def testGetLogitsAndProbProbabilityValidateArgs(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_prob(p=p, validate_args=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_prob(
p=p2, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(p=p2, validate_args=False)
prob.eval()
with self.assertRaisesOpError("p has components greater than 1"):
_, prob = distribution_util.get_logits_and_prob(
p=p3, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(p=p3, validate_args=False)
prob.eval()
def testGetLogitsAndProbProbabilityValidateArgs(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_prob(p=p,
validate_args=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_prob(
p=p2, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p2, validate_args=False)
prob.eval()
with self.assertRaisesOpError("p has components greater than 1"):
_, prob = distribution_util.get_logits_and_prob(
p=p3, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p3, validate_args=False)
prob.eval()
def testGetLogitsAndProbImproperArguments(self):
with self.test_session():
with self.assertRaises(ValueError):
distribution_util.get_logits_and_prob(logits=None, p=None)
with self.assertRaises(ValueError):
distribution_util.get_logits_and_prob(logits=[0.1], p=[0.1])
def testGetLogitsAndProbImproperArguments(self):
with self.test_session():
with self.assertRaises(ValueError):
distribution_util.get_logits_and_prob(logits=None, p=None)
with self.assertRaises(ValueError):
distribution_util.get_logits_and_prob(logits=[0.1], p=[0.1])
def __init__(self,
n,
logits=None,
p=None,
validate_args=False,
allow_nan_stats=True,
name="Binomial"):
"""Initialize a batch of Binomial distributions.
Args:
n: Non-negative floating point tensor with shape broadcastable to
`[N1,..., Nm]` with `m >= 0` and the same dtype as `p` or `logits`.
Defines this as a batch of `N1 x ... x Nm` different Binomial
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]` `m >= 0`, and
the same dtype as `n`. Each entry represents logits for the probability
of success for independent Binomial distributions.
p: Positive floating point tensor with shape broadcastable to
`[N1,..., Nm]` `m >= 0`, `p in [0, 1]`. Each entry represents the
probability of success for independent Binomial distributions.
validate_args: `Boolean`, default `False`. Whether to assert valid values
for parameters `n`, `p`, and `x` in `prob` and `log_prob`.
If `False` and inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to prefix Ops created by this distribution class.
Examples:
```python
# Define 1-batch of a binomial distribution.
dist = Binomial(n=2., p=.9)
# Define a 2-batch.
dist = Binomial(n=[4., 5], p=[.1, .3])
```
"""
self._logits, self._p = distribution_util.get_logits_and_prob(
name=name, logits=logits, p=p, validate_args=validate_args)
with ops.name_scope(name, values=[n]) as ns:
with ops.control_dependencies([
check_ops.assert_non_negative(
n, message="n has negative components."),
distribution_util.assert_integer_form(
n, message="n has non-integer components."),
] if validate_args else []):
self._n = array_ops.identity(n, name="n")
super(Binomial, self).__init__(
dtype=self._p.dtype,
parameters={"n": self._n, "p": self._p, "logits": self._logits},
is_continuous=False,
is_reparameterized=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
def testGetLogitsAndProbProbabilityMultidimensional(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_prob(
p=p, multidimensional=True, validate_args=True)
self.assertAllClose(special.logit(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbProbability(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_prob(
p=p, validate_args=True)
self.assertAllClose(special.logit(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbProbabilityMultidimensional(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_prob(
p=p, multidimensional=True, validate_args=True)
self.assertAllClose(np.log(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbProbability(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_prob(
p=p, validate_args=True)
self.assertAllClose(special.logit(p), new_logits.eval())
self.assertAllClose(p, new_p.eval())
def testGetLogitsAndProbLogitsMultidimensional(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_prob(
logits=logits, multidimensional=True, validate_args=True)
self.assertAllClose(new_p.eval(), p)
self.assertAllClose(new_logits.eval(), logits)
def testGetLogitsAndProbLogitsMultidimensional(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_prob(
logits=logits, multidimensional=True, validate_args=True)
self.assertAllClose(new_p.eval(), p)
self.assertAllClose(new_logits.eval(), logits)
def __init__(self,
logits=None,
p=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 positive 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 `p` should be passed in.
p: 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 `p` should be passed
in.
dtype: dtype for samples.
validate_args: `Boolean`, default `False`. Whether to validate that
`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
invalid, methods like `log_pmf` may return `NaN` values.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
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._p = distribution_util.get_logits_and_prob(
logits=logits, p=p, validate_args=validate_args)
with ops.name_scope("q"):
self._q = 1. - self._p
super(Bernoulli,
self).__init__(dtype=dtype,
is_continuous=False,
is_reparameterized=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._p, self._q, self._logits],
name=ns)
def __init__(self,
logits=None,
p=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 positive 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 `p` should be passed in.
p: 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 `p` should be passed
in.
dtype: dtype for samples.
validate_args: `Boolean`, default `False`. Whether to validate that
`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
invalid, methods like `log_pmf` may return `NaN` values.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
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._p = distribution_util.get_logits_and_prob(
logits=logits, p=p, validate_args=validate_args)
with ops.name_scope("q"):
self._q = 1. - self._p
super(Bernoulli, self).__init__(
dtype=dtype,
is_continuous=False,
is_reparameterized=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._p, self._q, self._logits],
name=ns)
def __init__(self,
logits=None,
p=None,
dtype=dtypes.int32,
validate_args=True,
allow_nan_stats=False,
name="Bernoulli"):
"""Construct Bernoulli distributions.
Args:
logits: An N-D `Tensor` representing the log-odds
of a positive event. Each entry in the `Tensor` parametrizes
an independent Bernoulli distribution where the probability of an event
is sigmoid(logits).
p: An N-D `Tensor` representing the probability of a positive
event. Each entry in the `Tensor` parameterizes an independent
Bernoulli distribution.
dtype: dtype for samples.
validate_args: Whether to assert that `0 <= p <= 1`. If not validate_args,
`log_pmf` may return nans.
allow_nan_stats: Boolean, default `False`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
self._logits, self._p = distribution_util.get_logits_and_prob(
name=name, logits=logits, p=p, validate_args=validate_args)
with ops.name_scope(name):
with ops.name_scope("q"):
self._q = 1. - self._p
super(Bernoulli,
self).__init__(dtype=dtype,
parameters={
"p": self._p,
"q": self._q,
"logits": self._logits
},
is_continuous=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
def __init__(self,
logits=None,
p=None,
dtype=dtypes.int32,
validate_args=True,
allow_nan_stats=False,
name="Bernoulli"):
"""Construct Bernoulli distributions.
Args:
logits: An N-D `Tensor` representing the log-odds
of a positive event. Each entry in the `Tensor` parametrizes
an independent Bernoulli distribution where the probability of an event
is sigmoid(logits).
p: An N-D `Tensor` representing the probability of a positive
event. Each entry in the `Tensor` parameterizes an independent
Bernoulli distribution.
dtype: dtype for samples.
validate_args: Whether to assert that `0 <= p <= 1`. If not validate_args,
`log_pmf` may return nans.
allow_nan_stats: Boolean, default `False`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
self._logits, self._p = distribution_util.get_logits_and_prob(
name=name, logits=logits, p=p, validate_args=validate_args)
with ops.name_scope(name):
with ops.name_scope("q"):
self._q = 1. - self._p
super(Bernoulli, self).__init__(
dtype=dtype,
parameters={"p": self._p, "q": self._q, "logits": self._logits},
is_continuous=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
def testGetLogitsAndProbProbabilityValidateArgsMultidimensional(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_prob(
p=p, multidimensional=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_prob(
p=p2, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p2, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError(
"(p has components greater than 1|p does not sum to 1)"):
_, prob = distribution_util.get_logits_and_prob(
p=p3, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p3, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError("p does not sum to 1"):
_, prob = distribution_util.get_logits_and_prob(
p=p4, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p4, multidimensional=True, validate_args=False)
prob.eval()
def testGetLogitsAndProbProbabilityValidateArgsMultidimensional(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_prob(
p=p, multidimensional=True)
prob.eval()
with self.assertRaisesOpError("Condition x >= 0"):
_, prob = distribution_util.get_logits_and_prob(
p=p2, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p2, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError(
"(p has components greater than 1|p does not sum to 1)"):
_, prob = distribution_util.get_logits_and_prob(
p=p3, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p3, multidimensional=True, validate_args=False)
prob.eval()
with self.assertRaisesOpError("p does not sum to 1"):
_, prob = distribution_util.get_logits_and_prob(
p=p4, multidimensional=True, validate_args=True)
prob.eval()
_, prob = distribution_util.get_logits_and_prob(
p=p4, multidimensional=True, validate_args=False)
prob.eval()
def __init__(self,
temperature,
logits=None,
p=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 `p` should be passed
in.
p: 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 `p` should be passed
in.
validate_args: `Boolean`, default `False`. Whether to validate that
`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
invalid, methods like `log_pmf` may return `NaN` values.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[logits, p, 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._p = distribution_util.get_logits_and_prob(
logits=logits, p=p, validate_args=validate_args)
with ops.name_scope("q"):
self._q = 1. - self._p
dist = logistic._Logistic(self._logits / self._temperature,
1./self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
def inverse_log_det_jacobian_fn(y):
return -math_ops.reduce_sum(math_ops.log(y) + math_ops.log(1-y),
reduction_indices=-1)
sigmoidbijector = bijector.Inline(
forward_fn=math_ops.sigmoid,
inverse_fn=(lambda y: math_ops.log(y) - math_ops.log(1-y)),
inverse_log_det_jacobian_fn=inverse_log_det_jacobian_fn,
name="sigmoid")
super(_RelaxedBernoulli, self).__init__(dist,
sigmoidbijector,
name=name)
def __init__(self,
n,
logits=None,
p=None,
validate_args=False,
allow_nan_stats=True,
name="Multinomial"):
"""Initialize a batch of Multinomial distributions.
Args:
n: 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 `n`. Defines this as a batch of `N1 x ... x Nm`
different `k` class Multinomial distributions.
p: Positive floating point tensor with shape broadcastable to
`[N1,..., Nm, k]` `m >= 0` and same dtype as `n`. Defines this as
a batch of `N1 x ... x Nm` different `k` class Multinomial
distributions. `p`'s components in the last portion of its shape should
sum up to 1.
validate_args: `Boolean`, default `False`. Whether to assert valid
values for parameters `n` and `p`, and `x` in `prob` and `log_prob`.
If `False`, correct behavior is not guaranteed.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to prefix Ops created by this distribution class.
Examples:
```python
# Define 1-batch of 2-class multinomial distribution,
# also known as a Binomial distribution.
dist = Multinomial(n=2., p=[.1, .9])
# Define a 2-batch of 3-class distributions.
dist = Multinomial(n=[4., 5], p=[[.1, .3, .6], [.4, .05, .55]])
```
"""
self._logits, self._p = distribution_util.get_logits_and_prob(
name=name, logits=logits, p=p, validate_args=validate_args,
multidimensional=True)
with ops.name_scope(name, values=[n, self._p]) as ns:
with ops.control_dependencies([
check_ops.assert_non_negative(
n, message="n has negative components."),
distribution_util.assert_integer_form(
n, message="n has non-integer components.")
] if validate_args else []):
self._n = array_ops.identity(n, name="convert_n")
self._mean_val = array_ops.expand_dims(n, -1) * self._p
self._broadcast_shape = math_ops.reduce_sum(
self._mean_val, reduction_indices=[-1], keep_dims=False)
super(Multinomial, self).__init__(
dtype=self._p.dtype,
parameters={"p": self._p,
"n": self._n,
"mean": self._mean,
"logits": self._logits,
"broadcast_shape": self._broadcast_shape},
is_continuous=False,
is_reparameterized=False,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
def __init__(
self,
logits=None,
p=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
`p` should be passed in.
p: 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 `p` should be passed in.
dtype: The type of the event samples (default: int32).
validate_args: Unused in this distribution.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution (optional).
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[logits]) as ns:
self._logits, self._p = distribution_util.get_logits_and_prob(
name=name, logits=logits, p=p, 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
logits_shape = array_ops.shape(self._logits, name="logits_shape")
if logits_shape_static[-1].value is not None:
self._num_classes = ops.convert_to_tensor(
logits_shape_static[-1].value,
dtype=dtypes.int32,
name="num_classes")
else:
self._num_classes = array_ops.gather(logits_shape,
self._batch_rank,
name="num_classes")
if logits_shape_static[:-1].is_fully_defined():
self._batch_shape_val = constant_op.constant(
logits_shape_static[:-1].as_list(),
dtype=dtypes.int32,
name="batch_shape")
else:
with ops.name_scope(name="batch_shape"):
self._batch_shape_val = logits_shape[:-1]
super(_OneHotCategorical, 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._num_classes],
name=ns)
def __init__(self,
temperature,
logits=None,
p=None,
dtype=dtypes.float32,
validate_args=False,
allow_nan_stats=True,
name="ExpRelaxedOneHotCategorical"):
"""Initialize ExpRelaxedOneHotCategorical using class log-probabilities.
Args:
temperature: An 0-D `Tensor`, representing the temperature
of a set of ExpRelaxedCategorical distributions. The temperature should
be positive.
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities
of a set of ExpRelaxedCategorical 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 `p` should be passed in.
p: An N-D `Tensor`, `N >= 1`, representing the probabilities
of a set of ExpRelaxedCategorical 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 `p` should be passed in.
dtype: The type of the event samples (default: int32).
validate_args: Unused in this distribution.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution (optional).
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[logits, p, 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._p = distribution_util.get_logits_and_prob(
name=name,
logits=logits,
p=p,
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
logits_shape = array_ops.shape(self._logits, name="logits_shape")
if logits_shape_static[-1].value is not None:
self._num_classes = ops.convert_to_tensor(
logits_shape_static[-1].value,
dtype=dtypes.int32,
name="num_classes")
else:
self._num_classes = array_ops.gather(logits_shape,
self._batch_rank,
name="num_classes")
if logits_shape_static[:-1].is_fully_defined():
self._batch_shape_val = constant_op.constant(
logits_shape_static[:-1].as_list(),
dtype=dtypes.int32,
name="batch_shape")
else:
with ops.name_scope(name="batch_shape"):
self._batch_shape_val = logits_shape[:-1]
super(_ExpRelaxedOneHotCategorical, self).__init__(
dtype=dtype,
is_continuous=True,
is_reparameterized=True,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._temperature, self._num_classes],
name=ns)
def __init__(self,
temperature,
logits=None,
p=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 `p` should be passed
in.
p: 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 `p` should be passed
in.
validate_args: `Boolean`, default `False`. Whether to validate that
`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
invalid, methods like `log_pmf` may return `NaN` values.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: A name for this distribution.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[logits, p, 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._p = distribution_util.get_logits_and_prob(
logits=logits, p=p, validate_args=validate_args)
with ops.name_scope("q"):
self._q = 1. - self._p
dist = logistic._Logistic(self._logits / self._temperature,
1. / self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
def inverse_log_det_jacobian_fn(y):
return -math_ops.reduce_sum(math_ops.log(y) + math_ops.log(1 - y),
reduction_indices=-1)
sigmoidbijector = bijector.Inline(
forward_fn=math_ops.sigmoid,
inverse_fn=(lambda y: math_ops.log(y) - math_ops.log(1 - y)),
inverse_log_det_jacobian_fn=inverse_log_det_jacobian_fn,
name="sigmoid")
super(_RelaxedBernoulli, self).__init__(dist,
sigmoidbijector,
name=name)
