def _default_event_space_bijector(self):
return chain_bijector.Chain([
shift_bijector.Shift(shift=self.loc,
validate_args=self.validate_args),
exp_bijector.Exp(validate_args=self.validate_args)
],
validate_args=self.validate_args)
def __init__(self,
loc,
scale,
validate_args=False,
allow_nan_stats=True,
name='LogNormal'):
"""Construct a log-normal distribution.
The LogNormal distribution models positive-valued random variables
whose logarithm is normally distributed with mean `loc` and
standard deviation `scale`. It is constructed as the exponential
transformation of a Normal distribution.
Args:
loc: Floating-point `Tensor`; the means of the underlying
Normal distribution(s).
scale: Floating-point `Tensor`; the stddevs of the underlying
Normal distribution(s).
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, 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 give Ops created by the initializer.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
super(LogNormal, self).__init__(
distribution=normal.Normal(loc=loc, scale=scale),
bijector=exp_bijector.Exp(),
validate_args=validate_args,
parameters=parameters,
name=name)
def __init__(self, nchan, dtype=tf.float32, validate_args=False, name=None):
parameters = dict(locals())
self._initialized = tf.Variable(False, trainable=False)
self._m = tf.Variable(tf.zeros(nchan, dtype))
self._s = TransformedVariable(tf.ones(nchan, dtype), exp.Exp())
self._bijector = invert.Invert(
chain.Chain([
scale.Scale(self._s),
shift.Shift(self._m),
]))
super(ActivationNormalization, self).__init__(
validate_args=validate_args,
forward_min_event_ndims=1,
parameters=parameters,
name=name or 'ActivationNormalization')
def __init__(
self,
temperature,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name='RelaxedOneHotCategorical'):
"""Initialize RelaxedOneHotCategorical using class log-probabilities.
Args:
temperature: An 0-D `Tensor`, representing the temperature
of a set of RelaxedOneHotCategorical distributions. The temperature
should be positive.
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities
of a set of RelaxedOneHotCategorical 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 RelaxedOneHotCategorical 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: Unused in this distribution.
allow_nan_stats: Python `bool`, 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 = dict(locals())
dist = ExpRelaxedOneHotCategorical(temperature,
logits=logits,
probs=probs,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats)
super(RelaxedOneHotCategorical, self).__init__(dist,
exp_bijector.Exp(),
validate_args=validate_args,
parameters=parameters,
name=name)
def _default_event_space_bijector(self):
return exp_bijector.Exp(validate_args=self.validate_args)
def quadrature_scheme_lognormal_quantiles(loc,
scale,
quadrature_size,
validate_args=False,
name=None):
"""Use LogNormal quantiles to form quadrature on positive-reals.
Args:
loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
the LogNormal prior.
scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
the LogNormal prior.
quadrature_size: Python `int` scalar representing the number of quadrature
points.
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.
name: Python `str` name prefixed to Ops created by this class.
Returns:
grid: (Batch of) length-`quadrature_size` vectors representing the
`log_rate` parameters of a `Poisson`.
probs: (Batch of) length-`quadrature_size` vectors representing the
weight associate with each `grid` value.
"""
with tf.name_scope(name, "quadrature_scheme_lognormal_quantiles",
[loc, scale]):
# Create a LogNormal distribution.
dist = transformed_distribution.TransformedDistribution(
distribution=normal.Normal(loc=loc, scale=scale),
bijector=exp_bijector.Exp(),
validate_args=validate_args)
batch_ndims = dist.batch_shape.ndims
if batch_ndims is None:
batch_ndims = tf.shape(dist.batch_shape_tensor())[0]
def _compute_quantiles():
"""Helper to build quantiles."""
# Omit {0, 1} since they might lead to Inf/NaN.
zero = tf.zeros([], dtype=dist.dtype)
edges = tf.linspace(zero, 1., quadrature_size + 3)[1:-1]
# Expand edges so its broadcast across batch dims.
edges = tf.reshape(
edges,
shape=tf.concat(
[[-1], tf.ones([batch_ndims], dtype=tf.int32)], axis=0))
quantiles = dist.quantile(edges)
# Cyclically permute left by one.
perm = tf.concat([tf.range(1, 1 + batch_ndims), [0]], axis=0)
quantiles = tf.transpose(quantiles, perm)
return quantiles
quantiles = _compute_quantiles()
# Compute grid as quantile midpoints.
grid = (quantiles[..., :-1] + quantiles[..., 1:]) / 2.
# Set shape hints.
grid.set_shape(dist.batch_shape.concatenate([quadrature_size]))
# By construction probs is constant, i.e., `1 / quadrature_size`. This is
# important, because non-constant probs leads to non-reparameterizable
# samples.
probs = tf.fill(dims=[quadrature_size],
value=1. / tf.cast(quadrature_size, dist.dtype))
return grid, probs
