dependencies(
[check_ops.assert_positive(total_count
)] if validate_args else []):
self._total_count = array_ops.identity(total_count)
super(NegativeBinomial, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._probs, self._logits],
name=ns)
def testGetLogitsAndProbsProbabilityMultidimensional(self):
p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True, validate_args=True)
self.assertAllClose(np.log(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbsProbabilityMultidimensional(self):
p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True, validate_args=True)
self.assertAllClose(np.log(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbsProbability(self):
p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
self.assertAllClose(special.logit(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def __init__(self,
total_count,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="NegativeBinomial"):
"""Construct NegativeBinomial distributions.
Args:
total_count: Non-negative floating-point `Tensor` with shape
broadcastable to `[B1,..., Bb]` with `b >= 0` and the same dtype as
`probs` or `logits`. Defines this as a batch of `N1 x ... x Nm`
different Negative Binomial distributions. In practice, this represents
the number of negative Bernoulli trials to stop at (the `total_count`
of failures), but this is still a valid distribution when
`total_count` is a non-integer.
logits: Floating-point `Tensor` with shape broadcastable to
`[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
Each entry represents logits for the probability of success for
independent Negative Binomial distributions and must be in the open
interval `(-inf, inf)`. Only one of `logits` or `probs` should be
specified.
probs: Positive floating-point `Tensor` with shape broadcastable to
`[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
Each entry represents the probability of success for independent
Negative Binomial distributions and must be in the open interval
`(0, 1)`. Only one of `logits` or `probs` should be specified.
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.
"""
parameters = locals()
with ops.name_scope(name, values=[total_count, logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with ops.control_dependencies(
[check_ops.assert_positive(total_count)] if validate_args else []):
self._total_count = array_ops.identity(total_count)
super(NegativeBinomial, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._probs, self._logits],
name=ns)
def testGetLogitsAndProbsLogitsMultidimensional(self):
p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
logits = np.log(p)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
logits=logits, multidimensional=True, validate_args=True)
self.assertAllClose(new_p.eval(), p)
self.assertAllClose(new_logits.eval(), logits)
def testGetLogitsAndProbsLogitsMultidimensional(self):
p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
logits = np.log(p)
with self.test_session():
new_logits, new_p = distribution_util.get_logits_and_probs(
logits=logits, multidimensional=True, validate_args=True)
self.assertAllClose(new_p.eval(), p)
self.assertAllClose(new_logits.eval(), logits)
def __init__(self,
logits=None,
probs=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="Bernoulli"):
"""Construct Bernoulli distributions.
Args:
logits: An N-D `Tensor` representing the log-odds of a `1` event. Each
entry in the `Tensor` parametrizes an independent Bernoulli 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 `1`
event. Each entry in the `Tensor` parameterizes an independent
Bernoulli distribution. Only one of `logits` or `probs` should be passed
in.
dtype: The type of the event samples. Default: `int32`.
validate_args: Python `Boolean`, 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 `Boolean`, 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: `String` name prefixed to Ops created by this class.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
validate_args=validate_args,
name=name)
super(Bernoulli, self).__init__(
dtype=dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=ns)
def __init__(self,
logits=None,
probs=None,
validate_args=True,
allow_nan_stats=False,
name="Geometric"):
"""Construct Geometric distributions.
Args:
logits: Floating-point `Tensor` with shape `[B1, ..., Bb]` where `b >= 0`
indicates the number of batch dimensions. Each entry represents logits
for the probability of success for independent Geometric distributions
and must be in the range `(-inf, inf]`. Only one of `logits` or `probs`
should be specified.
probs: Positive floating-point `Tensor` with shape `[B1, ..., Bb]`
where `b >= 0` indicates the number of batch dimensions. Each entry
represents the probability of success for independent Geometric
distributions and must be in the range `(0, 1]`. Only one of `logits`
or `probs` should be specified.
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.
"""
parameters = locals()
with ops.name_scope(name, values=[logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with ops.control_dependencies(
[check_ops.assert_positive(self._probs
)] if validate_args else []):
self._probs = array_ops.identity(self._probs, name="probs")
super(Geometric, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._probs, self._logits],
name=ns)
def __init__(self,
logits=None,
probs=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="Bernoulli"):
"""Construct Bernoulli distributions.
Args:
logits: An N-D `Tensor` representing the log-odds of a `1` event. Each
entry in the `Tensor` parametrizes an independent Bernoulli 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 `1`
event. Each entry in the `Tensor` parameterizes an independent
Bernoulli distribution. Only one of `logits` or `probs` should be passed
in.
dtype: The type of the event samples. Default: `int32`.
validate_args: Python `Boolean`, 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 `Boolean`, 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: `String` name prefixed to Ops created by this class.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
parameters = locals()
with ops.name_scope(name) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
validate_args=validate_args,
name=name)
super(Bernoulli, self).__init__(
dtype=dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=ns)
def __init__(self,
logits=None,
probs=None,
validate_args=True,
allow_nan_stats=False,
name="Geometric"):
"""Construct Geometric distributions.
Args:
logits: Floating-point `Tensor` with shape `[B1, ..., Bb]` where `b >= 0`
indicates the number of batch dimensions. Each entry represents logits
for the probability of success for independent Geometric distributions
and must be in the range `(-inf, inf]`. Only one of `logits` or `probs`
should be specified.
probs: Positive floating-point `Tensor` with shape `[B1, ..., Bb]`
where `b >= 0` indicates the number of batch dimensions. Each entry
represents the probability of success for independent Geometric
distributions and must be in the range `(0, 1]`. Only one of `logits`
or `probs` should be specified.
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.
"""
parameters = locals()
with ops.name_scope(name, values=[logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with ops.control_dependencies(
[check_ops.assert_positive(self._probs)] if validate_args else []):
self._probs = array_ops.identity(self._probs, name="probs")
super(Geometric, self).__init__(
dtype=self._probs.dtype,
is_continuous=False,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._probs, self._logits],
name=ns)
def testGetLogitsAndProbsProbabilityValidateArgsMultidimensional(self):
p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
# Component less than 0. Still sums to 1.
p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
# Component greater than 1. Does not sum to 1.
p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
# Does not sum to 1.
p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
with self.test_session():
_, prob = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError(
"(probs has components greater than 1|probs does not sum to 1)"
):
_, prob = distribution_util.get_logits_and_probs(
probs=p3, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p3, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError("probs does not sum to 1"):
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=False)
prob.eval()
def testGetLogitsAndProbsProbabilityValidateArgsMultidimensional(self):
p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
# Component less than 0. Still sums to 1.
p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
# Component greater than 1. Does not sum to 1.
p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
# Does not sum to 1.
p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)
with self.test_session():
_, prob = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError(
"(probs has components greater than 1|probs does not sum to 1)"):
_, prob = distribution_util.get_logits_and_probs(
probs=p3, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p3, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError("probs does not sum to 1"):
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=False)
prob.eval()
def __init__(self,
logits=None,
probs=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="OneHotCategorical"):
"""Initialize OneHotCategorical distributions using class log-probabilities.
Args:
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
set of Categorical distributions. The first `N - 1` dimensions index
into a batch of independent distributions and the last dimension
represents a vector of logits for each class. Only one of `logits` or
`probs` should be passed in.
probs: An N-D `Tensor`, `N >= 1`, representing the probabilities of a set
of Categorical distributions. The first `N - 1` dimensions index into a
batch of independent distributions and the last dimension represents a
vector of probabilities for each class. Only one of `logits` or `probs`
should be passed in.
dtype: The type of the event samples (default: int32).
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.
"""
parameters = locals()
with ops.name_scope(name, values=[logits, probs]) as ns:
self._logits, self._probs = distribution_util.get_logits_and_probs(
name=name,
logits=logits,
probs=probs,
validate_args=validate_args,
multidimensional=True)
logits_shape_static = self._logits.get_shape().with_rank_at_least(
1)
if logits_shape_static.ndims is not None:
self._batch_rank = ops.convert_to_tensor(
logits_shape_static.ndims - 1,
dtype=dtypes.int32,
name="batch_rank")
else:
with ops.name_scope(name="batch_rank"):
self._batch_rank = array_ops.rank(self._logits) - 1
with ops.name_scope(name="event_size"):
self._event_size = array_ops.shape(self._logits)[-1]
super(OneHotCategorical, self).__init__(
dtype=dtype,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=ns)
