def testProbabilityValidateArgs(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]
_, prob = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
self.evaluate(prob)
with self.assertRaisesOpError('Condition x >= 0'):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=True)
self.evaluate(prob)
_, prob = distribution_util.get_logits_and_probs(
probs=p2, validate_args=False)
self.evaluate(prob)
with self.assertRaisesOpError('probs has components greater than 1'):
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=True)
self.evaluate(prob)
_, prob = distribution_util.get_logits_and_probs(
probs=p3, validate_args=False)
self.evaluate(prob)
def testProbsMultidimShape(self):
with self.assertRaises(ValueError):
p = tf.ones([int(2**11+1)], dtype=tf.float16)
distribution_util.get_logits_and_probs(
probs=p, multidimensional=True, validate_args=True)
if tf.executing_eagerly(): return
with self.assertRaisesOpError(
'Number of classes exceeds `dtype` precision'):
p = np.ones([int(2**11+1)], dtype=np.float16)
p = tf1.placeholder_with_default(p, shape=None)
self.evaluate(distribution_util.get_logits_and_probs(
probs=p, multidimensional=True, validate_args=True))
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 = dict(locals())
with tf.compat.v1.name_scope(
name, values=[total_count, logits, probs]) as name:
dtype = dtype_util.common_dtype([total_count, logits, probs],
preferred_dtype=tf.float32)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name, dtype=dtype)
total_count = tf.convert_to_tensor(
value=total_count, name="total_count", dtype=dtype)
with tf.control_dependencies(
[tf.compat.v1.assert_positive(total_count)] if validate_args else []):
self._total_count = tf.identity(total_count, name="total_count")
super(NegativeBinomial, self).__init__(
dtype=self._probs.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._probs, self._logits],
name=name)
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 = dict(locals())
with tf.name_scope(name, values=[total_count, logits, probs]) as name:
dtype = dtype_util.common_dtype([total_count, logits, probs],
preferred_dtype=tf.float32)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name, dtype=dtype)
total_count = tf.convert_to_tensor(
total_count, name="total_count", dtype=dtype)
with tf.control_dependencies([tf.assert_positive(total_count)]
if validate_args else []):
self._total_count = tf.identity(total_count, name="total_count")
super(NegativeBinomial, self).__init__(
dtype=self._probs.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._probs, self._logits],
name=name)
def __init__(self,
logits=None,
probs=None,
dtype=tf.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 = dict(locals())
with tf.name_scope(name) as name:
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 = tensorshape_util.with_rank_at_least(
self._logits.shape, 1)
if tensorshape_util.rank(logits_shape_static) is not None:
self._batch_rank = tf.convert_to_tensor(
value=tensorshape_util.rank(logits_shape_static) - 1,
dtype=tf.int32,
name="batch_rank")
else:
with tf.name_scope("batch_rank"):
self._batch_rank = tf.rank(self._logits) - 1
with tf.name_scope("event_size"):
self._event_size = tf.shape(input=self._logits)[-1]
super(OneHotCategorical, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=name)
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 unnormalized log-probabilities
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 = dict(locals())
with tf.name_scope(name, values=[total_count, logits, probs]) as name:
dtype = dtype_util.common_dtype([total_count, logits, probs], tf.float32)
self._total_count = tf.convert_to_tensor(
total_count, name="total_count", dtype=dtype)
if validate_args:
self._total_count = (
distribution_util.embed_check_nonnegative_integer_form(
self._total_count))
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
multidimensional=True,
validate_args=validate_args,
name=name,
dtype=dtype)
self._mean_val = self._total_count[..., tf.newaxis] * self._probs
super(Multinomial, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._logits, self._probs],
name=name)
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 unnormalized log-probabilities
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 = dict(locals())
with tf.name_scope(name, values=[total_count, logits, probs]) as name:
dtype = dtype_util.common_dtype([total_count, logits, probs], tf.float32)
self._total_count = tf.convert_to_tensor(
total_count, name="total_count", dtype=dtype)
if validate_args:
self._total_count = (
distribution_util.embed_check_nonnegative_integer_form(
self._total_count))
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
multidimensional=True,
validate_args=validate_args,
name=name,
dtype=dtype)
self._mean_val = self._total_count[..., tf.newaxis] * self._probs
super(Multinomial, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._logits, self._probs],
name=name)
def testProbabilityMultidimensional(self):
p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True, validate_args=True)
self.assertAllClose(np.log(p), self.evaluate(new_logits))
self.assertAllClose(p, self.evaluate(new_p))
def testProbability(self):
p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
new_logits, new_p = distribution_util.get_logits_and_probs(
probs=p, validate_args=True)
self.assertAllClose(_logit(p), self.evaluate(new_logits))
self.assertAllClose(p, self.evaluate(new_p))
def testLogits(self):
p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32)
logits = _logit(p)
new_logits, new_p = distribution_util.get_logits_and_probs(
logits=logits, validate_args=True)
self.assertAllClose(p, self.evaluate(new_p), rtol=1e-5, atol=0.)
self.assertAllClose(logits, self.evaluate(new_logits), rtol=1e-5, atol=0.)
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 = dict(locals())
with tf.compat.v1.name_scope(
name, values=[logits, probs, temperature]) as name:
dtype = dtype_util.common_dtype([logits, probs, temperature], tf.float32)
self._temperature = tf.convert_to_tensor(
value=temperature, name="temperature", dtype=dtype)
if validate_args:
with tf.control_dependencies(
[tf.compat.v1.assert_positive(temperature)]):
self._temperature = tf.identity(self._temperature)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits, probs=probs, validate_args=validate_args, dtype=dtype)
super(RelaxedBernoulli, self).__init__(
distribution=logistic.Logistic(
self._logits / self._temperature,
1. / self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name + "/Logistic"),
bijector=sigmoid_bijector.Sigmoid(validate_args=validate_args),
validate_args=validate_args,
name=name)
self._parameters = parameters
def testLogitsMultidimensional(self):
p = np.array([0.2, 0.3, 0.5], dtype=np.float32)
logits = np.log(p)
new_logits, new_p = distribution_util.get_logits_and_probs(
logits=logits, multidimensional=True, validate_args=True)
self.assertAllClose(self.evaluate(new_p), p)
self.assertAllClose(self.evaluate(new_logits), logits)
def __init__(self,
total_count,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="Binomial"):
"""Initialize a batch of Binomial distributions.
Args:
total_count: Non-negative floating point tensor with shape broadcastable
to `[N1,..., Nm]` with `m >= 0` and the same dtype as `probs` 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 `total_count`. Each entry represents logits for the
probability of success for independent Binomial distributions. Only one
of `logits` or `probs` should be passed in.
probs: Positive floating point tensor with shape broadcastable to
`[N1,..., Nm]` `m >= 0`, `probs in [0, 1]`. Each entry represents the
probability of success for independent Binomial distributions. 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 = dict(locals())
with tf.compat.v1.name_scope(name, values=[total_count, logits,
probs]) as name:
dtype = dtype_util.common_dtype([total_count, logits, probs],
tf.float32)
self._total_count = self._maybe_assert_valid_total_count(
tf.convert_to_tensor(value=total_count,
name="total_count",
dtype=dtype), validate_args)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
validate_args=validate_args,
name=name,
dtype=dtype)
super(Binomial, self).__init__(
dtype=self._probs.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._total_count, self._logits, self._probs],
name=name)
def __init__(self,
logits=None,
probs=None,
dtype=tf.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 = dict(locals())
with tf.name_scope(name, values=[logits, probs]) as name:
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.shape.with_rank_at_least(1)
if logits_shape_static.ndims is not None:
self._batch_rank = tf.convert_to_tensor(
logits_shape_static.ndims - 1, dtype=tf.int32, name="batch_rank")
else:
with tf.name_scope(name="batch_rank"):
self._batch_rank = tf.rank(self._logits) - 1
with tf.name_scope(name="event_size"):
self._event_size = tf.shape(self._logits)[-1]
super(OneHotCategorical, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=name)
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 = dict(locals())
with tf.name_scope(name, values=[logits, probs, temperature]) as name:
dtype = dtype_util.common_dtype([logits, probs, temperature], tf.float32)
self._temperature = tf.convert_to_tensor(
temperature, name="temperature", dtype=dtype)
if validate_args:
with tf.control_dependencies([tf.assert_positive(temperature)]):
self._temperature = tf.identity(self._temperature)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits, probs=probs, validate_args=validate_args, dtype=dtype)
super(RelaxedBernoulli, self).__init__(
distribution=logistic.Logistic(
self._logits / self._temperature,
1. / self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name + "/Logistic"),
bijector=sigmoid_bijector.Sigmoid(validate_args=validate_args),
validate_args=validate_args,
name=name)
self._parameters = parameters
def __init__(self,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
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 = dict(locals())
with tf.name_scope(name) as name:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with tf.control_dependencies(
[assert_util.assert_positive(self._probs
)] if validate_args else []):
self._probs = tf.identity(self._probs, name='probs')
super(Geometric, self).__init__(
dtype=self._probs.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._probs, self._logits],
name=name)
def __init__(self,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
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 = dict(locals())
with tf.name_scope(name, values=[logits, probs]) as name:
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits, probs, validate_args=validate_args, name=name)
with tf.control_dependencies([tf.assert_positive(self._probs)]
if validate_args else []):
self._probs = tf.identity(self._probs, name="probs")
super(Geometric, self).__init__(
dtype=self._probs.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._probs, self._logits],
name=name)
def __init__(self,
logits=None,
probs=None,
dtype=tf.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 `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 p and logits are passed, or if neither are passed.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
self._logits, self._probs = util.get_logits_and_probs(
logits=logits,
probs=probs,
validate_args=validate_args,
name=name)
super(Bernoulli, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=name)
def testProbabilityValidateArgsMultidimensional(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)
_, prob = distribution_util.get_logits_and_probs(
probs=p, multidimensional=True)
self.evaluate(prob)
with self.assertRaisesOpError('Condition x >= 0'):
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=True)
self.evaluate(prob)
_, prob = distribution_util.get_logits_and_probs(
probs=p2, multidimensional=True, validate_args=False)
self.evaluate(prob)
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)
self.evaluate(prob)
_, prob = distribution_util.get_logits_and_probs(
probs=p3, multidimensional=True, validate_args=False)
self.evaluate(prob)
with self.assertRaisesOpError('probs does not sum to 1'):
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=True)
self.evaluate(prob)
_, prob = distribution_util.get_logits_and_probs(
probs=p4, multidimensional=True, validate_args=False)
self.evaluate(prob)
def __init__(
self,
temperature,
logits=None,
probs=None,
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 `probs` should be passed in.
probs: 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 `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 = dict(locals())
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([logits, probs, temperature], tf.float32)
self._logits, self._probs = distribution_util.get_logits_and_probs(
name=name,
logits=logits,
probs=probs,
validate_args=validate_args,
multidimensional=True,
dtype=dtype)
with tf.control_dependencies(
[assert_util.assert_positive(temperature)] if validate_args else []):
self._temperature = tf.convert_to_tensor(
temperature, name='temperature', dtype=dtype)
self._temperature_2d = tf.reshape(
self._temperature, [-1, 1], name='temperature_2d')
logits_shape_static = tensorshape_util.with_rank_at_least(
self._logits.shape, 1)
if tensorshape_util.rank(logits_shape_static) is not None:
self._batch_rank = tf.convert_to_tensor(
tensorshape_util.rank(logits_shape_static) - 1,
dtype=tf.int32,
name='batch_rank')
else:
with tf.name_scope('batch_rank'):
self._batch_rank = tf.rank(self._logits) - 1
with tf.name_scope('event_size'):
self._event_size = tf.shape(self._logits)[-1]
super(ExpRelaxedOneHotCategorical, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs, self._temperature],
name=name)
def testImproperArguments(self):
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])
